Remember building your first email campaign? Painstakingly crafting emails, hoping the recipient wouldn’t see the telltale “Hello (name),” and adding contacts to your email workflow manually? That’s all a thing of the past. Today, automation is a game-changer and life-saver for email marketers — helping you save time, money, and stress. Automation doesn’t mean impersonal responses and a cookie-cutter approach to your email workflow. Brands of all sizes are using AI and automation to streamline tedious processes, personalise emails, and form better relationships with their customers. Here’s how you can learn more about email workflows and start automating today. What is an email workflow in marketing? Your email workflow is the series of actions that guide the communication and engagement with customers through email campaigns. Sometimes these are automated, sometimes not. The goal of an email workflow is to nurture leads, build relationships, and drive desired actions – such as making a purchase or subscribing to a service. Another way to define an email marketing workflow? “The fine art of managing all the different kinds of work that go into creating a beautiful email from inception to completion.” By this definition, an email workflow can involve content, design, development, and often automation needs. Depending on your business, your email workflow may be complex or simple, but the goal remains the same: to create a streamlined process for creating and sharing emails with your audience. Examples of email workflows Some email workflows that you may already be familiar with include: Welcome emails The welcome email is the first thing your customers see when they agree to receive communication from your brand. It sets the tone and expectations for your relationship, so it’s important to get right. This email is simple to create — especially with a template — and is frequently automated after creation. Welcome emails are a great way to start automating the send portion of your email workflow if you’re new to the automation process. Get articles selected just for you, in your inbox Sign up now Lead nurture emails Lead nurture emails — which introduce new subscribers to your brand and show what you have to offer — are also frequently automated. These automated workflows send emails at regular intervals – ideally one day resulting in a conversion. Automating this email workflow can save you a lot of time in the long run. Why is automation so important in this conversation about workflows, again? According to recent research, marketing automation sparks a 14.5% increase in sales productivity and a 12.2% reduction in marketing overhead. Sales or limited time offers Sales offer emails let your subscribers know that you’ve got a deal or special offer for them. These emails require more time, effort, and review rounds throughout the content, design, and development process – and are usually time-sensitive. With a solid internal process and clever automation strategies, you can line up a string of emails to successfully send right when your subscribers will be most interested in receiving them. Post-purchase emails Post-purchase emails that often ask for a review or some other communication from the customer are another common case of email workflow automation. Use these emails to encourage your customers to share their thoughts – whether they’ve recently acquired your latest product, experienced your services, or had another positive interaction with your brand. These emails often include a warm and inviting message, a subtle encouragement to take action, or perhaps even a small extra incentive to enhance the deal and prompt customers to leave reviews on platforms such as Google, Yelp, or your own website. Customer outreach Feel like you’re losing touch? Haven’t heard from a customer in a while? A customer outreach workflow can send an automated email when an account or subscriber is inactive for a while. It’s a great way to reach out a human hand, and just say hi again. You can leave your audience with education or inspiration, or try something more creative. Again, while the content and design portions of the email process can be adjusted as needed depending on the team’s size and tools, there’s one key portion that remains the same: automation. Why should you change up your email workflow process? Here are a few reasons why you should be consistently and regularly updating your email workflow processes. Minimise repetitive tasks Automating your email workflow process can help minimise repetitive tasks. The result? More time for your team to take on big-picture work, instead of getting bogged down with tedious, repetitive tasks. Save time Think of all you can get done when you automate your email workflows. For instance, time spent manually adding contacts into your customer relationship management (CRM) tool can be better spent actually writing a personalised email to a subscriber, for example. The goal of automation is to save precious time for the things that really matter, like innovation and exploration. When you reduce manual tasks, or the time spent on manual tasks at least, you can spend more time getting closer to your customers. How AI can improve your email workflow Finally, the moment you’ve all been waiting for: AI. Whether we’re discussing predictive or generative AI, it’s top of mind for marketing professionals everywhere right now. It’s got us all wondering: how can AI help you improve your email workflow? Personalisation: AI algorithms can analyse vast amounts of customer data to personalise email content based on individual preferences, behaviours, and demographics. This leads to more relevant and engaging emails — and takes personalisation way beyond just an exercise in first name and last name. With AI, you can now send highly-targeted emails to each individual, showing a selection of products designed just for them. Predictive analytics: AI can predict customer behaviour and preferences by analysing historical data. This enables you to not just send emails at optimal times, but also predict which products or content a customer might be interested in, and tailor every email accordingly. Automated content generation: AI technologies, like Natural Language Processing (NLP), can assist in generating personalised content for emails. This includes dynamically creating subject lines, email body text, and even product recommendations based on everything from the customer’s past purchases to the weather in their city. Dynamic email content: AI enables the creation of dynamic content in emails that adapts based on user preferences or behaviour. This ensures that each recipient sees content that is most relevant to their interests. We’re already using AI to automate, segment, utilise behavioural triggers to send email campaigns. Generative AI is bringing even more ambitious new horizons into sight. The end result? Always stay competitive by testing new tools like AI to see how they can help your workflows and processes. This blog post was authored in partnership with Litmus.

When you think of a copilot, the first thing that comes to mind is probably an airplane. Until now, a copilot has been that person sitting in the second chair in the cockpit, helping the captain on your flight. But sometime last year, the term “copilot” started to trend in a big way in the artificial intelligence (AI) space. Take all of the generative AI technology you’ve come to know and love in apps like ChatGPT, Bard, and Einstein. Now, place that right in the flow of your work —or in that second chair, if you will. At its most basic level, an AI copilot is an AI assistant that can help you accomplish routine tasks faster than before. While the introduction of the modern copilot is linked to the launch of GitHub Copilot in 2021, these AI assistants go back even further. Since the 1990s, AI copilots —which, back then, were basic chatbots like ELIZA and Jabberwacky or virtual assistants like IKEA’s Anna —have been popping up in everything from your email platform to shopping, banking, and medical applications. Here’s the difference between now and then. Imagine you’re booking a business dinner with a client based in a different city. Before the world of AI copilots, you’d first scan the client’s customer relationship management (CRM) record to check for any dietary preferences. Next, you’d open the Resy app and spend far too long looking for a suitable restaurant with availability. Then, on to Expedia to make your travel and lodging reservations, and, finally, your email app to send a charmingly personalised confirmation to your customer. At minimum, you’d be looking at four different apps and at least a half hour of drudgery. Now imagine, instead, that you simply use one app: your trusty AI copilot. Instead of taking four different actions over the course of minutes or hours, you type, “Book dinner with Ted next Thursday.” All the steps above still take place, but the research happens in the background, and mostly without your intervention. Beyond the obvious time savings and the inherent sci-fi novelty, it’s hard to fully articulate the value of this transformation through traditional metrics. These assistants will do the work of dozens of apps to help us build reports faster, craft customer service replies with relevant answers, draft sales emails, send flowers to our bosses, and more. But first, how do they work? How does an AI copilot work? At the heart of AI copilots are powerful building blocks called copilot actions. A copilot action can encompass almost any single task or a collection of tasks for a specific job. These may include: Updating a CRM record. Generating descriptions for new products using your existing CRM data. Composing messages to customers. Handling a range of use cases. Summarising transcripts for a live service agent. Highlighting the most relevant information from meeting notes. These tasks can be “invoked,” or arranged and executed, in any order and are done so autonomously by the AI copilot. This ability to understand requests, reason a plan of action, and execute the needed tasks is what makes these systems and experiences unique. The AI copilot can handle a lot of instructions and learns from that. So, the more actions, the more capable the copilot. Stacked together, actions allow your copilot to perform a dizzying array of business tasks. For example, a copilot can help a service agent quickly resolve an issue in which a customer was overcharged for an order. Or it can help someone in sales trying to close a deal. Want more? Let’s put our copilot into action. Take the example of setting up dinner with your client, Ted. If you use Einstein Copilot, it would know Ted’s initial context, like their name and CRM session history, but it would require a bit more information from you, like the date and time. It could then execute on that and respond with any other questions it may have: It might ask you to clarify which Ted you want to meet with (if you have multiple contacts named Ted) and what type of cuisine Ted prefers. What’s nice about Einstein and other copilots at this level is that it feels like you’re talking with a coworker — but you’re actually chatting with your robust data, which the copilot is serving up in a new conversational way. The AI copilot decides which actions to trigger and then generates runtime dialogues, paraphrasing the actions’ output-data in everyday human language. So, it feels like you’re having a fairly sophisticated conversation with your AI assistant. And then dinner gets set up with little effort on your part. “We’re just telling the system, ‘Hey, do this task,’” said Carlos Lozano, director of product management at LIKE.TG AI. “But behind the scenes, the copilot is orchestrating a complex workflow of business processes and data to deliver a result that would have previously required the user to access multiple actions.” What different types of AI copilots exist? Although the concept of a copilot is fairly new, this technology has existed for a while. Have you ever chatted with a customer service representative only to realise they weren’t a person, but a bot? That’s a type of copilot. It helped you with basic customer service questions, but often couldn’t really get to the important details of your issue. Likely frustrated, you then turned to an actual human for help. Chatbots got more sophisticated with the launch of ChatGPT, Dall-E, Google’s Gemini, and Microsoft’s Bing Chat. Those generative AI platforms — let’s call them Chatbot 2.0 — can help craft emails, write code, generate images, and analyse data. With AI copilots, the interactivity becomes even more conversational, with your own AI assistant working behind the scenes to help improve everything you do. In addition to LIKE.TG, a number of other companies have introduced copilot products to the market, including Microsoft and GitHub, and even Apple is working on one. There are more niche industry-focused AI copilot companies like real-estate digital marketing company LuxuryPresence, healthcare-focused Nabla, and finance-focused ArkiFi. The copilot goes to the next level when it’s connected to data and metadata. What’s metadata? It’s the tagging system that defines your data. For instance, “first name” is the metadata that would define “Ted” in our example. This metadata makes it easier to find, use, and merge your proprietary data. So, this is what separates a workable copilot from a truly exceptional one — one that is super relevant for your everyday work. Here’s the main takeaway: When you are researching adding an AI copilot to your business, determine whether it will simply use external source information, like ChatGPT, or whether you’ll be able to safely connect it to your structured and unstructured data sources. Why you should use an AI Copilot By now, you’re probably familiar with at least one or two large language models (LLMs) like OpenAI’s GPT-4 or Google’s Gemini. These models power chatbots like ChatGPT that are fun to play with and are great for certain tasks. Some, however, only contain data through early 2022, so their responses can be limited. And those models only have access to public information about your business — they don’t have access to your trusted CRM information and data. This means they can’t help you craft relevant customer service answers or supply the juiciest sales opportunities. Nor can they act on your behalf to, say, reply to an email or book a flight. But an AI copilot can do all of the above. Okay, back to your dinner with Ted. You had a successful trip. Now, maybe you want to thank him with a gift basket from his favourite bakery. Because your copilot already has the requisite actions to look up Ted’s CRM contact and account to find his favourite bakery, and to charge goods on your behalf, all you’d need to do is type, “Send Ted his favourite muffins.” Of course, this is only a rudimentary example comprising a couple of copilot actions. Imagine what you could do with an AI copilot capable of orchestrating hundreds, or even thousands, of building blocks in virtually infinite combinations. The gains in efficiency apply to an excitingly wide range of job types. For example, a retail marketer can write product descriptions in numerous languages in just minutes, a healthcare clinician can review X-rays and lab results for multiple patients and help doctors make diagnoses, and a finance worker can use a copilot to analyse reams of data to propose various investment outcomes. The use cases and scenarios go on and on. If it seems like everything related to AI is happening at a breakneck pace — especially when it comes to how you work —and it’s making your head spin, you’re not alone. But you don’t have to be … alone, that is. You’ll have your trusted AI copilot. “With an AI copilot, you can quickly and easily become more efficient and productive, no matter the industry you work in,” Lozano said. “Having a conversational, generative AI-based assistant will truly let you offload those routine tasks while allowing you to interact and engage with data like never before. And that is the beauty of it.” Carlos Lozano, director of product management at LIKE.TG AI, contributed to this article.

Often, marketers are the early adopters of new tech. Constantly searching for new and innovative ways to surprise and delight our customers, we find ourselves leading the way when exploring new tools and techniques. A great example is the recent explosion of activity around generative artificial intelligence. Let’s face it – the possibilities are incredibly tempting. But here’s the question, with the rapid rate of change, and with new players emerging onto the scene, how can you make sure you’re using marketing technology and AI safely and ethically? How LIKE.TG ensures its marketing remains ethical Personalisation and optimisation have been part of the Marketing Cloud toolkit for some time. And its powerful predictive artificial intelligence tools have recently been joined by impressive generative AI. What do they have in common? They rely on robust, accurate customer data. Ethical data The survey response from our Trends in Ethical Marketing report had a key message that was loud and clear – the responsible use of data is an important factor in consumers’ purchasing decisions. More than 60% of customers said that they are comfortable sharing sensitive data with businesses, but only if they are reassured that it’s being used in a transparent and beneficial manner. So how can you make sure you’re collecting and using customer data in an ethical way? Here are just some of the methods we use at LIKE.TG: Understand the data you need Nearly three-quarters of customers think companies collect more information than they need, and nearly two thirds worry that companies aren’t transparent about how they use customer data. Digital privacy laws around the world agree that businesses should minimise the amount of customer data they collect. Before you even begin to gather and store customer data, ask yourself what information you need to achieve your objectives, and then collect only that data. Bottom line – if you don’t need it, don’t collect it. Collect – and respect – preferences International data protection and privacy laws also make it clear that the customer should have ultimate control over how their data is used. Your marketing tools should allow you to record your customers’ preferences about how their data is used, apply those preferences to your marketing activities, and – crucially – allow customers to change their minds. Treat customer data like it’s your own In the day-to-day business of marketing, we often work with partners. But not all partners are created equal, so it’s important to be vigilant about how you share your customer data, and with whom. Will they treat the data with the same care that you have? Will they share it with third parties outside of your control? Make sure you review the contracts with each of your partners to ensure that there are clear obligations with respect to the care, custody, and control of any data sent to them. Ethical personalisation An increasing number of customers expect every offer to be personalised, and it’s important that as marketers we’re able to meet that expectation. The flip side, of course, is that we have to demonstrate real value for our customers in exchange for that data. At LIKE.TG, we make sure that we are transparent with our customers regarding how their data is used, and what they’ll get in return for providing it. Ethical artificial intelligence While generative AI has been taking the world by storm, we at LIKE.TG have been developing – and using – AI for a decade. LIKE.TG marketing teams use predictive and generative AI in many different ways – from automating campaign optimisation, to producing unique and personalised messages. We even use AI internally. It helps by summarising long Slack threads, or automating our reporting and data analysis processes. The full list of ways that we use AI is long and varied, but the one thing that every application of AI has in common? They’re all built on the policy of ethical use that we set out for ourselves. Never share customer data with external language models. The Einstein Trust Layer, natively built into the whole LIKE.TG platform, allows teams to benefit from generative AI without compromising their customer data. Always ensure human review of AI-generated content. This ‘human in the loop’ model ensures we never compromise the trust of our customers Link every innovation, product, or campaign to our core values, especially trust. The benefits of ethical marketing – and how you can do it too As well as improving customer trust, there are also economic benefits to wider ethical practice, too. Eighty-six percent of customers are more loyal to ethical companies, and 69% actually spend more with a company who they see as ethical. Marketing Cloud – recently updated with bold new AI capabilities – is the perfect tool for ensuring your marketing remains ethical. The app is built on the Einstein Trust Layer, meaning your customers’ data is kept safe, and seamlessly integrates with Data Cloud for real-time data, giving you the ability to provide relevant, trustworthy personalisation. It’s a delicate balancing act – aiming to get the best value out of any tool, while also providing a trusted, impactful experience for our customers. Ensuring that ethical thinking is at the heart of all our marketing efforts means that we can stay ahead of the curve without risking time-consuming backtracking to fix mistakes, and it also provides a framework for innovation that is rooted in trust.

The big trend You can’t scan the headlines lately without seeing buzz around generative artificial intelligence (AI). The product innovations are only beginning. But even with the best technology out there, you’ll still be faced with a key question: How can you implement AI at scale in a way that maximises the return on your investment? Let’s look at one model company you can learn from. Breaking down silos Schneider Electric, a global energy management and industrial automation company, has formalised an AI program under a new Chief AI Officer and scaled it to every corner of the company. Its vision, “data and AI first,” is already paying dividends. For example, the company has saved millions by using AI to more accurately forecast and manage inventory demand. The backstory you might need Enterprise AI use has already doubled since 2017, but few companies are seeingsignificant return on their upfront costs, and a majority have failed to scale AI beyond the pilot stage. Analysts say the reasons include a lack of skills, complex programming models, upfront costs, and a lack of business alignment. What you can do now Take cues from Schneider Electric: Formalise AI efforts under one senior executive Understand the immense impact of AI – this is not like any technology that’s come before Hire dedicated AI and data experts Consider creating an AI centre of excellence to work with business unit leaders on AI projects AI success requires AI at scale Schneider had already been using AI in a decentralised fashion for years when, in 2021, it began its AI at Scale initiative and appointed its first Chief AI Officer, Philippe Rambach, to formalise its AI strategy. Madhu Hosadurga, global vice president of enterprise AI at Schneider, said it’s important to have such a top-down approach. “If you want to drive AI at scale and get value from it, top management has to motivate it as a corporate-wide objective,” said Hosadurga. “Without the C-suite, everyone tries different things at a departmental and individual level.” He said a departmental approach typically involves highly technical people that understand the technology but “lack the influence and power to make change management happen.” Bring business and tech leaders together to scale AI The company has implemented a global hub and spoke AI operating model. Each business function “spoke” (marketing, sales, service, etc.) has an AI product owner and change agent who works with the tech competency centre “hub” to find new uses for AI, deliver the technology, and ensure employee adoption. The hub is comprised mainly of technologists who help the business leaders identify AI opportunities and put them into use. For example, supply chain leaders wanted to use AI for, among other things, balancing inventory based on projected demand, and its ability to deliver based on those projections. With 200 factories and tens of thousands of suppliers, it’s impossible for humans to ensure optimal inventory levels, Hosadurga said. AI analytics and predictive modeling helped it reduce inventory levels to avoid a glut while balancing its ability to efficiently deliver products like transformers, switches, and prefabricated substations. He said that improvement alone has resulted in about $15 million in savings, measured by how much excess inventory it reduced, and capital allocated to other projects. “We targeted $5 million to $10 million in value, so that was a pleasant surprise,” he said, adding that it plans to use new AI capabilities to pare an additional five percent of inventory. Hire AI and data experts for better decision-making Schneider’s AI at Scale program included adding more than 200 AI and data experts. These two are inexorably linked, as AI is the linchpin to extracting more value from data and therefore making better, faster decisions. For many business leaders, it’s still a challenge. LIKE.TG research shows a deep disconnect between business leaders and their data. Half of business leaders lack understanding of data because it’s complex or not accessible, and the vast majority aren’t using it to make better decisions. According to Yuval Atsmon, senior partner at McKinsey, this is a missed opportunity. “For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today,” he said on a recent podcast. Get articles selected just for you, in your inbox Sign up now It’s extremely hard to synthesise huge amounts of data, let alone detect patterns, make recommendations and predictions. This is the promise of AI-driven systems. Hosadurga offered this advice for companies looking to formalise their own AI program: Bring AI to the mainstream. Don’t view it as just another tool in your tech toolbox but as a new business capability that can change the way you operate, sell to customers, and enhance your employee experience. Organise with IT and business partnering from the get-go. Often, AI is relegated to the IT team. When that happens, IT will ask the business for a use case, but the business usually doesn’t know what to do with AI. At Schneider, people come together from both sides, with a mix of about 70% business and 30% tech. Don’t wait until your data is perfect, in terms of quality and being all in one place, before embarking on a companywide AI initiative. “Many organizations believe they can’t use AI without perfect data,,” Hosadurga said, “but it’s more of a mindset issue where each business use case has to find the data, which is there in one form or another or in different places.” AI is not like other technology Business people dominate most AI projects at Schneider, Hosadurga said, which is one thing that makes it different from any other technology project. “Every use case — and we have use cases in almost every function —has people from both the AI Hub and business,” Hosadurga said. It’s entirely possible to deliver AI at scale, but unlike some other major business technologies, AI requires an entrepreneur’s mindset. “If you look at a typical IT culture, things are well defined, you know what you get from them and they can be programmed with a long-term plan,” he said. “But AI tools move so fast that it requires a very agile, quick-win, fail-fast culture. We operate more like a standup where we find an idea, incubate it quickly, and move on to the next phase.” Schneider Electric, which invests tens of millions of dollars in AI each year, plans to apply more AI and automation to its finance, sales, marketing, IT, and human resources functions over the next year. The company has launched an AI knowledge library, featuring blogs, ebooks, podcasts, training, courses, and other resources, prepared by its AI experts, so others can learn from its experience. “It’s as applicable as Excel in business,” Hosadurga said “It’s everywhere.”

You can’t sell something to someone you don’t know exists yet, and they can’t buy anything from a company they’ve never heard of. Demand generation marketing (or “demand gen,” for short) means finding, learning about, and winning over potential customers. It’s about helping that person realise that your product helps solve their problems (when that happens, it’s called generating demand).It’s not quite as easy as it sounds, so we’re here to make it simpler. There are plenty of obstacles between you and good demand generation marketing, but fortunately also plenty of ways to conquer them. In this piece, we’re going to walk you through some of these challenges, the keys to overcoming them, the objectives and processes that fuel successful demand gen, and why good demand gen is worth the effort. Then we’ll show you what successful demand generation marketing looks like in the real world. What is demand generation marketing? Demand generation marketing builds brand awareness, educates potential customers, and ultimately motivates them to interact with a brand. There are many ways to approach or think of demand generation marketing, but at its core it has five basic steps: Brand awareness and education: Make potential customers aware of your brand and product, and how they’re unique. Lead generation: Give those newly aware customers a reason to be curious about or interested in the brand, becoming “leads” in the process. Lead nurturing: Entice those leads to become more involved with the brand and more likely to purchase from it. You can do this through free content or gated assets the customer can get in exchange for sharing their information. Conversion: When a lead is properly nurtured, they start buying from your brand and become a customer. Tracking and data analysis: Learn from every conversion (and from failed conversion opportunities) to refine your demand generation approach and work toward more consistent results and higher conversion rates. What are the marketing challenges of demand generation? Demand generation faces a more crowded marketplace than ever before. Your competitors are all doing it too, so you need to find a way to stand out. The main challenge is keeping your customers’ and potential customers’ interest and attention focused on you — even as they’re inundated with lots of content. Those customers also expect more and more from brands. Having so many options affords them the freedom to be choosy, and they tend to pick brands that can speak to them on a personal level. Our State of the Connected Customer report found that 80% of customers say the experience a brand provides is just as important as its products or services. Additionally, our State of Marketing report found that 73% of customers expect companies to understand their unique needs and expectations. So how do you guarantee those quality experiences to thousands or millions of people at a unique personal level? Your data is the key to overcoming both those challenges, but it’s also a challenge unto itself. With so many different streams active – web, email, social media, etc. – how do you sort, organise, and process all that incoming data in a way that’s easily accessible for your teams? How do you make sure everyone has access to the same data, and how do you make sure that data is correct? Having a complete picture of your customer from all their various data streams is extremely valuable – to be frank, at this point it’s virtually a necessity for good demand generation. But creating that picture means being able to process a tremendous volume of incoming data very quickly, and being able to turn all that raw data into something easily digestible and useful for your teams.Businesses also need to figure out where AI fits into their demand generation marketing approach. Your competitors are using it, so you need to figure out how to use it better than they do. Get articles selected just for you, in your inbox Sign up now What are the key components of demand generation marketing? Who, what, why, how, where, and when? The six most common questions in the English language are also the key components of good demand gen. Whom should you be targeting? What do they need, and why do they need it? How and where do you reach them (and how do you tell if it’s working)? When is it time to check back in, change up your approach, incorporate new technology, or jump on a trend? The best way to answer these is with good data, audience segmentation, and targeting. Strong first-party data is best – that’s your potential customers (or “leads” in traditional demand gen parlance) themselves giving you the answers, but good market research and a good customer data platform can help you find them even in the absence of first party data. Once you know who they are, you should segment them according to both their needs and your strategy. All your leads probably want something you offer, but they don’t all want the same thing (or respond to it the same way). Finding and sorting these prospects is usually called “lead generation.”Once you’ve got your leads segmented out by who they are and what they want, the next step is targeting “why.” That’s also when you start answering your own “how.” No matter what they want or why they want it, the best way you’re going to help them realise you can give it to them is with quality content. Your data, segmenting, and targeting should give you a pretty good idea of what they’re receptive to, so your responsibility is to make sure you’re making that content as compelling as possible. AI is a big player here, as it’s the key to helping you deliver timely, personalised content at scale.“Where?” is about making sure that content reaches them in the right place. A good multi-channel marketing approach makes sure you reach your leads on the channels where they’re most responsive. From there, you move from lead generation to “lead nurturing.” This can mean different things to different businesses, but mostly it’s about taking a lead from being someone who’s aware of your brand to someone who’s actively buying from it. A good lead nurturing strategy makes all the difference in the world.Finally, you get to your “when?” You should be making data-driven marketing decisions based on how your leads are responding to your demand generation marketing efforts, so you know exactly when to scale up or down, or if it’s time to try another tactic. You also want to make sure your sales and marketing teams are using the same platform, so when your leads are ready to become customers, the transition is smooth and efficient. What are the key objectives of demand generation marketing? The first thing you need your demand generation to do is increase awareness and visibility. Nobody can buy from your brand if they don’t know it exists, and even potential leads who may vaguely know who you are may not realise exactly what you offer.Another major objective is generating and nurturing leads from that awareness. It’s not enough to simply make those leads aware of your brand, you want to motivate them to engage with it, and ultimately to convert. Once you’ve got a consistent recipe to turn awareness into leads into conversion, your demand generation becomes one of your most powerful drivers of revenue. The right plan can help you convert unaware prospects into repeat customers that keep your business growing. What is the demand generation process? So how do you actually do demand generation marketing? Let’s dig a little deeper into those key areas from above: Step 1: Create brand awareness with your content marketing, thought leadership, and social media content. Use SEO best practices to help more potential leads find your content. Step 2: Generate leads through lead magnets, data analysis, landing pages, forms, webinars, and other events. Offer high-quality free content or gated assets in exchange for customers volunteering their information. Step 3: Nurture leads using targeted email marketing, personalised content, and scalable marketing automation tools. Use unified data and cross-functional platforms to help marketing and sales work in harmony. Step 4: Improve conversions and sales by optimising your landing pages and user experience and by writing killer CTAs. You can use your data to implement intelligent lead scoring and qualification processes that make sure you only spend your resources on leads you can actually convert. Step 5: Track your results and improve your approach by identifying which metrics are most useful in evaluating your demand gen. And have a reliable process for adjusting based on what those results are telling you. AI is a tool that can make every one of those steps easier and more effective. AI-driven personalisation is the key to steps 1-3. AI lead scoring and data analytics drastically reduce the workload required to execute steps 4 and 5 effectively. What are the benefits of demand generation marketing? Following these basic guidelines and using the right automation tools makes demand generation marketing a powerful force that benefits your business up and down the funnel. At the top of the funnel, good demand gen gives you a wider audience that’s more familiar with your brand, which quickly translates to greater market share. Good lead generation helps fill up the middle of your funnel, while good lead nurturing makes sure they make it to the bottom. The personalised content and data-driven approaches you took on the way there help spark ongoing customer engagement and loyalty well beyond that first purchase.All this adds up to more customers, more engagement, more conversions, and ultimately, a healthier business enjoying consistent, replicable growth.

Companies constantly seek ways to optimise their sales and marketing strategies in the competitive business landscape. One crucial aspect of this optimisation process is lead qualification, the process of determining the potential and suitability of prospective customers. This article delves into the concept of lead qualification, its significance, benefits, and challenges. We will explore various frameworks used in lead qualification, including BANT, CHAMP, and GPCTBA/C, and provide insights into selecting the most appropriate framework for your business. Additionally, we will examine how LIKE.TG, a leading customer relationship management (CRM) platform, supports lead qualification and enhances sales efficiency. What is lead qualification? Within the business environment space, companies constantly optimise their sales and marketing strategies to stay competitive. A critical aspect of this optimisation process is lead qualification, the process of determining the potential and suitability of prospective customers. Lead qualification enables businesses to focus on the most promising leads, increasing efficiency and effectiveness. Simply put, lead qualification is the process of identifying leads that are most likely to convert into customers. By assessing various factors such as the lead’s budget, authority, need, and timeline, businesses can prioritise their sales and marketing efforts, allocate resources effectively, and prioritise leads that ultimately drive revenue growth. Lead qualification is a gatekeeper, filtering out unqualified leads and allowing sales teams to focus their time and energy on nurturing the most promising opportunities. This targeted approach improves sales productivity and enhances customer satisfaction by ensuring that resources are directed towards leads who are genuinely interested in the company’s products or services. Effective lead qualification is a cornerstone of successful sales and marketing strategies. By investing time and resources in qualifying leads, businesses can increase their chances of converting prospects into customers, driving business growth, and achieving long-term success in a competitive marketplace. Why is lead qualification important? In today’s competitive business landscape, efficiently converting leads into customers is essential for sustained growth and profitability. Lead qualification plays a pivotal role in achieving this objective by enabling businesses to identify and prioritise the most promising leads, those who exhibit a genuine interest in their offerings and possess the characteristics of valuable customers. Businesses can make strategic decisions about where to allocate their sales resources by qualifying leads. This allows sales teams to focus their efforts on nurturing and cultivating the most promising leads, increasing the likelihood of successful sales outcomes. By avoiding the trap of pursuing unqualified or disinterested sales leads alone, businesses can enhance their sales efficiency and effectiveness, maximising their chances of success. Furthermore, lead qualification contributes to improved customer satisfaction. By ensuring that only qualified leads are passed on to the sales team, businesses can deliver personalised and targeted sales experiences to their customers. This approach increases the receptivity of leads to sales pitches and facilitates meaningful conversations with sales representatives. As a result, customer satisfaction soars, and businesses build a positive brand reputation. Moreover, lead qualification empowers businesses to optimise their marketing and sales resources. Businesses can tailor their marketing campaigns and sales strategies by gaining a deep understanding of the specific needs, challenges, and preferences of qualified leads. This targeted approach enhances the relevance and effectiveness of marketing efforts, leading to higher conversion rates and a substantial return on investment. Finally, lead qualification serves as a critical metric for measuring the efficacy of marketing and sales efforts. By tracking and analysing the conversion rates of qualified leads, businesses can obtain valuable insights into the effectiveness of their lead generation and sales strategies. This data-driven approach enables businesses to continuously refine and improve their sales processes, driving long-term growth and success. Lead qualification is a cornerstone of successful sales and marketing strategies. By identifying, prioritising, and nurturing the most promising leads, businesses can maximise their chances of converting prospects into customers, enhancing customer satisfaction, optimising resource allocation, and achieving sustainable growth. Benefits of lead qualification process Lead qualification offers numerous advantages that can significantly enhance your sales and marketing strategies. These benefits are critical for businesses looking to optimise their sales efforts, reduce costs, and drive growth. Here are several key benefits of lead qualification: 1. Focus on Sales Efforts and Save Time: Lead qualification helps sales teams focus their time and resources on the most promising leads. By identifying qualified leads, sales representatives can prioritise their efforts and target the most likely customers to convert. This focused approach saves sales reps valuable time and ensures that sales efforts are directed towards the most relevant opportunities. 2. Increased Sales Efficiency and Effectiveness: Lead qualification improves sales efficiency by allowing businesses to concentrate on leads genuinely interested in their products or services. This targeted approach reduces the time spent on unqualified leads, ensuring sales representatives engage in meaningful conversations with potential customers. As a result, sales teams can close more deals and drive higher revenue. 3. Improved Customer Satisfaction: Lead qualification enhances customer satisfaction by ensuring that potential customers are matched with the right products or services. By understanding the needs and requirements of each lead, businesses can provide personalised and relevant solutions, leading to increased customer satisfaction and loyalty. 4. Optimised Marketing and Sales Resources: Lead qualification optimises marketing and sales resources by ensuring marketing efforts focus on generating high-quality leads. This alignment between marketing teams and sales teams improves the overall efficiency of the lead generation process and ensures that marketing investments are utilised effectively. 5. Accurate Measurement of Marketing and Sales Performance: Lead qualification serves as a valuable metric for measuring the effectiveness of marketing and sales efforts. By tracking the number of qualified leads generated and converted into customers, businesses can assess the success of their lead-generation strategies and make data-driven decisions to improve their overall performance. In summary, lead qualification is crucial in helping businesses focus their sales efforts, save time and money, improve sales efficiency and effectiveness, and ultimately close more deals. Businesses can optimise their sales and marketing strategies and drive sustainable growth by implementing a robust lead qualification process. Challenges of lead qualification Despite its importance, lead qualification is not without its challenges. One of the primary challenges is the time-consuming nature of the process. Evaluating each lead thoroughly requires careful consideration of multiple factors, including budget, authority, need, and timeline. This can be particularly demanding for businesses that generate a high volume of leads. Another challenge lies in the complexity of accurately assessing lead quality. Determining whether a lead is a good fit for a product or service requires subjective judgement and can be influenced by various factors, such as the lead’s communication style, level of engagement, and the evaluator’s own biases. This complexity can lead to inconsistencies in the qualification process and potentially result in promising leads being overlooked or disqualified prematurely. Obtaining accurate and up-to-date information from leads can also pose a challenge. Inaccurate or outdated information can lead to flawed assessments and misaligned sales efforts. This challenge is particularly relevant in industries where customer needs and preferences evolve rapidly. Businesses must employ effective data collection and verification strategies to ensure that they have access to reliable information for lead qualification. Another challenge is balancing the need for thorough qualification with the need to move leads through the sales funnel quickly. While it is essential to assess leads carefully, excessive qualification can slow down the sales process and potentially frustrate leads who are genuinely interested in the product or service. Finding the right balance between thoroughness and efficiency is crucial to maintaining a healthy sales pipeline and optimising conversion rates. Finally, the lead qualification process is not immune to bias and subjectivity. Evaluators’ personal biases and preferences can influence their assessment of leads, leading to inconsistent or unfair evaluations. To mitigate this challenge, businesses should establish clear and objective qualification decision criteria beforehand, provide training to ensure consistent application of these criteria and implement regular audits to monitor and address any potential biases in the process. How to Qualify Leads using BANT (Budget, Authority, Need, and Timing) One effective method for qualifying leads is by using the BANT (Budget, Authority, Need, and Timing) framework. Let’s explore each element of BANT and its significance in lead qualification: Budget: Assessing a lead’s budget is essential to determine if they have the financial means to purchase your product or service. By understanding their budget constraints, you can align your offerings accordingly and avoid wasting time on leads who cannot afford your solutions. Authority: Identifying the decision-maker or the person with the authority to make a purchase is crucial. Knowing who has the power to say yes can streamline your sales process and ensure that you’re directing your efforts towards the right individuals. Need: Determining the lead’s specific needs and pain points is vital. Understanding their challenges and objectives allows you to tailor your pitch and demonstrate how your product or service can solve their problems. Timing: Assessing the lead’s timeline for the decision-making process or a decision is equally important. Knowing their urgency can help you prioritise your efforts and allocate resources effectively. Leads with shorter timelines may require more immediate attention and follow-up. By evaluating these four key factors, you can effectively qualify leads and focus your sales efforts on those who are most likely to become customers. This targeted approach increases your chances of success, improves sales efficiency, and optimises your marketing and sales resources. How to Qualify Leads using CHAMP (Challenges, Authority, Money, and Prioritisation) Qualifying leads is crucial in optimising your sales efforts and ensuring efficient resource allocation. The CHAMP framework offers a practical approach to assessing the potential of a lead and determining if it aligns with your product or service. Challenges: Begin by comprehending the potential customer’s specific challenges and obstacles. Your product or service should offer effective solutions to their pain points. Ask questions to identify their most pressing concerns and evaluate if your offerings can provide genuine value. Authority: Identify the key decision-maker within the organisation. This individual holds the power to make purchasing decisions. Engaging with the wrong person can waste valuable time and resources. Ensure you communicate with the individual with the authority to commit to a purchase. Money: Financial readiness is a critical factor in lead qualification. Determine the potential customer’s budget and assess if it aligns with the cost of your product or service. It’s essential to ensure they have the financial means to purchase. Prioritisation: Understand the urgency and timeline of the potential customer’s needs. Are they ready to decide soon, or is their purchase on a longer horizon? Evaluate if your delivery capabilities match their requirements and if they’re prepared to move forward with a purchase. By thoroughly evaluating these four key factors, you can effectively qualify leads and focus your sales efforts on those with the highest potential to convert into customers. This systematic approach enhances sales efficiency, increases customer satisfaction, and optimises your marketing and sales resources. Embrace the CHAMP framework to make informed decisions for sales and marketing teams and drive successful business outcomes. How to Qualify Leads using GPCTBA/C (Goals, Plans, Challenges, Timeline, Budget, Authority, and Negative Consequences/Positive Implications) The GPCTBA/C framework is a comprehensive approach to lead qualification that evaluates multiple dimensions of a lead’s situation. By considering the lead’s goals, plans, challenges, timeline, budget, authority, negative consequences of inaction, and positive implications of taking action, you can better understand their needs and assess their fit for your product or service. Goals: Begin by understanding the lead’s goals and objectives. What are they trying to achieve? How does your product or service align with their aspirations? Leads whose goals align with your offerings are more likely to be genuinely interested and motivated to purchase. Plans: Assess the lead’s plans for achieving their goals. Do they have a clear strategy in place? Are they actively seeking solutions to address their challenges? Leads with well-defined plans and a sense of urgency are more likely to be ready to make a purchase decision. Challenges: Identify the challenges and obstacles that the lead is facing. What are the pain points that your product or service can address? Leads experiencing significant challenges and seeing your offering as a potential solution are more likely to be receptive to your sales pitch. Timeline: Determine the lead’s timeline for making a decision. Are they looking for an immediate solution, or are they still in the early stages of their research? Leads who have a clear timeline, decision process and a sense of urgency are more likely to be ready to engage in a sales conversation. Budget: Understand the lead’s budget and decision-making authority. Do they have the financial resources to purchase your product or service? Are they the primary decision-maker or do they need to consult with others? Leads with the budget and authority to make a purchase decision are more likely to be your marketing-qualified leads. Authority: Assess the lead’s level of authority within their organisation. Are they the primary decision-makers, or do they need to obtain approval from others? Leads with the authority to make a purchase decision without extensive approval are more likely to be qualified leads. Negative Consequences/Positive Implications: Consider the potential negative consequences of inaction and the positive implications of taking action. How would the lead’s situation be impacted if they do not address their challenges? How would your product or service positively impact their business or personal life? Leads who recognise the potential negative consequences of inaction and the positive implications of taking action are more likely to be motivated to make a purchase decision. By systematically evaluating these seven critical factors, you can effectively qualify leads, prioritise your sales efforts, and focus on those who are most likely to convert into customers. This approach improves your sales efficiency and effectiveness, enhances customer satisfaction, and optimises your marketing and sales resources. How to Choose the Best Framework for Qualifying Leads Choosing the most suitable framework for qualifying leads is crucial to the success of your sales team. Several factors need to be considered when making this decision, including the size of your sales team, the complexity of your sales process, the resources you have available, and the specific needs of your business and your target market. If you have a small sales team and a relatively straightforward sales process, you may be able to get by with a simple framework such as BANT (Budget, Authority, Need, and Timing). However, if you have a larger sales and marketing team or a more complex sales process, you may need a more comprehensive framework such as CHAMP (Challenges, Authority, Money, and Prioritisation) or GPCTBA/C (Goals, Plans, Challenges, Timeline, Budget, Authority, Negative Consequences of Inaction, and Positive Implications of Taking Action). Ultimately, the best way to choose the right framework for qualifying leads is to test different frameworks and see what works best for your team. You can do this by tracking the conversion rates of leads who have been qualified using different frameworks. Over time, you can determine which framework is most effective at generating revenue for your business. Here are some additional tips for choosing the best framework for qualifying leads: Start with your ideal customer profile What are the characteristics of your ideal customer? What are their needs and pain points? Once you know who you’re looking for, you can develop a framework to help you identify those customers. Consider your sales process. How do you typically sell your product or service? What are the key steps in your sales process? Your lead qualification framework should be aligned with your sales process so that you can identify leads who are ready to buy. Get input from your sales team. Your sales team is the one who will be using the lead qualification framework, so it’s important to get their input. What are their needs and concerns? What kind of information do they need to qualify leads? Test different frameworks. There is no one-size-fits-all lead qualification framework. The best way to find the right framework for your business is to test different frameworks and see what works best. By following these tips, you can choose the best framework for qualifying leads and improve your sales efficiency and effectiveness. LIKE.TG and Lead qualification LIKE.TG is a powerful customer relationship management (CRM) platform that can be used to improve the lead qualification process by providing a number of features that can help businesses assess the potential of leads. These features include lead scoring, lead qualification criteria, and lead automation. Lead scoring is a process of assigning a numerical value to each lead based on their likelihood of becoming a customer. This lead score is calculated using a variety of factors, such as the lead’s industry, company size, job title, and previous interactions with the company. Lead scoring can be used to identify the most promising leads and prioritise them for sales follow-up. Lead qualification criteria are a set of rules that are used to determine whether a lead is qualified for sales outreach. These criteria can be based on various factors, such as the lead’s budget, authority to make a purchase, and need for the company’s product or service. Lead qualification criteria can help businesses focus their sales efforts on the most likely to convert leads. Lead automation is a process of using software to automate the lead qualification process. This can include tasks such as capturing lead data, scoring leads, and routing leads to the appropriate sales representative. Lead automation can help businesses save time and resources by automating the repetitive tasks associated with the entire lead qualification process. By using LIKE.TG, businesses can improve the lead qualification process and focus their sales efforts on the most promising leads. This can a sales-qualified lead, to increased sales, improved customer satisfaction, and reduced costs.

Demand forecasting, a major aspect of business strategy, is pivotal in anticipating future demand for products and services. By leveraging demand forecasting techniques, businesses gain the ability to make informed decisions regarding production, inventory management, and marketing strategies. This blog looks into the intricacies of demand forecasting, exploring its significance, challenges, and various methodologies employed to predict market trends accurately. We’ll also provide practical examples and industry insights to illustrate how businesses can harness the power of demand forecasting to gain a competitive edge within the evolving marketplace. Demand forecasting overview In the ever-changing business landscape, accurately predicting future demand for products and services is paramount to success. This is where demand forecasting comes into play. Demand forecasting is the art and science of predicting the future demand for a particular product or service. By leveraging historical data, market trends, and various analytical techniques, businesses can gain valuable insights into consumer behaviour and market dynamics, enabling them to make better choices regarding production, inventory management, and marketing strategies. The significance of demand forecasting cannot be overstated. It empowers businesses to enhance their operations, minimise production costs, and ensure customer satisfaction by meeting demand effectively. Accurate demand forecasting also assists businesses in identifying potential market opportunities, plan for seasonal fluctuations, and respond swiftly to consumer preferences changes. Numerous demand forecasting methods and techniques are available, each with strengths and limitations. Some of the commonly used short-term demand forecasting methods include: – Quantitative methods: These methods rely on historical data and statistical analysis to predict future demand. Examples include time series analysis, regression analysis, and econometric models. – Qualitative methods: These methods incorporate subjective judgments and market research to estimate future demand. Techniques such as surveys, expert opinions, and focus groups fall under this category. – Causal methods: These methods establish a cause-and-effect relationship between demand and various factors such as economic indicators, consumer behaviour, and market trends. The choice of demand forecasting method depends on several factors, including the nature of the product or service, the availability of historical data, and the level of accuracy required. It is often beneficial to employ a combination of methods to enhance the reliability of forecasts. Demand forecasting is a continuous process that requires regular monitoring and updating. As new data becomes available, forecasts should be revised to reflect changing market conditions. By staying attuned to market dynamics and leveraging robust demand forecasting techniques, businesses can gain a competitive edge and navigate the uncertainties of the marketplace with greater confidence. Demand Forecasting explained Demand forecasting is an imperative component within the business strategy domain, enabling organisations to peer into the future and anticipate the ebb and flow of market demand. This intricate process of predicting consumer behaviour holds the key to optimising production, managing inventory precisely, and crafting marketing strategies that hit the bullseye. At the heart of demand forecasting lies the meticulous analysis of historical data, discerning patterns and trends illuminating the demand trajectory. Techniques such as moving averages and exponential smoothing transform this data into invaluable insights, guiding businesses toward the correct conclusions. Another avenue for demand forecasting involves venturing into the field of market research, where surveys, focus groups, and customer conversations unveil consumers’ hidden desires and preferences. This qualitative approach paints a vivid picture of market dynamics, allowing businesses to tailor their strategies accordingly. When historical data falls short or market shifts disrupt the landscape, businesses turn to the expertise of industry veterans – sales representatives, market analysts, and specialists with a wealth of knowledge. Their informed judgement acts as a compass, navigating the uncertainties and charting a course toward accurate demand forecasts. Econometric models, wielding the power of statistics and mathematical finesse, establish intricate connections between demand and economic factors like GDP, inflation, and consumer spending. These sophisticated tools, however, demand specialised knowledge and careful thought regarding complex economic relationships. Machine learning algorithms and artificial intelligence emerge as game-changers at the cutting edge of demand forecasting. Their ability to process vast data volumes and discern intricate patterns unlocks a new level of precision. These methods capture the nuances of non-linear relationships and integrate a diverse array of variables, yielding forecasts that resonate with market realities. The choice of demand forecasting method hinges on a delicate balance of factors: the nature of the product or service, the availability of historical data, the degree of market uncertainty, and the resources at hand. Often, a prudent approach involves blending multiple methods, and harnessing their collective strengths to enhance forecast accuracy. Regular updates to demand forecasts are paramount in a world of constant flux. Market conditions, economic trends, and consumer whims can shift lightning, demanding businesses to stay nimble and responsive. By continuously monitoring actual demand and incorporating fresh data, organisations can refine their forecasts, ensuring their decisions remain grounded in reality. Demand forecasting, an art as much as a science, lies at the heart of business success. It empowers organisations to hone their operations, minimise costs, and adapt seamlessly to the ever-changing market landscape. Embracing this practice enables businesses to navigate the complexities of consumer demand, securing their competitive edge and propelling them toward sustained growth. Benefits of demand forecasting Businesses that accurately forecast demand gain a competitive edge by optimising inventory levels, improving customer satisfaction, planning for future production and staffing needs, identifying and mitigating risks in the supply chain, and supporting data-driven decision-making and strategic planning. Optimising inventory levels: Accurate demand forecasting and inventory planning help businesses maintain optimal inventory levels, avoiding stockouts that can lead to lost sales and customer dissatisfaction, as well as excess inventory that ties up capital and incurs storage costs. By aligning inventory levels with anticipated demand, businesses can minimise costs and maximise profitability. Improving customer satisfaction: To attain customer satisfaction, you must first meet customer demand. When businesses accurately forecast demand, they can ensure adequate inventory to promptly fulfil customer orders. This reduces the likelihood of stockouts, backorders, and delayed deliveries, all of which can lead to customer frustration and churn. By consistently meeting customer demand, businesses build customer trust and loyalty. Planning for future production and staffing needs: Demand forecasting enables businesses to plan for future production and staffing needs. By anticipating demand trends, businesses can adjust their production schedules and workforce levels accordingly. This helps them avoid production bottlenecks, capacity constraints, and labour shortages, ensuring smooth operations and efficient resource allocation. Identifying and mitigating risks in the supply chain: Demand forecasting helps businesses identify potential risks in the supply chain, such as disruptions due to natural disasters, geopolitical events, or supplier issues. By anticipating these risks, businesses can develop contingency plans and mitigation strategies to minimise their impact on operations and customer service. Supporting data-driven decision-making and strategic planning: Accurate demand forecasting provides valuable insights that inform the organisation’s data-driven decision-making and strategic planning. It helps businesses allocate resources effectively, set realistic sales targets, optimise marketing campaigns, and make informed product development and expansion investments. By leveraging demand forecasting, businesses can make proactive decisions that align with market dynamics and customer needs, driving long-term growth and success. Challenges of demand forecasting Businesses face numerous challenges when forecasting demand, which can impact the accuracy and effectiveness of their predictions. One significant challenge lies in data accuracy and availability. Businesses rely on various data sources, such as historical sales data, market research, and economic indicators, to forecast demand. However, the accuracy and reliability of these data sources can vary, leading to potential errors in the forecasting process. Some businesses may also need more historical data, especially for new products or services, making it difficult to establish reliable demand patterns. Another challenge in demand forecasting is the influence of external factors beyond a business’s control. Economic conditions, changes in consumer preferences, technological advancements, and global events can significantly impact internal demand forecasting. For instance, a sudden economic downturn can lead to decreased demand for non-essential products, while a new technological innovation may disrupt existing markets and create unexpected demand. Businesses must continuously monitor and analyse these external factors to adjust their demand forecasts accordingly. Long lead times, particularly in industries with complex supply chains, pose another challenge in demand forecasting. Certain products may require extended production or shipping times, making it difficult to predict demand over longer horizons accurately. This challenge is compounded by the risk of stockouts or overstocking, which can negatively affect customer satisfaction and profitability. Product seasonality also presents forecasting difficulties. Demand for specific products or services may fluctuate significantly based on seasonal factors, such as weather, holidays, or fashion trends. Accurately predicting these seasonal variations is vital to avoid stockouts during peak demand periods and excess inventory during off-seasons. Lastly, rapidly changing consumer preferences can disrupt even the most carefully crafted demand forecasts. Factors such as evolving tastes, social media, consumer trends, and consumer behaviour shifts can quickly alter market dynamics. Businesses must stay agile and responsive to these changes by continuously gathering and analysing consumer insights to adapt their demand forecasts. Addressing these challenges requires businesses to adopt robust demand forecasting methodologies, leverage advanced analytics tools, and maintain a data-driven approach. By overcoming these obstacles, businesses can improve the accuracy of their demand forecasts, increase their operations, and gain a competitive advantage in the market. Why Is Demand Forecasting Important for Businesses? Demand forecasting is a crucial business process that enables companies to anticipate future demand for their products or services. By accurately predicting demand, businesses can maximise their operations and make informed decisions that drive growth and profitability. Demand forecasting is a necessity when it comes to several key areas: Supply Chain Management: Accurate demand forecasting allows businesses to maintain optimal inventory levels, reducing the risk of stockouts and overstocking. This optimisation of inventory levels directly impacts cash flow, customer satisfaction, and overall supply chain efficiency. Production Planning: With precise demand forecasts, businesses can effectively plan their production schedules to meet anticipated demand. This ensures that they have the right resources, materials, and workforce in place to fulfil customer orders efficiently. Proper planning minimises production disruptions, reduces costs, and enhances operational efficiency. Marketing and Sales Strategies: Demand forecasting provides valuable insights into market trends and customer preferences. This information empowers businesses to develop targeted marketing and sales strategies that resonate with their customers. By aligning marketing efforts with forecasted demand, businesses can make the most of their marketing budgets and maximise their return on investment. Financial Planning and Budgeting: Accurate demand forecasting enables businesses to make informed financial decisions. By anticipating future demand and revenue, businesses can create realistic budgets, allocate resources effectively, and plan for future investments. This financial planning ensures the long-term sustainability and growth of the business. Risk Management: Demand forecasting helps businesses identify potential risks and challenges in the market. By anticipating fluctuations in demand, businesses can develop contingency plans to mitigate these risks and minimise their impact on operations. This proactive approach enhances business resilience and allows companies to respond swiftly to changing market conditions. Overall, demand forecasting is an essential tool that empowers businesses to make data-driven decisions, increase their operations, and gain a competitive edge in the market. By accurately predicting future demand, businesses can achieve improved customer satisfaction, increased profitability, and sustainable growth. What Factors Impact Demand Forecasting? This section discusses the various factors that can impact demand forecasting. These factors include economic conditions, seasonality, weather, competitors’ actions, and changes in consumer preferences. Economic conditions play a significant role in demand forecasting. A strong economy typically increases demand for goods and services, while a weak economy can lead to decreased demand. Factors such as GDP growth, inflation, interest rates, and consumer confidence affect economic conditions and demand forecasting. Seasonality is another essential factor to consider in demand forecasting. Many products and services experience predictable fluctuations in demand throughout the year. For example, demand for ice cream is typically higher in the summer months, while demand for winter coats is higher in the winter months. Businesses need to take seasonality into account when forecasting demand to ensure that they have adequate inventory to meet customer needs. Weather can also impact demand forecasting. For example, a cold and snowy winter can increase demand for heating oil and snow removal services, while a hot and dry summer can increase demand for air conditioners and swimming pools. Businesses located in areas with volatile weather patterns need to adjust their demand forecasts quickly in response to changing weather conditions. Competitors’ actions can also affect demand forecasting. For example, if a competitor launches a new product or service similar to yours, it can decrease demand for your product or service. Businesses need to keep a close eye on their competitors’ activities and be prepared to adjust their demand forecasts accordingly. Finally, changes in consumer preferences can also impact demand forecasting. For example, becoming more health-conscious can lead to decreased passive demand forecasting for sugary snacks and increased demand for healthy foods. Businesses need to be aware of changing consumer preferences and be able to adjust their demand forecasts accordingly. By considering all of these factors, businesses can improve the accuracy of their demand forecasts and make better-informed decisions about production, inventory, and marketing. 7 Demand Forecasting Types When it comes to demand forecasting, there exists a diverse array of methodologies, each tailored to specific business scenarios and product characteristics. Let’s take a deeper look into seven prominent demand forecasting types, exploring their distinctive features, strengths, and limitations: 1. Historical Data Analysis: This method leverages historical sales data to project future demand. It’s straightforward to implement, making it a popular choice for businesses with ample historical information. However, its accuracy is limited by the assumption that past trends will continue into the future, which may only sometimes hold true. 2. Expert Opinion: This method involves soliciting insights from industry experts, sales personnel, or customers to estimate future demand for a product. It’s beneficial when historical data is scarce, or the product is new to the market. However, the accuracy of this method hinges on the expertise and objectivity of the individuals providing the estimates. 3. Market Research: Conducting market research surveys, focus groups, or analysing consumer behaviour can provide valuable insights into future demand. This method is well-suited for new product launches or understanding evolving customer preferences. However, it can be time-consuming and may not accurately capture purchasing behaviour accurately. 4. Econometric Models: These models incorporate economic indicators, such as GDP growth, inflation, and consumer spending, to forecast demand. They are advantageous when there’s a strong correlation between economic factors and product demand. However, econometric models require robust data and expertise in economic analysis, which may only be readily available to some businesses. 5. Time Series Analysis: This method analyses historical demand data to identify patterns and trends. It’s effective for products with relatively stable demand patterns. However, it needs help to capture sudden shifts in demand caused by unforeseen events or market disruptions. 6. Causal Models: Establish cause-and-effect relationships between various factors and demand. They are helpful when there’s a clear understanding of demand drivers, such as advertising, promotions, or pricing. However, building causal models can be complex and requires substantial data and expertise. 7. Machine Learning Algorithms: These algorithms leverage historical data and advanced statistical techniques to predict demand. They excel in handling large datasets and identifying intricate patterns. However, machine learning models require specialised expertise and can be challenging to interpret, making it difficult to understand the underlying reasons behind the forecasts. Each of these demand forecasting methods has its merits and drawbacks. The choice of method depends on factors such as data availability, product characteristics, market dynamics, and the level of accuracy required. Businesses should carefully evaluate these factors and select the most appropriate method to ensure reliable and actionable demand forecasts. How to Forecast Demand To forecast demand, businesses can leverage historical sales data and market research to gain insights into past demand patterns and market trends. This data can be analysed using statistical techniques and econometric models to identify factors influencing demand, such as seasonality, economic conditions, and consumer preferences. Businesses can also employ machine learning and artificial intelligence algorithms to analyse large volumes of data and identify complex relationships between variables that may impact demand. Qualitative factors such as consumer behaviour, economic conditions, and competitive activity should be considered when forecasting demand. Consumer surveys, focus groups, and market research can provide valuable insights into consumer preferences and buying patterns. Economic indicators such as GDP growth, inflation, and unemployment rates can also impact demand, while understanding the strategies and actions of competitors can help businesses anticipate changes in market share. Regularly updating and refining forecasts is crucial due to the evolving nature of markets. New information and changing market conditions can quickly render forecasts obsolete. Businesses should establish a process for continuously monitoring demand-related data and incorporate new information into their forecasts as soon as it becomes available. This agility allows businesses to adapt their strategies and make informed decisions in response to evolving market conditions. Businesses can develop robust demand forecasts that support effective decision-making by combining historical data analysis, market research, qualitative insights, and machine learning techniques. Accurate demand forecasting enables businesses to advance production schedules, manage inventory levels, plan marketing campaigns, and allocate resources efficiently, ultimately driving growth and profitability. Demand Forecasting Methods Demand forecasting is critical to business planning, enabling companies to make informed decisions about production, inventory, marketing, and financial strategies. Businesses can utilise various demand forecasting methods to predict future demand for their products or services. Here are some commonly used demand forecasting methods: Time Series Analysis: This method analyses historical demand data to identify patterns and trends. It assumes that future demand will follow similar patterns as observed. Time series analysis includes techniques such as moving averages, exponential smoothing, and seasonal decomposition of time series. Causal Analysis: This method identifies and analyses the causal factors influencing demand. It involves studying the relationship between demand and factors such as economic conditions, market trends, consumer behaviour, and competitive activity. Causal analysis helps businesses understand the underlying drivers of demand and make more accurate forecasts. Judgmental Forecasting: This method involves using the knowledge and expertise of experienced professionals to make demand forecasts. It is often used when historical data is limited or when qualitative factors play a significant role in demand. Judgmental qualitative demand forecasting techniques include expert opinion, the Delphi method, and market research. Machine Learning: Machine learning algorithms can be used to analyse large volumes of data and identify complex patterns that may not be evident through traditional quantitative demand forecasting and methods. Machine learning techniques such as regression analysis, decision trees, and neural networks can be applied to demand forecasting. Econometric Models: These models use statistical and economic theories to forecast demand. They incorporate economic variables such as income, prices, interest rates, and consumer sentiment to predict future demand. Econometric models are often used for short-term demand and long-term demand forecasting. The choice of demand forecasting method depends on several factors, including the availability of historical data, the nature of the product or service, the forecast horizon, and the level of accuracy required. By selecting the appropriate demand forecasting method and regularly updating forecasts based on new data, businesses can improve their decision-making and achieve better operational efficiency and profitability. Demand Forecasting Examples Demand forecasting is a valuable tool for businesses of all sizes and industries. Here are a few examples of how demand forecasting can be used in practice: Retail: A clothing retailer might use demand forecasting to predict how many units of a new product to produce for the upcoming season. By considering factors such as historical sales data, current fashion trends, and economic conditions, the retailer can decide how much inventory to carry to meet customer demand. Manufacturing: A industrial equipment manufacturer might use demand forecasting to predict how many units of a particular product to produce each month. By considering factors such as customer orders, production capacity, and lead times, the manufacturer can ensure that it has enough inventory to meet customer demand without overproducing. Transportation: A logistics company might use demand and forecasting models to predict how much freight it will need to transport each week. By considering factors such as shipping volumes, economic conditions, and weather patterns, the logistics company can ensure that it has enough resources to meet customer demand. Healthcare: A hospital might use demand forecasting to predict how many patients it will need to accommodate daily. By considering factors such as historical patient data, current patient trends, and the availability of medical staff, the hospital can ensure that it has enough resources to meet patient demand. Technology: A software company might use demand forecasting to predict how many licences of a new software product to sell each month. By considering factors such as market research, competitor analysis, and pricing strategy, the software company can ensure that it has enough licenses to meet customer demand without overproducing. Demand Forecasting Trends Demand forecasting has significantly transformed in recent years, driven by technological advancements and changing business dynamics. The emergence of real-time data and machine learning has revolutionised the field, enabling businesses to make more accurate and timely predictions. Real-time data provides businesses with up-to-the-minute information on market conditions, consumer behaviour, and supply chain dynamics, allowing them to respond quickly to changes in demand. Machine learning algorithms analyse vast amounts of data to identify patterns and trends, enabling businesses to make more accurate forecasts and optimise their operations. Collaborative planning is another critical trend in demand forecasting. This approach involves bringing together stakeholders across the organisation, including sales, marketing, production, and finance, to develop demand forecasts collectively. Collaborative demand planning also fosters a shared understanding of market dynamics and ensures forecasts align with the business strategy. By combining the knowledge and expertise of various teams, businesses can improve the accuracy and reliability of their demand forecasts. The rise of artificial intelligence (AI) and advanced analytics further enhances demand forecasting capabilities. AI-powered tools can analyse vast amounts of data, identify complex patterns, and make predictions with a high degree of accuracy. Advanced analytics techniques, such as predictive modelling and simulation, enable businesses to test different scenarios and make informed decisions about their production and inventory levels. By leveraging AI and advanced analytics, businesses can gain a competitive edge by optimising their supply chains and meeting customer demand more effectively. In summary, the evolution of demand forecasting is characterised by integrating real-time data, machine learning, collaborative planning, and AI-powered analytics. These trends are revolutionising how businesses predict demand, enabling them to make more accurate and data-driven decisions. By embracing these trends, businesses can gain a competitive advantage, progress their operations, and meet the ever-changing needs of their customers. How to Choose Demand Forecasting Software Choosing the right demand forecasting software is essential for businesses developing their operations and making informed decisions. With a wide range of demand forecasting software options available, it’s essential to consider several key factors to select the best tool for your organisation. 1. Assess Your Business Needs: Before selecting software, thoroughly assess your business’s unique needs and requirements. Consider the size and complexity of your organisation, the industry you operate in, and the specific forecasting challenges you face. Determine the level of accuracy and granularity required for your forecasts and the types of data you need to analyse. 2. Evaluate Software Features and Functionality: Evaluate the features and functionality offered by different demand forecasting software options. Look for software that provides the necessary capabilities, such as historical data analysis, trend identification, seasonal adjustment, and scenario modelling. Consider the user interface, ease of use, and the level of customisation available to meet your specific requirements. 3. Scalability and Integration: Choose software that can scale to meet your growing business needs. Consider whether the software can handle increasing data volumes and complexity as your business expands. Assess the software’s ability to integrate with your existing systems, including enterprise resource planning (ERP) and customer relationship management (CRM) systems, to ensure seamless data flow and analysis. 4. Cost and Return on Investment: Compare the costs associated with different software options, including licensing fees, implementation costs, and ongoing maintenance and support. Evaluate the potential return on investment (ROI) by considering the benefits the software can bring in terms of improved forecast accuracy, reduced inventory costs, optimised production planning, and enhanced customer service. 5. Customer Support and Training: Consider the level of customer support and training provided by the software vendors. Ensure that the vendor offers responsive and reliable support to address any issues or queries you may have. Assess the availability of training resources, such as user manuals, tutorials, and workshops, to help your team effectively use the software. 6. Data Security and Compliance: Evaluate the software’s security measures to protect your sensitive business data. Ensure that the software complies with relevant industry regulations and standards. Consider the data encryption methods, access controls, and disaster recovery plans offered by the software vendor. By carefully considering these factors, you can select the demand forecasting software that best aligns with your business goals and requirements, enabling you to make data-driven decisions and gain a competitive edge in your industry. Make Demand Forecasting Easier with LIKE.TG Demand forecasting is an essential business process, but getting accurate results can take time and effort. LIKE.TG makes demand forecasting easier with AI-powered tools that help you get accurate results in minutes, collaborate with your team on forecasts, and adjust your forecasts as new data comes in. You’ll also get real-time insights into your demand forecast so you can make informed decisions. LIKE.TG’s demand forecasting tools use a variety of data sources to create accurate forecasts, including historical sales data, current market conditions, and even future sales trends. This data is then analysed using machine learning algorithms to identify patterns and relationships that can be used to predict future demand. LIKE.TG’s demand forecasting tools are easy to use and can be customised to meet the specific needs of your business. You can create forecasts for individual products or services or entire product lines. You can also create forecasts for different periods, such as days, weeks, or months. Once you’ve created a forecast, you can share it with your team and collaborate on it. You can also track the accuracy of your forecasts over time and make adjustments as needed. LIKE.TG’s demand forecasting tools are valuable for businesses of all sizes. They can help you improve your planning and decision-making, ultimately increasing your profitability. Here are some of the benefits of using LIKE.TG’s demand forecasting tools: Get accurate results in minutes: LIKE.TG’s demand forecasting tools use AI-powered algorithms to analyse data and create accurate forecasts quickly and easily. Collaborate with your team: You can share your forecasts with your team and collaborate on them. This makes getting everyone on the same page and making better choices easy. Adjust your forecasts as new data comes in: LIKE.TG’s demand forecasting tools allow you to adjust your forecasts as new data becomes available. This ensures that your forecasts are always up-to-date and accurate. Get real-time insights into your demand forecast: LIKE.TG’s demand forecasting tools provide real-time insights into your demand forecast. This information can help you make better choices regarding your business.

Revenue and forecasting models are significant business practices that predict future revenue based on historical data, future demand, and current trends. It empowers businesses to plan for growth, make informed decisions, and effectively manage their finances. This blog post will closely examine the revenue forecasting model, exploring its benefits, challenges, and methodologies. We will also provide practical tips to enhance backlog revenue forecasting model accuracy and ensure sustainable business growth. Revenue forecasts explained So, why is revenue forecasting important? Revenue forecasting is the foundation within the business planning space, empowering organisations to peer into the future and anticipate their financial trajectory. This entails meticulously analysing historical data and current market trends to make informed predictions about upcoming revenue streams. This process of revenue forecast models acts like a compass for financial planning, guiding businesses through the complexities of decision-making, resource allocation, and financial management of future revenues. The significance of revenue forecasting cannot be overstated. It serves as a starting point for businesses to chart their course towards growth and sustainability. Businesses can allocate their resources judiciously by accurately predicting future revenue growth, ensuring that every dollar invested yields maximum returns. This foresight enables them to make choices regarding investments, sales team hiring, and marketing strategies, significant for startups and small businesses with limited resources. For large enterprises, revenue forecasting is equally important in navigating the complexities of growth and financial management. It gives business leaders the necessary insights to make strategic decisions about product development, market expansion, and capital investments. By anticipating revenue streams or forecasting revenue and drivers, these businesses can elevate their operations, identify revenue growth and expansion opportunities, and mitigate potential risks. Revenue forecasting is an art and a science, blending historical data with market intelligence to make accurate forecasts that paint a vivid picture of the future. By mastering this practice, businesses gain the power to navigate uncertainty and seize opportunities for sustainable growth. It is a practice that empowers businesses to thrive in a dynamic and ever-changing marketplace. Benefits of revenue forecasting Revenue forecasting offers a wealth of benefits to businesses, enabling them to improve resource allocation and navigate the ever-changing market landscape with greater agility. One of the primary advantages of a revenue forecasting business model is its ability to guide businesses in making well-informed decisions about resource allocation. By accurately predicting future revenue, companies can allocate their resources judiciously, directing investments towards areas with the highest potential for growth and profitability. This data-driven approach minimises wastage and maximises returns, ensuring that every dollar invested yields optimal results. Another significant benefit of a revenue forecasting model is its role in proactively managing cash flow and preventing unexpected financial surprises. By using accurate revenue forecasting models and anticipating revenue streams, businesses can effectively plan for upcoming expenses and manage their cash flow more efficiently. This foresight allows companies to avoid cash flow shortfalls, ensuring they have the necessary liquidity to meet their financial obligations and capitalise on new opportunities. Revenue forecasting also plays a key role in setting realistic sales targets for marketing campaigns and tracking the sales pipeline’s progress towards achieving them. With accurate revenue projections, businesses can establish achievable sales goals that align the sales cycle with their overall growth objectives. This clarity enables sales teams to focus on high-priority prospects and develop targeted strategies to drive revenue growth. Regularly monitoring the sales pipeline and team’s progress against these targets allows businesses to make timely adjustments and course corrections, ensuring they stay on track to meet their revenue goals. In summary, revenue forecasting is a powerful tool that empowers businesses to see future sales, make better decisions regarding revenue, optimise resource allocation and growth rate, manage cash flow effectively, and set realistic sales targets. By leveraging historical data and current trends to create a revenue forecast, businesses can gain invaluable insights into their future revenue potential and navigate the complexities of the market with greater confidence and success. Challenges of revenue forecasting Within the scope of revenue forecasting, while presenting a plethora of benefits, is not without its share of formidable challenges. One significant hurdle businesses encounter in performing revenue forecasting is acquiring precise and dependable historical data points. Formulating well-informed predictions hinges on compiling historical and future sales data, market trends, and economic indicators. However, the accuracy of these data sources can be undermined by human error, data manipulation, or external factors that lie beyond a company’s sphere of control. Consequently, generating reliable and accurate revenue forecasts can be an arduous task. Another challenge emanates from the inherent unpredictability of external events. Economic fluctuations, shifts in consumer preferences, technological advancements, and regulatory changes can profoundly impact revenue projections. For instance, the COVID-19 pandemic served as a stark reminder of the disruptive potential of unforeseen events, as it wreaked havoc on global supply chains and consumer behaviour, resulting in substantial revenue losses for countless businesses. Navigating such volatile environments demands high adaptability and responsiveness to changing circumstances. Human error lurks as a constant threat in the revenue forecasting process. Manual data entry, computational errors, and subjective judgments can introduce inaccuracies that undermine the integrity of the revenue and forecasted revenue and models. To mitigate this challenge, businesses must implement robust data validation protocols, embrace automated revenue forecasting models and tools, and involve multiple stakeholders. By doing so, they can minimise the likelihood of human-induced errors and enhance the reliability of their forecast revenue projections. The intricate nature of contemporary business models further compounds the challenges of revenue forecasting. Businesses today operate within dynamic and interconnected markets, rendering accurate predictions of revenue streams increasingly elusive. Factors such as product diversification, global expansion, and evolving customer segments add complexity to the revenue forecasting process. To navigate this, businesses must employ sophisticated revenue forecasting models and techniques and leverage advanced analytics to account for these complexities and improve the precision of their revenue projections. Last but not least, the ever-shifting sands of customer behaviour pose a persistent challenge for revenue forecasting. Consumer preferences, purchasing patterns, and market trends are in perpetual flux, making it arduous for businesses to keep pace. To surmount this obstacle, businesses must constantly be vigilant about market dynamics, conduct regular customer surveys, and meticulously analyse consumer data to gain invaluable insights into these shifting behaviours. By attuning themselves to the pulse of their customers, businesses can refine their revenue forecasts and adapt their sales strategies accordingly, ensuring their continued success in the face of constant change. Types of revenue forecasting methods Several revenue and forecasting tools and methods are available, each with advantages and disadvantages. The choice of method depends on the availability of data, the complexity of the various business models, and the level of accuracy required. One standard revenue forecasting method is the moving average method. This method takes the average of the revenue from a specified number of past periods and uses it to predict future revenue. The moving average method is simple to use and understand, but it can be slow to react to changes in the underlying trend. Another revenue forecasting method is exponential smoothing. This method assigns exponentially decreasing weights to past revenue data, with more recent data given more weight. Exponential smoothing is more responsive to changes in the underlying trend than the moving average method, but it can be more sensitive to noise in the data. Regression analysis is a statistical technique that can be used to predict revenue based on the relationship between expected revenue, and other variables, such as economic indicators, marketing efforts, and competitive activity. Regression analysis can be a powerful revenue forecasting tool, but it requires significant data and can be difficult to implement. Monte Carlo simulation is a technique that uses random sampling to generate a range of possible, future values for revenue outcomes. The Monte Carlo simulation can be used to estimate the probability of achieving different forecast revenue and targets and assess the risk associated with different revenue targets and forecasts. Monte Carlo simulation is a powerful revenue forecasting tool, but it can be computationally intensive and requires significant data. Bottom-up revenue forecasting software is a method that involves building a revenue forecast from the ground up, by starting with individual sales estimates for each product or service and then aggregating them to arrive at a total revenue forecast. The bottom-up revenue forecasting model is a detailed and accurate revenue forecasting method, but it can be time-consuming and complex to implement. The choice of revenue forecasting method depends on the specific needs and circumstances of the business. Some businesses may find that a simple method like the moving average method is sufficient, while others may need a more sophisticated method like regression analysis or Monte Carlo simulation. How to improve revenue forecasting accuracy To improve revenue forecasting accuracy, businesses should leverage historical performance data to identify patterns and trends in past performance that can inform their future sales projections. By analysing past performance, businesses can gain insights into seasonal fluctuations, economic cycles, and customer behaviour that affect revenue, enabling them to make more informed revenue predictions. Additionally, gathering and analysing market research can provide valuable information about industry trends, competitors’ strategies, and customer preferences. This information can be incorporated into revenue forecasts to enhance accuracy and reliability. Incorporating machine learning and artificial intelligence (AI) into revenue and forecasting models can significantly improve the accuracy of predictions. These technologies can analyse large volumes of data, identify complex patterns, and make predictions based on real-time information. By leveraging machine learning and AI, businesses can better understand customer behaviour and market dynamics, resulting in more precise and accurate revenue forecasts. Regularly reviewing sales forecasting and updating forecasts is essential to maintaining accuracy. Businesses should continuously make sales forecasts, monitor actual performance against sales forecast-ed results and adjust sales forecasts as needed. This process ensures accurate forecasting and that forecasts remain aligned with changing market conditions and evolving business strategies. Finally, conducting scenario planning and sensitivity analysis can help businesses create a revenue forecast and assess the impact of different variables on revenue forecasts. Businesses can make more robust and resilient revenue projections by considering various scenarios, such as changes in economic conditions, competitive landscapes, or customer demand. By implementing these strategies, businesses can significantly improve the accuracy of their revenue forecasts, enabling them to optimise resource allocation, and achieve their financial goals. How to Forecast Revenue Building an accurate revenue forecast is crucial for any business looking to allocate resources effectively, and achieve its financial goals. Historical revenue data serves as valuable groundwork to begin the forecasting process. Businesses can identify patterns, seasonality, and growth rates by analysing past revenue and identifying trends therein, providing insights into future performance. The next step, which affects the revenue forecast, involves recognising external factors that may impact revenue, such as market conditions, industry trends, and economic fluctuations. Incorporating these external factors into the revenue forecast helps create a more realistic and comprehensive revenue projection. Businesses can then employ various revenue forecasting models and projection methods to enhance the accuracy of their revenue predictions. Some standard revenue forecasting methods and projection methods include: Moving Average: This method calculates the average revenue over a specific period, such as the last 12 months. It is straightforward and suitable for stable revenue patterns. Exponential Smoothing: This method assigns more weight to recent revenue growth rate data, assuming it is more indicative of future revenue growth rates and trends. It is useful when revenue is growing or declining at a steady rate. Regression Analysis: This statistical technique establishes a relationship between a company’s revenue and one or more independent variables, such as marketing spend or economic indicators. It is effective when a clear correlation exists between revenue and these variables. Monte Carlo Simulation: This method uses random sampling to generate multiple possible revenue outcomes, providing a range of potential scenarios. It is beneficial for complex revenue streams with multiple variables. Bottom-up Forecasting: The bottom-up pipeline revenue forecasting model or method estimates revenue by summing up individual revenue components, such as product lines or customer segments. It is suitable for businesses with diverse revenue streams. By combining historical data analysis, external factor consideration, a forecasting model, and other appropriate forecasting tools and methods, businesses can generate revenue forecasts that are both accurate and reliable. This empowers them to make strategic decisions, optimise resource allocation, and navigate the uncertainties of the business landscape with greater confidence.

Sales analysis is a major component of business success, providing valuable insights into sales performance, customer behaviour, and market trends. By leveraging data analysis techniques, businesses can identify areas for improvement, increase sales strategies, and gain a competitive edge. In this exhaustive guide, we’ll look deeper into sales analysis, exploring its significance, various types of products, sales analysis, key metrics, and the benefits it offers. Additionally, we will uncover the powerful sales analysis tools available within LIKE.TG, empowering businesses to make data-driven decisions and drive growth. What is sales analysis? Today, sales analysis has emerged as a powerful tool that empowers businesses to make informed decisions, better sales strategies, and drive growth. It involves the systematic collection, analysis, and interpretation of data related to sales performance, customer behaviour, and market trends. By leveraging sales analysis, businesses gain valuable insights into their sales operations, enabling them to identify areas for improvement, address challenges, and capitalise on opportunities. Sales analysis plays a key role in understanding the effectiveness of sales strategies and tactics. By analysing sales data, businesses can determine which strategies are yielding positive results and which ones need to be revised. This data-driven approach allows sales managers to allocate resources efficiently, focus on high-potential opportunities, and eliminate ineffective strategies. Sales analysis also provides insights into customer behaviour, preferences, and buying patterns. This knowledge empowers businesses to tailor their sales strategies to meet the specific needs and expectations of their target audience, resulting in enhanced customer satisfaction and increased sales. To continue, sales analysis enables businesses to identify trends and patterns in sales performance. By recognising these trends, businesses can anticipate market changes, adapt their strategies accordingly, and stay ahead of the competition. Additionally, sales analysis helps businesses identify underperforming sales representatives and provides valuable feedback for coaching and training purposes. This data-driven approach to performance management ensures that sales reps and teams are equipped with the skills and knowledge necessary to excel in their roles. The importance of sales analysis Understanding the significance of sales analysis is crucial for businesses aiming to achieve sustainable growth and success. It’s a powerful tool that empowers businesses to make informed decisions, advance their sales strategies, and drive revenue growth by providing valuable insights into their sales performance. Through meticulous analysis of sales data and market research, businesses can uncover hidden trends, patterns, and correlations that reveal customer behaviour, preferences, and buying habits. Armed with this knowledge, they can tailor their sales approach to better align with customer needs, leading to increased sales opportunities and enhanced customer satisfaction. Sales analysis acts as a diagnostic tool, helping businesses identify areas for improvement within their sales process. By pinpointing strengths and weaknesses in sales processes, businesses can allocate resources more efficiently, focusing on high-potential opportunities and providing targeted training to their sales teams. This data-driven approach ensures that sales efforts are optimised, resulting in increased productivity and overall performance. Sales analysis provides a solid foundation for strategic decision-making. It enables businesses to make choices based on facts and evidence rather than simple assumptions or intuition. This analytical approach to sales strategy significantly reduces risks and increases the likelihood of success, allowing businesses to remain competitive and thrive in a dynamic market environment. In essence, sales analysis is an indispensable tool for businesses seeking to drive growth and success. By using sales data analysis and harnessing the power of data, businesses can gain profound insights into their sales performance, identify opportunities for improvement, and make informed decisions that lead to increased revenue and long-term sustainability. Embracing sales analysis is a strategic move that sets businesses on a path of continuous improvement and competitive advantage. Types of sales analysis There are several types of sales analysis that businesses can use to improve their sales performance and grow their business. Some of the most common types of sales analysis include: 1. Sales performance analysis: This type of analysis involves collecting and analysing data on sales performance, such as sales volume, revenue, and market share. This data can be used to identify trends and patterns in sales performance, as well as to identify areas for improvement in the sales pipeline. 2. Sales forecasting: This type of analysis involves using historical sales data to predict future sales. This can be used to help businesses make informed decisions about resource allocation, production levels, and marketing campaigns. 3. Customer segmentation: This type of analysis involves dividing customers into different groups based on their demographics, psychographics, and buying behaviour. This can be used to help businesses tailor their marketing and sales strategies to specific customer groups. 4. Product profitability analysis: This type of analysis involves calculating the profitability of individual products or product lines. This can be used to analyse sales and help businesses make decisions about which products to focus on and which products to discontinue. 5. Competitor analysis: This type of analysis involves collecting and analysing data on competitors’ sales performance, marketing strategies, and product offerings. This can be used to help businesses identify competitive advantages and develop strategies to differentiate themselves from their competitors. Get articles selected just for you, in your inbox Sign up now Sales analysis metrics KPIs Sales analysis metrics and KPIs are essential for measuring sales performance, using sales targets, identifying areas for improvement, and making informed decisions about sales strategies. These metrics provide businesses with valuable insights into their sales performance and help them track their progress towards achieving their sales goals. Some of the most common predictive sales analysis, metrics and KPIs include: 1. Sales revenue: This metric measures the total amount of revenue generated from sales. It is a key indicator of the overall financial performance of the sales team and can be used to track the team performance and sales growth over time. 2. Number of sales: This metric measures the total number of sales transactions completed. It can be used to track the sales volume and identify trends in sales activity. 3. Average order value: This metric measures the average amount of money spent per sales transaction. It can be used to track the profitability of a sales rep and identify opportunities to increase the average order value. 4. Customer acquisition cost: This metric measures the cost of acquiring a new customer. It can be used to track the efficiency of sales and marketing efforts and identify opportunities to reduce customer acquisition costs. 5. Customer lifetime value: This metric measures the total amount of revenue that a customer is expected to generate over their lifetime. It can be used to track the profitability of customers and identify opportunities to increase customer loyalty. 6. Sales cycle length: This metric measures sales pipeline analysis and the average amount of time it takes to complete a sales transaction. It can be used to track the efficiency of the sales process and identify opportunities to shorten the sales cycle. 7. Win rate: This metric measures the percentage of sales opportunities that result in a closed sale. It can be used to track the effectiveness of the sales process and identify opportunities to improve the win rate. These are just a few examples of the many sales analysis metrics and KPIs that businesses can use to measure their sales performance. By tracking these sales trend analysis metrics and KPIs, businesses can gain valuable insights into their sales performance and make informed decisions about their sales strategies. Benefits of sales analysis Sales analysis is a powerful tool that can help businesses improve their sales performance and efficiency. By analysing sales data, businesses can identify areas for improvement and growth, and make informed decisions about resource allocation and sales strategies. One of the key benefits of sales analysis is that it provides actionable insights for decision-making. By understanding which sales strategies are working and which ones are not, businesses can make adjustments to improve their sales performance. For example, if a business finds through sales analytics that a particular product is not selling well, it can decide to discontinue that product or develop a new marketing strategy to increase sales. Sales can perform a sales analysis that can also help businesses with forecasting and budgeting. By analysing historical sales data, businesses can make informed predictions about future sales. This information can be used to develop budgets and make decisions about staffing levels and inventory. Finally, sales analysis can help businesses improve customer satisfaction and loyalty. By understanding customer buying patterns and preferences, businesses can develop products and services that meet the needs of their customers. This can lead to increased sales and customer loyalty. A sales analysis report is a valuable tool that can help businesses improve their sales performance, efficiency, and customer satisfaction. By analysing sales data, businesses can gain insights into their sales process, identify areas for improvement, and make informed decisions about resource allocation and sales strategies. Sales analysis tools at LIKE.TG Sales analysis tools are essential for businesses that want to understand their sales performance and make informed decisions. LIKE.TG offers a range of various sales analysis reports and tools that can help businesses of all sizes improve their sales performance. These tools include LIKE.TG Analytics Cloud, Einstein Analytics, Tableau CRM, Datorama, and LIKE.TG reports and dashboards. LIKE.TG Analytics Cloud is a powerful business intelligence platform that provides users with a variety of tools for data analysis and visualisation. With LIKE.TG Analytics Cloud, businesses can create custom reports and dashboards to track their sales performance, identify trends, and make informed decisions. Einstein Analytics is a cloud-based artificial intelligence platform that can help businesses predict future sales trends and identify opportunities for growth. Einstein Analytics uses machine learning and artificial intelligence to analyse data and provide businesses with actionable insights. Tableau CRM is a cloud-based analytics platform that provides businesses with a variety of tools for data visualisation and analysis. Tableau CRM can be used to create interactive dashboards and reports that make it easy for businesses to track their sales performance and identify trends. Datorama is a cloud-based marketing analytics platform that can help businesses track their marketing performance and measure the ROI of their marketing campaigns. Datorama can be used to integrate data from multiple sources, including LIKE.TG, Google Analytics, and Adobe Analytics. Finally, LIKE.TG reports and dashboards provide businesses with a way to track their sales performance and identify trends. With LIKE.TG reports and dashboards, businesses can create custom reports and dashboards to track the sales metrics that are most important to them.

A Sales-Qualified Lead (SQL) is a potential customer thoroughly assessed by both the marketing and sales teams. Having demonstrated an intention to purchase and meet specific lead qualification criteria, this prospect is considered suitable for advancing to the next phase in the sales process. Once a prospect surpasses the engagement stage, they receive the SQL label, signifying readiness for targeted efforts to convert them into a valued customer. A sales-qualified lead stands at that critical point in the sales process where they have moved beyond the initial interest or basic awareness and are now showing a clear intent to purchase. The way this distinction is made is through a meticulous qualification process, examining factors like your lead’s need for your product or service, their decision-making authority, and their readiness to make a purchase. Identifying a sales-qualified lead means acknowledging a lead’s transition from considering your offerings to actively seeking to solve a problem or fulfilling a need with what you have to offer. In the following piece, we’ll take a closer look into how sales-qualified leads are identified, the criteria that set them apart, and strategies for effectively managing and converting these valuable prospects. After all, understanding the nuances of sales-qualified leads is essential for any sales team aiming to advance their sales process and achieve better outcomes. Why are sales-qualified leads important? Sales-qualified leads (SQLs) are important because they represent the prospects that are most likely to convert into customers. They have already shown interest in a company’s product or service. They may have visited the company’s website, downloaded a whitepaper, or attended a webinar. By focusing on SQLs, sales teams can increase their efficiency and close more deals. In addition to representing new business potential, SQLs can help businesses focus their sales efforts on the most promising leads. By qualifying leads, sales teams can identify the prospects that are most likely to be a good fit for their product or service. This allows them to allocate their resources more effectively and focus on the leads that are most likely to close. The more information a business has about a lead, the better it can qualify them and determine if they are an SQL. Some of the most important information to collect about a lead includes their name, company, job title, email address, phone number, and interests. Businesses can also collect information about a lead’s budget, timeline, and pain points. This information can help sales teams to better understand the lead’s needs and tailor their sales pitch accordingly. By focusing on SQLs, sales teams can increase their efficiency and close more deals. SQLs represent the potential for new business and can help businesses focus their sales efforts on the most promising leads. By qualifying leads, sales teams can identify the prospects that are most likely to be a good fit for their product or service and allocate their resources more effectively. SQL vs. MQL Sales-qualified leads (SQLs) and marketing-qualified leads (MQLs) are two important concepts in the sales process. While both types of leads represent potential customers, they have key differences. MQLs are leads that have been generated by marketing efforts, such as advertising, email campaigns, or social media. These leads have expressed some interest in a company’s product or service, but they have not yet been qualified by the sales team. SQLs, on the other hand, are leads that have been qualified by the sales team as being worth pursuing. These leads have met certain criteria, such as having a budget, a need for the product or service, and the authority to make a purchase decision. The difference between SQLs and MQLs is important because it allows sales teams to focus their efforts on the most promising leads. By qualifying leads, sales teams can avoid wasting time on leads that are not likely to convert into customers. Here’s a closer look at the main differences between sales-qualified leads and marketing-qualified leads: Stage in the Sales Funnel: Marketing-qualified leads are just beginning their journey, their interest has been piqued, and yet they’re not yet ready to buy. Sales-qualified leads are further along and prepared to discuss purchasing. Engagement Level: Marketing-qualified leads interact with your content, showing interest. Sales-qualified leads take significant actions, like asking for a demo, indicating they’re ready to consider a purchase. Qualification Process: Marketing teams identify marketing-qualified leads based on their engagement activities. Sales teams then rigorously evaluate the sales-qualified leads, confirming their readiness and compatibility with what’s on offer. By understanding these distinctions, you can tailor your approach to nurturing and converting leads more effectively. Recognising what makes each type of lead unique allows your marketing and sales teams to align their efforts, moving leads through the sales funnel more efficiently and boosting your chances of making a sale. The shift from marketing to sales-qualified leads isn’t just about sorting leads; it’s about adopting a more strategic mindset that understands each buyer’s journey. It ensures every touchpoint is timely and relevant and propels the prospect closer to saying ‘yes’ to your solution. How Do Organisations Identify SQLs? Different businesses adopt a variety of criteria to identify their sales-qualified leads, a main point in refining the sales process. The success of this strategy depends on consistent collaboration between sales and marketing teams, aimed at ensuring the most promising leads are quickly recognised and nurtured, transitioning smoothly from marketing-qualified to sales-qualified status. Evaluating Engagement and Interest: Finding a viable sales-qualified lead starts with assessing how a lead interacts with your marketing efforts and their demonstrated interest in your offerings. This includes tracking website visits, content downloads, and social media activity and employing a scoring system to prioritise leads based on their level of engagement. This quantitative approach helps single out leads actively seeking solutions, ensuring focus is placed on those most interested. Assessing Budget and Authority: It’s critical to understand a lead’s interest and their ability to make purchasing decisions. By engaging leads with targeted questions, teams can gauge whether a lead has the necessary budget and decision-making authority, focusing efforts on leads capable of moving forward in the sales process. Determining Fit and Need: Assessing whether a lead’s requirements align with your offering involves a detailed look at company size, industry, and specific challenges. Marketing teams are key in this phase, using targeted content and communications to evaluate a lead’s needs and how they match up with your solutions, an essential step in moving a lead towards being sales-qualified. Timeline Consideration: Understanding when a lead plans to make a purchase is also highly important. Sales teams work to align a lead’s buying timeline with the business’s sales cycle, an essential time in deciding if a lead is ready to be considered a sales-qualified lead. The Harmony Between Sales and Marketing: The transition from a marketing-qualified lead to a sales-qualified lead highlights the critical nature of sales and marketing collaboration. Through regular communication and agreed-upon lead scoring criteria, both departments ensure that only the most qualified leads progress through the sales funnel. This partnership is key to refining the lead qualification process, optimising how resources are allocated, and boosting the overall efficiency and effectiveness of the sales strategy. Identifying sales-qualified leads represents a strategic, coordinated effort between sales and marketing, driven by data and an extensive understanding of the customer. This meticulous approach makes the sales funnel more efficient and ensures that sales initiatives are targeted towards leads with the highest conversion potential, fostering sustained business growth. SQL vs. MQL Examples Revisit the comparison between SQLs and MQLs, providing more in-depth insights into the specific characteristics and behaviours that set them apart. Use real-world examples to illustrate scenarios where a lead may transition from being an MQL to an SQL. When refining the sales journey, it’s essential to have a thorough understanding of that defining shift in the funnel from marketing-qualified lead to sales-qualified lead, as it will ensure a more efficient path to purchase for your sales team. Marketing-qualified leads, sparked by marketing engagements such as content downloads, signal that initial interest. This, however will likely evolve into a sales-qualified lead, which marks a deeper intent to buy, demonstrated through actions like premium content engagement. This transition shows us that the lead is now ready for a direct sales interaction. For example, consider a lead’s participation in a detailed product webinar or their consistent interaction with targeted emails. These actions would signify a readiness to begin to explore solutions, positioning them perfectly for a shift to sales-qualified lead status. Recognising and effectively fostering these moments can significantly enhance the effectiveness of the sales funnel, transitioning leads into customers more smoothly and successfully. For sales teams, engaging sales-qualified leads means adopting a nuanced and specialised approach. The interaction history of each lead, be it a closer look into a webinar or a keen interest in pricing information, demands a unique follow-up strategy. This personalisation ensures that sales communications resonate deeply, addressing each potential customer’s specific needs and interests. Leveraging analytics and lead scoring sharpens this focus, pinpointing the subtle but significant signs of a lead’s progression from a marketing-qualified lead to a sales-qualified lead. Such precision makes the most of resource allocation and maximises your conversion opportunities. It’s essential to have a strong feedback loop between sales and marketing teams to enrich this process, fine-tuning lead qualification criteria to ensure a consistently high-quality pool of sales-qualified leads ready for engagement. This collaborative effort extends beyond simple process efficiency, enhancing customer experience and fostering loyalty. By guiding leads through their buying experience with detail-oriented, strategic insights and tailored engagement, businesses can achieve not just higher conversion rates but also build lasting relationships with their customers. Moving a Lead from MQL to SQL Moving a lead from MQL to SQL involves several key steps that help sales teams identify and qualify leads with the highest potential for conversion. Here’s a detailed look at the process: 1. Determine BANT Criteria Fulfillment:Before qualifying an MQL as an SQL, sales representatives assess whether the lead meets the BANT criteria: Budget, Authority, Need, and Timeline. This evaluation determines if the lead has the financial resources, decision-making authority, genuine requirement for the product or service, and a specific timeframe for purchase. Leads that satisfy these criteria are considered strong candidates for further qualification. 2. Lead Scoring:Lead scoring is vital in prioritising MQLs based on their likelihood of converting into customers. Sales teams assign numerical values to various lead attributes, such as industry, company size, job title, engagement level, and website activity. Leads with higher scores are deemed more sales-ready and are nurtured accordingly. 3. Lead Nurturing:Nurturing MQLs involves providing them with relevant information and resources that educate them about the product or service and address their pain points. This can be achieved through personalised email campaigns, webinars, case studies, and content marketing. The goal of lead nurturing is to build trust, credibility, and desire, ultimately moving the lead closer to becoming sales-qualified. 4. Scheduling a Meeting or Call:Once an MQL demonstrates a strong interest in the offering and exhibits readiness to engage in a sales conversation, the next step is to schedule a meeting or call. This allows the sales representative to delve deeper into the lead’s requirements, understand their challenges, and present tailored solutions. 5. Closing the Deal: The final stage of the MQL to SQL journey involves closing the deal and converting the lead into a customer. This entails negotiating terms, addressing objections, and guiding the lead through purchasing. Successful deal closure relies on effective communication, skilful negotiation, and a customer-centric approach. By systematically following these steps, sales teams can effectively identify and qualify MQLs, nurturing them into SQLs and ultimately driving revenue growth. The Difference Between an MQL and SQL In the sales world, understanding the difference between a Marketing Qualified Lead (MQL) and a Sales Qualified Lead (SQL) is crucial for optimising lead generation and conversion processes. While both MQLs and SQLs represent potential customers, they are at different stages of the sales funnel, each requiring distinct strategies for nurturing and qualification. An MQL is a lead that has shown some interest in a company’s product or service, typically through interactions with marketing initiatives such as website visits, content downloads, or email campaigns. MQLs have demonstrated a level of awareness and engagement with the brand but may not yet be ready to make a purchase decision. Nurturing MQLs involves providing them with relevant content, answering their questions, and building trust to move them further down the sales funnel. On the other hand, an SQL is a lead that has been deemed by the sales team to be a good fit for the company’s product or service and is ready to be contacted by a salesperson. SQLs have expressed a stronger interest in the offering and have typically engaged in more substantial interactions with the company, such as requesting a demo, scheduling a consultation, or providing contact information. They are considered sales-ready and more likely to convert into customers than MQLs. The key difference between an MQL and an SQL lies in their level of qualification and readiness to engage with the sales team. MQLs require further nurturing and education to become SQLs, while SQLs are considered hot leads actively considering a purchase and ready for direct contact from a salesperson. Why Differentiating Between MQLs and SQLs is Important Distinguishing between MQLs and SQLs is of paramount importance for several reasons. Firstly, it enables businesses to allocate their resources more efficiently. By focusing their efforts on SQLs, sales teams can prioritise leads that are most likely to convert, maximising their chances of success. This targeted approach allows businesses to optimise their sales processes and achieve greater investment returns. Secondly, differentiating between MQLs and SQLs enhances sales team productivity. Sales representatives can concentrate their time and energy on nurturing and converting SQLs, rather than wasting effort on unqualified leads. This increased focus leads to higher productivity, improved sales performance, and increased revenue generation. Moreover, differentiating between MQLs and SQLs elevates the customer experience. Businesses can provide more relevant and personalised interactions by engaging with leads who are genuinely interested in their offerings. This enhances customer satisfaction and builds stronger relationships, fostering loyalty and increasing the likelihood of repeat business. Lastly, distinguishing between MQLs and SQLs offers valuable insights into the sales funnel. By analysing the conversion rates of MQLs to SQLs, businesses can gain a deeper understanding of their sales process and identify areas for improvement. This data-driven approach allows businesses to refine their lead generation and nurturing strategies, continuously optimising their sales funnel and driving sustainable growth. In conclusion, differentiating between MQLs and SQLs is crucial for efficient lead management and revenue generation. By recognising and nurturing SQLs, businesses can optimise their sales efforts, enhance customer experiences, and gain valuable insights into their sales funnel, ultimately achieving greater success and profitability. SQL vs. MQL: A deeper dive Sales-qualified leads (SQLs) and marketing-qualified leads (MQLs) are pivotal concepts in the sales and marketing realm. SQLs are prospects deemed worthy of pursuit by the sales team, while MQLs are potential customers generated through marketing initiatives but not yet deemed sales-ready. Distinguishing between SQLs and MQLs is crucial for efficient lead management and revenue generation. SQLs are often further along the sales funnel, exhibiting a higher interest in a company’s offerings and a greater likelihood of purchasing. These leads hold higher value for the company as they have a greater potential to close deals and contribute to revenue growth. On the other hand, MQLs require further nurturing before they can be considered sales-ready. These leads have demonstrated some level of interest in a company’s products or services but need additional qualifications to assess their purchase intent and readiness. Marketing teams are vital in generating MQLs by implementing targeted campaigns and capturing relevant data from potential customers. While the distinction between SQLs and MQLs is essential, there’s no rigid formula for categorising leads. Different companies may have varying criteria for defining SQLs and MQLs based on their specific sales processes and target markets. However, understanding these key differences enables sales and marketing teams to collaborate effectively, focusing their efforts on leads with the highest conversion potential. By aligning their strategies and nurturing MQLs into SQLs, sales and marketing teams can optimise their lead generation and conversion processes, ultimately driving revenue growth and achieving organisational success. Saleforce and sales qualified leads LIKE.TG is one of the most popular customer relationship management (CRM) platforms on the market, and it offers a range of features that can help businesses manage and track sales-qualified leads (SQLs). The Sales Cloud Lead Management module provides a centralised location for storing lead information, tracking interactions, and managing the sales process. LIKE.TG also allows businesses to create custom lead-scoring models and qualification criteria, so they can focus their sales efforts on the most promising leads. One of the benefits of using LIKE.TG for SQL management is that it provides a way to track the entire customer journey, from the initial lead capture to the closed deal. This allows businesses to identify which marketing and sales strategies are most effective and to make adjustments as needed. LIKE.TG also provides a range of reporting and dashboard options, so businesses can easily track their progress and measure their success. Here are some specific ways that LIKE.TG can be used to manage and track SQLs: Lead capture: LIKE.TG can be used to capture leads from various sources, including website forms, email marketing campaigns, and social media. Lead scoring: LIKE.TG can be used to score leads based on various criteria, such as their industry, company size, and recent website activity. Lead qualification: LIKE.TG can be used to qualify leads based on specific criteria, such as their budget, timeline, and decision-making process. Opportunity management: LIKE.TG can be used to track the progress of sales opportunities, from the initial contact to the closed deal. Reporting and dashboards: LIKE.TG provides a range of reporting and dashboard options, so businesses can easily track their progress and measure their success. Businesses can use LIKE.TG to manage and track SQLs to improve their sales efficiency, close more deals, and grow their revenue.

Today’s world faces unprecedented disruption and change. The digitisation of every aspect of our life, economy, and society continues rising. To thrive in this dynamic ecosystem, an organisation needs true business agility and innovation at scale. This calls for a new operating paradigm to drive digital evolution in the new world. This is where API-led connectivity comes in. The future of business is composable, connected, and automated. Any successful future organisation must adopt composability as it’s the means to resilience, adaptability, and growth in the face of change and disruption. What is API-led connectivity? API-led connectivity is a methodical way to connect data to applications through reusable and purposeful APIs within an organisation’s ecosystem. These APIs are developed to play a specific role: unlocking data from systems, composing data into processes, or delivering an experience. Building blocks are the most fundamental unit of the composable enterprise. They have a clearly articulated purpose of driving a business or technology outcome. They can be automated and orchestrated with other capabilities, making them interoperable. They are discoverable, accessible, and manageable. They represent the “nouns and verbs,” or the “vocabulary” of your business. It’s the API that converts a piece of software into a building block by enabling governance, manageability, visibility, security, monetisation, intelligence, and discovery. API-led connectivity goes beyond the REST APIs to enable universal connectivity. Why is API-led connectivity important? API-led connectivity is fundamental in driving business agility for an organisation. It allows an organisation to tap into the innovation done by other players in their ecosystem. As the picture above suggests, a retail business leverages capabilities (shipping, payments, marketing, infrastructure, social media, sentiment analysis, geo-location, etc.) from other organisations in addition to its own capabilities to drive success now. The flexibility in connecting both the internal and external building blocks to meet the business needs is the key to driving business agility. So when a new initiative comes along, rather than building the solution components, API-led connectivity enables the rewiring, reconnecting, and orchestrating of the building blocks. The winner in the digital race is not the one who creates the fastest, but who integrates the fastest. This makes API-led connectivity a critical integration strategy for an organisation. The number of moving parts and the complexity of the technology and business landscape will continue to increase. So the traditional ad-hoc point-to-point connections often implemented as an afterthought will not scale. They have led to brittle systems that are prone to failure and prohibitive to maintain. API-led connectivity, on the other hand, is future-proof and enables scalable universal connectivity. It changes the role of integration from a necessary evil to a business differentiator. It enables a flexible model for value exchange between building blocks, thereby allowing organisations to have agility in implementing innovative business models. What are the 3 APIs that enable API-led connectivity? API-led connectivity provides an approach for connecting and exposing building blocks in an ecosystem. Their scope can vary: within a specific domain, within a line of business (LoB), across an organisation spanning multiple LoBs or geographies, and into the external ecosystem. There is a natural tiering as well that moves from the system of records to the system of engagements. The APIs used in an API-led approach to connectivity fall into three categories: System APIs Process APIs Experience APIs System APIs System APIs usually access the core systems of record and provide a means of insulating the user from the complexity or any changes to the underlying systems. They create the nouns of your business vocabulary into reusable building blocks. Once built, many users can access data without any need to learn the underlying systems and can reuse these APIs in multiple projects. Process APIs Process APIs interact with and shape data within a single system or across systems (breaking down data silos). They often represent the verbs of your business vocabulary. They help implement an organisation’s processes without having to worry about the source systems where data originates or the target channels through which that data is delivered. They lend themselves very well to automation capabilities and Bots. Experience APIs Experience APIs are catered toward delivering a delightful end-consumer experience. They get their power by maniacally focusing on the consumer and reusing the building blocks already created (typically in the form of System or Process APIs). Often built by a different persona, they can speed up delivery by working from the API specs built as a part of the design-first approach. This drives a coherent omnichannel experience without having to go back to the system of records in an unmanageable point-to-point fashion. Get IT articles selected just for you, in your inbox Sign up now How does API-led connectivity work? API-led connectivity is a critical element in closing the IT delivery gap and enabling the composable enterprise. Let’s use a simple scenario to explain this point: Suppose you need to develop a web app to provide real-time order status and order history for sales teams to engage with customers. Let’s assume you have customer data in SAP and LIKE.TG, inventory data in SAP, and order data in an e-commerce system. In a traditional point-to-point integration approach, your IT team might aggregate customer data by wiring together customer data from both systems with code. Then, the aggregated customer data is further combined with order data in the e-commerce system to produce both the order status and order history data with more code. Now, these two data sources are hooked into a web app API which the web app can leverage. This project might be considered a success because it was launched on time, on budget, and has the correct functionality – but does it solve for business agility? If the IT team must build a mobile app, they aren’t able to use any of the work from previous projects. They have to start from scratch. Incremental changes become expensive, and soon the familiar and undesirable spaghetti code pattern begins to take shape. But with an API-led connectivity approach, when teams must build a new mobile app, they now have reusable building blocks to start from (created from System and Process APIs), eliminating most of the work needed to build them. Creating the mobile app, therefore, is a matter of rewiring instead of recreating. This makes it easier to innovate and add new services, e.g. adding shipment status information in the same way they accessed order status and history. This is the key to driving agility and adopting a product mindset as opposed to a project mindset. API-led connectivity is not limited to just RESTful APIs; it also relies on flexible universal connectivity patterns. How does API-led connectivity reduce IT’s workload? As change and demands for digitisation grow, IT finds itself in a tough spot. The number of new projects necessary to implement today’s technology and business needs measured against IT’s capacity to deliver them is spiraling upward. IT has to deliver on these ever-increasing projects and maintain legacy systems even as its resources stay constant. Eventually, what results is an IT digital transformation delivery gap: Most IT decision-makers expect their budgets to stay the same or increase very slightly, so unlimited resources are not an option. This is where the digital paradigm of building a composable, connected, and automated enterprise is the way out. Rather than delivering on individual projects, IT delivers the reusable building blocks of the enterprise, and with the right tooling and automation, enables LoB folks to innovate as well. API-led connectivity is the cornerstone of building this connected ecosystem. Every new project permits the creation of new building blocks. So when a new initiative comes along, rather than starting from scratch, API-led connectivity enables their reuse. This re-assembly can reduce the IT digital transformation delivery gap. Emergent benefits of API-led connectivity When an organisation uses API-led connectivity to build a composable enterprise, they can eliminate the IT digital transformation gap. Business agility API-led connectivity allows an organisation to tap into the innovation done by other players in their ecosystem. This enables businesses to be nimble and agile, not only in connecting to the right building blocks but also providing flexibility in the business value exchange models. As the picture shows, it’s not just the technical flexibility, but this connectivity also enables the right kind of value exchange between building blocks. For example, if you adopt a freemium monetisation strategy, you can have a different level of SLA for a trial customer and a different Platinum SLA for your Tier 1 customers. Build vs. buy: Driving the business differentiator API-led connectivity in the composable ecosystem helps business and IT leaders make the right build vs. buy decision. The choices made here, what to build versus buy or partner, have far-reaching consequences on the success of a project and its time-to-value. Businesses build their business differentiator, which captures their intellectual property, which they can monetise. You can integrate the supporting domains into the composable enterprise. So the all-important build vs. buy decision becomes create vs. integrate decision enabled by API-led connectivity. Drive the intelligent enterprise Through API-led connectivity, businesses can have end-to-end real-time visibility into their data flows, thereby creating an organisation’s central nervous system. This ‘business context aware’ visibility into the data and the related meta data enables them to see the forest for the trees and to drive network intelligence, analytics, and data science/machine learning models that were previously unattainable. It also lets the organisation collect real-time business KPIs, which eventually help them measure and fine-tune their business operations and strategy. Break data silos and create a customer 360 This universal connectivity also helps break data silos. It lets you build a true customer 360 with data attributes and sources that span across the entire ecosystem (internal, LoB, or external). APIs are the purest form of data: Context-aware, real-time, domain-specific, secure, and curated for consumption. API-led connectivity also delivers a coherent way to engage with your customers across any channel seamlessly. Experience APIs drive a specific channel of user engagement. By connecting to the Process APIs as opposed to the systems of record directly, they drive a consistent user experience and make it easy to spin up a new channel. API-led connectivity in the composable enterprise can drive any System of Engagement. The engagement layer could be a LIKE.TG Cloud, Slack, or any other technology component. This is critical in driving a consistent and coherent omnichannel experience for your customers. How does MuleSoft enable API-led connectivity? MuleSoft has pioneered the API-led connectivity architectural paradigm, which has now found universal acceptance. The key part of the offering starts from the vision of driving business agility at scale by enabling the composable, connected, and automated enterprise, as mentioned earlier. The next part is the methodology: the architectural paradigm of connecting your organisation’s building blocks using API-led connectivity as a key pillar to delivering on this vision. It’s the actual product capabilities and continuous innovation to make the vision a reality delivered through the Anypoint platform and related product capabilities. It provides the most flexible ways of connecting the building blocks: REST Connect, Orchestration, RPA, BOTs, GraphQL, EDI, and more. It supports various integration patterns: APIs, PubSub, EDA, ETL, ELT, microservices, ESB, B2B, SFTP, and others. A rich marketplace with pre-built OotB box connectors, templates, and accelerators for key industries and SaaS providers makes it easy to start enabling universal connectivity in your ecosystem. The tightly integrated iPaaS, full API lifecycle, and automation capabilities help accelerate your digital transformation journey. On average, MuleSoft’s customers found that the agility and speed provided by API-led connectivity led to delivering projects three to five times faster and increased team productivity by 300% compared to legacy or homegrown integration solutions. Examples of API-led connectivity in action Let’s look at real-world scenarios to understand the impact of API-led connectivity. Financial industry: Multiple LoBs and omnichannel Consider a scenario where an organisation provides multiple offerings to its customers through four different LoBs operating under different brands: checking and account management, loans and credit cards, savings and investments, and auto loans. The four LoBs operated in their silos resulting in a broken customer experience and a missed opportunity to cross-sell and upsell the customer. This is the illustrative three-layered ALC architecture for them: They started their journey by creating a Process level “Identity and Authentication Customer” API, providing a consistent way to authenticate their customers across all offerings. A significant step forward in driving CX and a necessary step in its digital transformation journey. The “Get Accounts, and Transactions” API in the Process layer was instrumental in driving a consistent omnichannel experience. It tapped into the four system APIs below: Core Bank Accounts API, Loans API, Credit Card API, and Auto Lease API – each representing the four different LoBs. This enables a holistic view of the customer’s financial health. Not only that, but the same “Get Accounts and Transactions API” can now power multiple experiences: the financial advisor in the financial services cloud, marketing cloud, online banking platform, and mobile banking app. Developers don’t have to duplicate the work of going from the top to the bottom of the stack repeatedly. This simplifies the architecture, reducing the long-term operational cost, and it’s future-proof. It gives the organisation the ability to switch the core banking provider without having any significant upstream/downstream impact, thereby enabling a true plug-and-play architecture. It also allows multiple providers to co-exist during the transition period without disrupting the business. This is a great example of how API-led connectivity drives a true customer 360. Transportation industry: Partner and supply chain Consider a scenario of a company that provides freight and transportation services to mid-market clients. Their business strategy required them to onboard new partners quickly, so they built an EDI transformation layer using MuleSoft’s Partner Manager to cater to their partner’s different data formats and transport protocols. They ended up reducing the time to onboard a new partner from six to nine months down to 60 days. This is the illustrative three-layered ALC architecture for them. But the story doesn’t end there. It’s not just about getting the right information from your partner, supplier, or manufacturer in the door, but how you act on it with other entities inside your organisation to drive efficiency, visibility, and actionable insights. That’s where API-led connectivity complements the traditional B2B/EDI patterns. The System layer at the bottom unlocks the system of records, or the “nouns” of your organisation. For example, you could use the OotB connector for SAP to unlock the invoice or the location data. The process layer orchestrates the System APIs to model your business processes. The Experience layer on the top is customised to deliver a delightful end-customer experience. The beauty of this architecture is that each layer abstracts the complexity from the layer below and creates reusable building blocks. So the shipment 360 API that draws from order, transportation, location, and inventory can not only service the partner ecosystem, but the same shipment API can also drive up the customer experience by powering the service portals and mobile apps as well. API-led connectivity-based architecture is built for agility and reuse. How can I learn more about API-led connectivity? To discover more about customers in every industry who have benefited from API-led connectivity, find out how API-led connectivity enables digital transformation.

The past few years have brought new ways for marketers to connect with customers, but email is still a powerful way to engage. In fact, customers say email still is their preferred channel to interact with brands. According to our research, the number of outbound emails increased 15% last year. The volume of sends is high because it’s driven by high customer engagement. And now AI in email marketing is helping to boost results even more. AI is helping us in many ways, but it’s still in the early stage. There are plenty of questions that marketers need to answer before fully taking advantage of the technology. How do we integrate AI into our strategy? How will AI work with our existing platform? How will we know AI will target customers the way we want? Read on for tips on how AI can help your email marketing, from generating messages to optimising their performance. What is AI in email marketing? How can AI in email marketing increase performance? What are the challenges with AI in email marketing? How can AI help with my email content? How does AI in email marketing improve ROI over time? What are the best practices for using AI in email marketing? What’s ahead for AI email marketing What is AI in email marketing? AI in email marketing uses machine learning algorithms to personalise content, optimise send times, and segment audiences. While predictive AI provides insights based on historical data, generative AI can use this information to create new, relevant content or solutions that are tailored to specific user needs at speed and scale. They work together to automate, optimise, and personalise the email marketing process. Both have the same goal: improved email marketing engagement and customer satisfaction. AI — which is embedded into many marketing platforms — can help you optimise and deliver great email marketing campaigns, as long as you understand how the technology works. For example, by analysing a customer’s response to various email campaigns and website interactions, AI can assign a lead score that indicates the likelihood of conversion. AI can also provide insights into the potential revenue generated by each customer over their lifetime with the brand. You can also prompt AI to generate profiles of ‘lookalike‘ audiences, enabling you to expand your reach to new prospects who are more likely to engage and convert. How can AI in email marketing increase performance? One marketer recently told us that generative AI is where “creativity meets innovation and personalisation takes centre stage.” It’s clear that they’ve tapped into its ability to create natural language-based segments for more nuanced messaging. As customers’ preferred communication patterns are identified, you can segment customers to deliver highly personalised and targeted content at scale. By using AI, you increase your chances to understand and represent your customer’s preferences. In doing so, AI not only enhances customer experiences but also makes them inclusive, ensuring a diverse range of customer preferences are represented. With its ability to analyse historical customer engagement patterns – such as open rates, click-through rates, and conversion rates – predictive AI can identify the best moments to send emails to individual recipients. Subscribers receive emails according to their preferences, which minimises email fatigue and enhances engagement and loyalty. Which brings us to email A/B testing, the strategy where you provide different email versions to your audience to help you figure out which variation performs better. The responses readers take are clear-cut, meaning they choose between two-option reactions or actions, such as opening or not opening an email. When you use AI to test email subject lines, you can find out which one generates higher engagement rates. You’ll also maximise clickthrough rates which will help you fine-tune your messaging – keeping in mind the goal of discovering what works best for a given audience. One marketer reported how their A/B testing improved 10x using generative AI in email marketing. “Instead of testing only subject lines, I can also test user behaviour, allowing me to be more strategic with every send,” they told us. “Along with content, I also use AI in the design process. It helps me select images and colours that best resonate with my target audience.” AI helps get the low-expertise structure done so you can add the high-value content and your specified point of view. For example, you can ask it for a list of subcategories and their definitions for a topic you’re exploring. Get articles selected just for you, in your inbox Sign up now What are the challenges with AI in email marketing? As you adopt and adapt AI, you’ll see benefits like increased personalisation at scale, improved engagement, and reduced costs. However, there could be undesirable consequences if you don’t grasp the fundamentals.Safeguards must be in place to make sure AI programs are learning fast enough to keep up with changing customer behaviour. Ethical concerns about data privacy, security, and consumer trust call for compliance and regulations to keep customer information safe. Technical expertise is vital for successful AI integration, so training up to an AI skilled workforce capable of optimising tools and platforms can feel like a major hurdle. Good AI in email marketing relies on a solid data foundation. It also relies on making the outputs of AI usable in the flow of work. What data streams can you realistically plug into your AI email marketing efforts? How can AI help with my email content? AI can improve your email content by helping with personalised messaging. A marketer for a clothing retailer, for example, can create one email that showcases product recommendations based on purchases and browsing behaviour. The marketer can then prompt AI to quickly create ten new versions of that original email with the intention of catering to different customer segments. Now that you have all that email content, you can use it to create one-to-one personalisation. Keep in mind that your AI strategy should be connected to your data and customer engagement strategy for best results. You can use the historical data from your customer relationship management (CRM) system to integrate dynamic content, offers, and recommendations for individual customers. Based on how your customer interacts, AI can then power the next best email to continue the journey. Your AI-powered email content now understands your specific customer preferences and business goals, making it easier for you to tailor your messaging. This type of content creation simultaneously streamlines and scales the process of customisation. How does AI in email marketing improve ROI over time? AI models are trained to deliver insights from every customer interaction. Their algorithms continuously adapt and learn with each interaction, so you’re able to get better results from your A/B email testing. AI analytics can help you reach across your entire email dataset – or, as it’s more commonly known to do now, bring in data from other sources in your customer data platform (CDP). It works by combining customer interactions across email, website, and purchases – analysing preferences and trends. With your customer base’s specific patterns, tendencies, and connections identified, you’re that much closer to being able to segment them. It’s easier to target communication with segmented audiences. AI-powered dynamic content can enhance customer engagement – surging the potential for click-through rates. By tailoring email content to customer preferences, including product recommendations and offers based on data, AI helps to make sure that emails resonate with the recipient. It can save you thousands of hours and result in conversions. What are the best practices for using AI in email marketing? There’s no doubt AI email marketing is the wave of the future. Here are some AI fundamentals you can’t afford to skip: Start with building an ethical, strategic, and technological foundation. This means implementing transparent data practices, ensuring data privacy compliance, and fostering a culture of ethical AI usage internally. It also means establishing clear goals and plans for how you want to apply new AI advances. Having a roadmap for what you wish to accomplish should always be the first part of the plan. A few other tips: Use embedded, no-code AI features like send-time optimisation, content selection, and subject line testing. Work up to creating multi-variant emails allowing for diverse content versions catered to specific customer segments. Move into the realm of real-time personalisation, tailoring email content in response to immediate customer behaviours and preferences. Then explore the intricacies of building custom AI models tailored to your unique business needs. Start with your email data as a foundation. Grow your customer profiles using a fuller picture of each customer across marketing, sales, commerce, and service. The customer insights you get from integrating data into a CDP will help you create more personalised emails. Apply what you know about AI capabilities for personalisation to segment your audience. Learn how to creatively prompt AI to generate fresh content. When you do your A/B email testing, don’t test multiple things at once. It’s important to make sure you have a control group. Leading AI email marketing platforms have tools to automate this process so you can test continuously. Employ a system for analytics, iteration, and retargeting. This system should be able to connect your email performance to web and app conversions as well as commerce and sales data to optimise business impact. This is a key requirement to look for in your AI email marketing software. What’s ahead for AI email marketing AI is the key to making sure people open and read your emails. From creating content to testing performance, AI can help identify the best ways to improve your email campaign. AI is transforming the entire marketing workflow. For all its newness, there are plenty of lingering questions about how AI email marketing will improve and what new capabilities marketers can expect to see. Think about AI as a combination of a supportive friend and personal assistant — a tool that helps you put your big-picture goals in focus. AI can help test your email send times and content selection, including catchy subject lines, images, and colours. It can take the guesswork out of how to connect with your customers by helping deliver personalisation at scale. AI can help you move faster. Right now, pushing out more content is a lengthy process that requires multiple layers of approval. With AI at your side, it’s likely we will see a real decrease in turnaround time. In addition to automating manual tasks, AI will continue to help drive marketers toward more empathetic and thoughtful content. In this sense, AI can help you be more efficient and become better at what you do. These days, marketers are more focused on first-party data — information gained directly from the customer. Instead of relying on old systems like open-data exchanges to buy audience data, they are modernising the way they build first-party data assets through the lens of user consent. Using AI in email marketing is essential for acquiring that precious data. As data and AI become more integrated, the growing importance of trust in email cannot be over emphasised. We’re also seeing a reimagining of the creative process, with generative AI reaching a point where it can create 1:1 personalisation for every email – and not just for customer segments. In the next two to five years, marketers using AI will be furthering businesses to make better decisions in their email marketing campaigns. Most of the campaign process, from lead generation to final customer message, will be led by marketers with strong knowledge of how AI works.

Everything. Everywhere. All at once. Yes, it’s an Oscar-winning movie, but it also perfectly describes the impact artificial intelligence (AI) is having on businesses, including the job market. The 360 view According to McKinsey, generative AI has the potential to add between $2.6 trillion and $4.4 trillion in value to the global economy across all industries, including banking, retail, high tech, healthcare, and life sciences. It will affect vocations such as customer operations, marketing and sales, software engineering, and research and development. And while there’s been much fear around AI taking our jobs away, the new technology will, in fact, give rise to myriad new jobs for human beings. For example, high-paying roles like prompt engineer —essentially, a master of the art of crafting prompts for GPT interfaces —and AI product manager are currently trending on popular job search sites. According to a recent Salesforce-sponsored IDC white paper of 500 organisations using AI-powered solutions, the next 12 months will also see a sharp increase in hiring for data architects, AI ethicists, and AI solutions architects. That same report predicts 11.6 million new jobs will be created within the LIKE.TG ecosystem alone over the next six years.* What you can do now AI needs people in control in order for it to work properly within our society. And members of the workforce, like you, have the opportunity now to hone existing skills across various industries and learn new skills to grow with the economy. “The exciting thing about these tools is they’re nascent and they’re largely democratised,” said David Berthy, senior director of LIKE.TG Futures. “So, if people have the volition, they can go out and learn how to increase their own value.” Platforms such as LIKE.TG’s Trailhead, Coursera, LinkedIn Learning, and Udemy all offer free and paid certification courses for in-demand AI-related skills. AI will eliminate repetition and create new skills and roles Let’s start by clearing something up: Yes, AI will likely eliminate repetitive job functions — scheduling social media posts, sifting through resumes, poring over data, answering basic customer service questions, sending follow-up emails — but that will free up people to be more strategic, creative, and productive in their current roles. People will now have time to, well, be people. If you’re in sales or customer service, you can now devote more time to interacting with customers to create even better relationships. In marketing? You can spend more focused time on strategic thinking or creative projects. And if you work in the legal or healthcare fields, AI can help analyse and research contracts or help interpret MRIs and X-rays, respectively. Not all new AI jobs will be as obvious as those in engineering or data-related fields. Jobs in healthcare, finance, graphic design, and more will evolve thanks to the assistance of smart AI. “AI will be sort of a background to everything,” said Chris Poole, AI technical consulting lead in LIKE.TG’s global AI practice in professional services. “I think it’s going to be very interesting to see.” Get articles selected just for you, in your inbox Sign up now How generative AI is creating new careers — 12 roles to consider What’s new and interesting to the AI curious? Here are a dozen jobs to look out for — some that currently exist (if only in early iterations), and some that Berthy envisions existing in the near term. Is one in your future? Prompt engineer Prompt engineers excel at writing prompts for AI tools, like a GPT product or chatbot, to get the most accurate or desired results. Some refer to prompt engineering as “AI whispering” because you’re essentially guiding the generative AI product to give you a creative solution to your question or prompt. AI trainer An AI trainer works behind the scenes to make sure an AI algorithm does what it’s supposed to do. The trainer prepares heavy data sets to teach chatbots how to think and interact with user inputs so the AI responds with more natural-sounding human language. Trainers also fine-tune the data and systems to achieve proper outcomes. Simply put: “AI trainers teach AI systems how to think, interact, and be genuinely useful,” according to Boost.AI. AI learning designer As AI technology rapidly grows and changes, organisations will need people to optimise personal learning at scale, Berthy said. Not only will these roles help train people on AI systems and how to work alongside AI copilots, but they’ll also refine the ways in which people learn. “Companies that have better versions of [learning systems] will be better equipped to adapt these new technologies,” Berthy said. AI instructor As companies continue to implement AI technologies, someone will need to teach employees how to use them. AI instructors teach people the skills they’ll need to advance their careers, whether directly or indirectly involved with AI. They’re responsible for developing curriculum and teaching methods, leading hands-on classes and lectures, and more around AI education. Sentiment analyser Even though it can understand and interpret human language, AI is not human and does not have feelings. It doesn’t understand nuance and it can’t interpret emotion. That’s where a sentiment analyser comes in. They work with a sentiment analysis program to determine whether data pulled off the internet, like social media comments or product feedback, is positive, negative, or neutral, and to identify its emotional tone. Stitcher A stitcher is a generalist who uses AI to combine a range of skills held by multiple roles into one role or workflow. For example, this job will use AI to more quickly stitch together modular software tools into workflows that create unique value for customers, Berthy said. So, instead of having multiple people work on a project from design and storytelling to engineering and overall business implementation, the stitcher will tackle it all by using AI. Interpersonal coach This role will help people living and working in a digital-first world and working with AI to gain basic interpersonal skills like relational intelligence, empathy, active listening, and connecting in face-to-face interactions. It’s similar to a business coach, but focused on helping people who work behind a screen or mostly with machines. Workflow optimiser This role will use data and intelligence to have a bird’s-eye view across a company, and determine where AI could help people be more productive. This person will use AI to review how people and teams work, and highlight productivity gaps to increase overall efficiency. AI compliance manager As AI regulations continue to get refined globally, an AI compliance manager ensures an organisation’s AI processes adhere to relevant regulations, ethical standards, and industry guidelines. They make sure data-handling practices align with privacy laws and also mitigate AI’s potential impact on an organisation. AI security manager If AI technology gets into the wrong hands, it can quickly become dangerous. The AI security manager will be an important role within an organisation to ensure AI systems are used properly. They’ll also protect against vulnerabilities and security threats. Chief AI officer The newest role to enter the C-suite, the CAIO sets the overall AI strategy for an organisation. This includes the responsible and ethical design, development, and implementation of all AI tech produced by the company. Chief data and analytics officer (CDAO) The chief data and analytics officer oversees all things related to data and analytics in a company. Sometimes this role is shared by two people, one as chief data officer (CDO) and the other as chief analytics officer (CAO). How to prepare for new AI careers With all of these new AI jobs emerging, it’s time to embrace training and start playing around with the free tools at your disposal. “Don’t be afraid of the tools,” Poole said. “Look at it as a helper that can improve your way of life and your work.” Online learning platforms are making it easier to access these tools and skill up, said Aleksandra Radovanovic, senior product manager of business technology at Okta, a cloud-based identity management service that provides single sign-on solutions for businesses. She said to identify relevant skills and look for online courses to help build them. Trying to gain practical experience, even through certification courses, will also help. “The people who embrace the changes and the opportunity they create and increase their own learning are the ones who will have the brightest futures,” Berthy said. “You can capture that ethos and give people some hope because there’s enormous opportunity to up your own marketability.” *LIKE.TG Economic Impact: LIKE.TG AI-Powered Cloud Solutions Will Generate $948 Billion in New Revenues for Customers by 2028 (doc #US51404923, December 2023)

For anyone who has seen films like Star Wars, Metropolis, or, more recently, TikToks of Beyoncé’s latest tour, the concepts of robots, cyborgs, and other sentient machines have been around since at least the turn of the 20th century. While these pieces capture both the most fantastic and menacing ideas about this kind of technology, reality has been catching up since the 1950s, when Alan Turing published his paper, “Computing Machinery and Intelligence.” Artificial intelligence isn’t new. What is new, though, is how accessible AI is. When Turing first asked, “Can machines think?” he was met with so many barriers, including computing limitations and cost. A better question might have been, “Can we even afford to find out?” Now, there’s been such an explosion of AI offerings that 94% of business leaders see AI as essential to their work. But, with better access and more choices, AI implementation has become less an abstract vision for the future, and more akin to a New Year’s resolution — you know you should do it, if only you could get started. Which brings us to today’s big question: How can your business get started on an AI journey, and in a way that reduces costs, increases value for your customers, protects your data, and doesn’t leave your people behind? Where to start your AI implementation In my role leading LIKE.TG Professional Services, I speak with business leaders around the world who are facing this challenging question. AI in its current state, with its myriad uses and capabilities, lends itself perfectly to my team’s advisory work. When AI can be used for anything from sales to customer service to marketing to backend code development, choosing where to start can feel overwhelming. So, before we help you build the roadmaps for your AI journeys, step one is finding what fits your goals. Where will AI add value? A journey needs a destination. From that outcome, we calculate a roadmap using the best route to get to a successful AI implementation. This means thinking about the end goal first (the business version of “manifesting”). Typically, goals for AI implementation fall into one of three categories: Increasing revenue where AI unveils new market opportunities and streamlines operations Reducing costs where, through automation and process optimisation, AI reduces operational costs and enhances overall business efficiency Driving customer loyalty where AI creates personalised experiences to help customers feel valued and understood, which builds and maintains loyalty, and in turn, translates to increased revenue and reduced costs Once you figure out which of these goals aligns with your current business needs, we can get on the road(map). Recalibrate expectations Knowing the destination doesn’t make the journey predictable. The technology may be more widespread now, but AI can still surprise. Consider the example of a retail company with a disastrous customer service call centre. Their high abandonment rates and low net promoter scores (NPS) indicate terrible customer satisfaction. Initially, they might focus an AI solution on the front end, like a customer service chatbot. But, on deeper exploration, they realised a better understanding of customer needs will provide a bigger benefit. AI can play a critical role here in customer service automation and also in analysing feedback and purchasing patterns. But getting to this shift in perspective requires stepping back, looking at your business process, and finding inefficiencies or potential improvements. AI can emerge as more than a singular tool and instead as a strategic force, combining the best of computer science and data to reach business goals. Make the AI business case As the business goals begin to come into focus, an important strategic checkpoint is clarifying the reasoning and justification for the AI implementation. When rolling out any big project or new technology, there should be a hard look at benefits, disadvantages, cost, and risk. But, since AI has the potential to be a more transformative technology than others, and comes in many different shapes and sizes, it’s even more important to take this disciplined approach. Think of this as the last exit before the highway. Prioritise trust Again and again, one of the top concerns about AI is trust. Luckily, that’s our #1 value at LIKE.TG. That means we strongly believe in addressing concerns about data security, privacy, ethical use of AI, and trust right at the onset. Transparency and clear communication about responsible AI practices are crucial. The most common questions that I’ve encountered are: “Where’s my data going again?” Understanding the flow and storage of data is fundamental. Once the data is collected and stored, it needs to be managed with the utmost care and respect for privacy. “Who are you sharing it with?” This is the heart of data-sharing policies. Data sharing should be governed by strict protocols and transparency, ensuring that information is only shared where necessary and under stringent conditions. “Is it protected?” The security of all data is vital. Implementing robust security measures to safeguard data against breaches and unauthorised access is a top priority in any AI implementation. These valid concerns echo the early days of software as a service (SaaS), when businesses were initially hesitant to embrace that new technology. We’ve since seen that SaaS has transformed the landscape of software delivery and usage. AI has the potential to have an even greater impact. But this can’t happen if we don’t address issues up front and create trust. Shape your company’s AI plan with a (human) AI Coach When the Turing Test was first introduced in 1950, it was originally called “the imitation game” —the idea being if a computer could successfully imitate a human, then the answer to the question, “Can a machine think?” would be a definitive, “Yes!” Though it’s up for debate whether the Turing Test is still a useful measurement, the fact that it’s being debated at all means we’re not quite ready to go human-free. Readiness for AI implementation transcends technology. There needs to be a comprehensive evaluation of AI’s potential business value, organisational data quality, the trustworthiness and security of the AI solution, and an organisation’s adaptability — not to mention preparing for a new way of working. This is where LIKE.TG Professional Services’ trusted advisers come in. We bring the specialists and technology together with our AI Coach program. Through this process, we evaluate a company’s overall readiness, including internal skills and expertise, existing technology infrastructure, data preparedness, governance, and ultimately build the roadmap for long-term success. For now, the human part of AI might be the most important. Make it the experts at LIKE.TG Professional Services. Get articles selected just for you, in your inbox Sign up now

What’s next in first-party advertising? Our community came together in December at World Tour New York to learn about the latest marketing innovations and how to reach customers better. Here’s the scoop on ourlatest releases and updates— and what they can do for your business. Companies are now prioritising first-party customer data in their marketing campaigns. However, changes in data privacy laws, not to mention the fast-approaching cookieless future, make it tough for companies to meet these expectations without the right tools to collect and capitalise on that first-party data. In December, we announced new Data Cloud integrations with Google Display Video 360 and LinkedIn. These integrations help companies connect their first-party data and execute automated, personalised advertising campaigns. Let’s take a look at how these innovations will help your advertising efforts. What is first-party data advertising? First-party data advertising uses information collected directly from your company’s customers or users to personalise and target advertising efforts. This valuable data, comprising customer preferences and interactions, enables your business to create highly tailored campaigns, enhancing the relevance of your messaging. In contrast to third-party data, customers consent to you using their first-party data, which your company directly manages. This leads to customer trust, as well as compliance with privacy regulations.By letting your customers tell you exactly who they are and what they want, you unlock more effective retargeting, personalised content creation, and an overall improved customer experience. What our new Data Cloud updates mean for your business As you explore how first-party data advertising can help your company, we want to make sure we tell you both the what and the why behind these new innovations. So what do these Marketing Cloud updates mean for you? You can improve the way you interact with your customers and your marketing systems, to do things like: Personalise at scale: With these new Data Cloud integrations, you can personalise advertising using a complete customer profile that unifies first-party data from customers across marketing, commerce, sales, service, or any touchpoint. Increase efficiency: The new integrationshelp you deliver the right message to the right person at the right time. You can do this with rapidly updated segment memberships, near real-time data sharing with advertising partners, and suppressing users that have already purchased or have open Service Cloud cases. Segment and activate quickly: You can now compress the weeks it takes to build out segments with legacy SQL-based tools into minutes. Drag-and-drop and natural language generative AI interfaces (coming February ‘24) help you create, test, and build segments quickly. Then you can immediately activate the new integrations with Google Display Video 360 and LinkedIn alongside your other channels like email, SMS, web, app, and connected devices. This results in a more personalised experience for your customer on their preferred channel. Build trust with your customers: With these updates, it’s easier to build trust through messages that are more relevant to your customers and compliant with today’s regulations. The new Data Cloud integrations allow you to launch ad campaigns that are more relevant(using first-party data) and efficient, through the power of automation. Make your data work for you Want to see how Data Cloud can get you closer to your customers? Start with a quick lesson today on Trailhead, the free online learning platform from LIKE.TG. Let’s get started +300 points Module Data Cloud-Driven Interactions in Marketing Cloud What are some new things marketers can do with Data Cloud? We’ve covered how your business can benefit from these innovations. Now let’s look at some specific ways you can use them to improve your results: Create connected advertising experiences across display, video, TV, audio, and other channels with Google Display Video 360. You can improve customer loyalty with engaging and seamless advertising by using unified customer profiles from Data Cloud. This allows you to deliver personalised ads and campaign measurement across multiple channels. For example, a media brand can increase loyal customers and retain at-risk subscribers by using AI insights to engage audiences in Google Ads campaigns, targeting those who are most likely to upgrade or churn with relevant messages and offers. Target a network of over 1 billion active professionals on LinkedIn based on job title, function, industry, and more. You can use first-party data, combined with AI-powered product interest scoring from Marketing Cloud, Sales Cloud, and Service Cloud, and product usage data from your own apps to reach more customers. For example, with Data Cloud, a tech company can create an end-to-end program to increase awareness and promote relevant upsell and cross-sell conversion opportunities to grow their sales pipeline. Greater efficiency, effectiveness, and personalisation are top of mind for every business looking to improve their advertising strategy. Capitalising on trusted first-party data within your advertising is the key to delivering the experiences customers want — and the campaign performance your company needs The new first-party data advertising integrations with Google Display Video 360 and LinkedIn are expected to be generally available in Q1 2024.

We’re living in a pivotal era where digital transformation is not just an option but a necessity. LIKE.TG continues to pave the way for AI innovation, bringing together CRM with trusted AI and data on one integrated platform, so our customers are prepared to lead in the AI revolution. World Tour Essentials Singapore will help you unlock your AI potential with the transformative capabilities of our latest Data Cloud and Einstein innovations. Register and join us on Wednesday 8 May, 8:00 a.m. – 5:30 p.m. at the Marina Bay Sands Convention Centre to learn from customer Trailblazers, visionary AI experts and thought leaders. Read on to catch a glimpse of the sessions you can see. Everyone’s an Einstein with CRM + AI + Data + Trust At the heart of World Tour Essentials Singapore is our main keynote session featuring the inspiring Trailblazer FairPrice Group. Here, we’ll unveil how LIKE.TG is revolutionising CRM by integrating it with AI, data, and trust. This session will not only provide insights into our latest AI innovations but also demonstrate how these technologies are accessible to all – empowering every business to make smarter, data-driven decisions. Stick around after the keynote for the ‘Unlock All of Your Trapped Data with Data Cloud’ session to learn how Data Cloud is designed to enhance, not replace, systems like data warehouses and data lakes, and how it helps solve for the “last mile” of data activation. Explore the full agenda here and mark your calendar. AI for Everyone: From Sales to Service to Marketing Dive into the transformative power of AI across all business functions with our comprehensive session lineup. Discover how AI is reshaping sales with smarter insights for lead scoring and customer engagement, transforming service by predicting customer needs and automating responses, and helping marketing teams personalise interactions and craft powerful campaigns at scale. Featured sessions will include case studies from leading organisations including Philippine Airlines and Siam Commercial Bank, showcasing their AI success stories and helping you build a business case for AI in your sector. Here are some of the industry-specific sessions that you won’t want to miss: LIKE.TG for Financial Services: Empower Customer Success Learn how to leverage the #1 Trusted AI CRM to unlock financial insights that deliver better outcomes, responsibly, for your clients, members, and policyholders. Hear from Siam Commercial Bank on how they’re embracing digital transformation to deliver personalised customer experiences. Revolutionise Marketing Excellence with Marketing Cloud and AI Learn about the latest innovations in Marketing Cloud and how to harness the power of AI. Hear our customer Trailblazer discuss how to deliver personalised experiences and drive business growth with data and AI. The Future of Sales: Supercharge Selling with Trusted AI Are you looking for the smartest path forward in today’s fast-changing environment? AI can give sellers superpowers to drive efficient growth. Join us to learn how your entire sales organisation can boost productivity, leverage data, and increase revenue with the #1 AI CRM for sales. Reimagine Service with Trusted AI Learn how to activate AI to scale service, increase team productivity and save costs using the #1 AI platform for service. And hear from Trailblazer Mark Anthony Munsayac, Head of Customer Experience at Philippine Airlines on how they are redefining customer service and engagement with LIKE.TG. Trailblazers Who Are Blazing Ahead with AI Hear from inspiring Trailblazers like FairPrice Group, Siam Commercial Bank, Philippine Airlines, and Grab, and see how they’ve successfully integrated LIKE.TG’s AI-powered CRM into their operations. Their stories will provide a blueprint for transforming your business with AI and help you understand the tangible benefits of CRM + Data + AI + Trust. Notably, the ‘Transform How Your Teams Get Work Done with the Einstein 1 Platform in Slack’ session with Southeast Asian super-app company Grab will showcase how it has increased productivity by bringing its operations into the place where its Grabbers work – Slack. Attend this session in the keynote room and see how Slack can empower your teams, putting customer data and insights at their fingertips. Putting AI in the Hands of Everyone with Slack With a full day of sessions and demos at the Slack Theatre, you can see all the ways LIKE.TG is making AI more accessible than ever with Slack. Slack puts AI tools directly into the flow of work, where real-time data and insights can lead to immediate and impactful decisions. Sessions will demonstrate how integrating LIKE.TG with Slack allows teams to act quickly, collaborate efficiently, and leverage AI-driven data without ever leaving the platform where they work. Make sure you review the agenda and mark your calendar to attend sessions including: Put AI into the Hands of Everyone with Slack AI Bring Your CRM Data Right into the Flow of Work with Sales Cloud and Slack Discover How Automation Can Transform Your Work with Slack Delight Customers and Drive Service Team Efficiency with Slack Super Demo: Unlock Sales Productivity with Team Selling in Slack Explore the Latest in AI-Powered CRM at World Tour Essentials Singapore As the digital landscape continues to evolve it’s essential to stay up-to-date with the latest tools and technologies at your fingertips. At World Tour Essentials Singapore, you’ll discover how integrating AI with CRM is not just enhancing business processes but is also essential for driving growth and maintaining a competitive edge. Don’t miss this opportunity to see firsthand how you can transform your customer experiences with trusted AI. Register to attend World Tour Essentials Singapore, Wednesday 8 May at the Marina Bay Sands Convention Centre and propel your business forward with CRM + AI + Data + Trust.

Customer acquisition is the lifeblood of any business. It’s the process of bringing in new customers and growing your business. Without a steady stream of new customers, your business will eventually stagnate and die. In this comprehensive guide, we’ll cover everything you need to know about an effective customer acquisition strategy, from the basics to advanced strategies. We’ll discuss what customer acquisition is, why it’s important, and how to create an effective and sustainable customer acquisition strategy. We’ll also explore the different channels you can use to acquire customers and how to measure your success. By the end of this guide, you’ll have the knowledge and tools you need to develop a successful customer acquisition strategy for your business. What is customer acquisition? Customer acquisition is the lifeblood of any business. Simply put, it is the process of identifying and acquiring new customers. As the first stage in the customer lifecycle, a customer acquisition plan involves creating awareness of your product or service, generating leads, and converting those leads into customers. Every business needs a steady stream of new customers to survive and grow. Without a consistent influx of fresh faces, your business will eventually stagnate and eventually cease to exist. Customer acquisition is an ongoing process that requires businesses to constantly be on the lookout for new ways to reach and engage with potential customers. Customer Acquisition and the Customer Lifecycle Customer acquisition is the first stage in the customer lifecycle, which is the journey a customer takes from the moment they become aware of your business until they become a loyal customer. The customer lifecycle can be divided into four main stages: 1. Awareness: This is the stage where potential customers first become aware of your online business, and what you have to offer. 2. Consideration: This is the stage where potential customers are considering your product or service as a solution to their needs. 3. Conversion: This is the stage where potential customers make the decision to purchase your product or service. 4. Retention: This is the stage where you focus on keeping your customers happy and satisfied so that they continue to do business with you. Customer acquisition is the key to moving potential customers through the customer lifecycle and ultimately turning them into loyal customers. Why is customer acquisition so essential? Customer acquisition holds significant value for enterprises across all stages and sizes. This process enables your company to: Generate revenue to cover expenses, compensate staff, and fund further expansion, and Demonstrate growth and momentum to external stakeholders like investors, partners, and key influencers. The ability to consistently draw in and secure new clients is an essential for maintaining the vitality and expansion of businesses, ensuring investor confidence in the process. What is the purpose of customer acquisition? Customer acquisition is the process of identifying and acquiring new customers. It is an important part of any business’s growth strategy and can have several key benefits for a business. Some of the main benefits of paid customer acquisition strategies include: – Increasing the number of satisfied customers a business has: This can lead to increased revenue and profit, as well as a larger customer base to which the business can market its products or services. – Increasing revenue and profit: Acquiring new customers can directly increase a business’s revenue and profit. This is because new customers can purchase products or services from the business, increasing the business’s overall sales. – Building brand awareness and customer loyalty: Acquiring new customers can help build brand awareness and loyalty. This is because when new customers have a positive experience with a business, they are more likely to return for future purchases and become loyal customers. – Entering new markets or expanding into new customer segments: Acquiring new customers can help a business enter new markets or expand into new customer segments. This can help the business grow its customer base and reach new customers who may not have been aware of the business before. – Increasing market share: Acquiring new customers can help a business increase its market share. This is because when a business acquires new customers, it takes away market share from its competitors. What is acquisition marketing? Acquisition marketing focuses on attracting new customers or clients to your business. It encompasses various strategies and channels aimed at generating leads and converting them into paying customers. The primary goal of any customer acquisition channel or marketing is to increase your paying customer base and boost revenue. One of the key elements of acquisition marketing is lead generation. This involves identifying potential customers who have shown interest in your products or services. This can be done through various channels such as online advertising, social media marketing, content marketing, search engine optimisation (SEO), and email marketing. By creating engaging and relevant content, you can attract potential customers and encourage them to provide their contact information, thus becoming leads. Once you have generated leads, the next step is to nurture them and convert them into customers. This can be done through personalised email campaigns, follow-up phone calls, or providing prospective customers with additional resources and information to help them make informed decisions. By building relationships and trust with potential customers, you can increase the likelihood of converting them into paying customers. Acquisition marketing involves ongoing marketing efforts, to continuously attract and acquire new customers. It requires a combination of effective strategies, understanding your target audience, using customer acquisition techniques and analysing customer behaviour. By implementing a well-executed acquisition marketing plan, you can expand your customer base, grow your business, and achieve long-term success. The customer acquisition funnel is a model that businesses use to understand and track the customer journey from awareness to purchase. The funnel is divided into five stages: awareness, interest, consideration, decision, and retention. At the top of the marketing funnel is the awareness stage, where potential customers first become aware of your brand or product. This can happen through various channels, such as advertising, social media, or word-of-mouth. The goal of the awareness stage is to generate interest and motivate potential customers to move down the funnel. The next stage is the interest stage, where potential customers start to show interest in your product or service. They may visit your website, read your blog, or follow you on your social media channels. The goal of the interest stage is to engage potential customers and provide them with more information about your offering. Once potential customers are interested in your product, they move into the consideration stage. At this stage, they are comparing different options and considering whether or not to make a purchase. The goal of the consideration stage is to differentiate your product from the competition and convince potential customers that your offering is the best solution for their needs. The fourth stage of the funnel is the decision stage, where potential customers make a decision about whether or not to purchase your product. This is the critical stage of the funnel, as it is where you convert leads into customers. The goal of the decision stage is to make it easy for potential customers to purchase your product and provide them with the information they need to make an informed decision. The final stage of the funnel is the retention stage, where you focus on retaining existing customers and building long-term relationships. The goal of the retention stage is to ensure that customers are satisfied with your product and continue to do business with you. By understanding the customer acquisition funnel, you can develop targeted marketing and sales strategies to move potential customers through each stage of the funnel and increase your chances of converting them into customers. Acquisition channels There are various other customer acquisition methods and channels that businesses can use to reach and acquire new customers. These channels include organic search, paid search, social media, email marketing, and content marketing. Organic search refers to the process of optimising a website so that it appears higher in search engine results pages (SERPs) for relevant keywords. This can be achieved by creating high-quality content, building backlinks, and improving the technical aspects of a website. By optimising for organic search, businesses can increase their visibility in search engines and attract more visitors to their website, leading to increased customer acquisition. Paid search involves using paid advertising to place ads at the top of SERPs for specific keywords. This can be an effective way to reach potential customers who are actively searching for products or services like yours. However, it is important to carefully manage paid search campaigns to ensure that you are getting a positive return on investment (ROI). Social media marketing strategy involves using social media platforms such as Facebook, Twitter, and Instagram to connect with potential customers and build relationships. By creating engaging content, running social media ads, and interacting with followers, businesses can use social media to generate leads and drive traffic to their website. Email marketing involves sending promotional emails to a list of subscribers. This can be an effective way to stay in touch with potential and existing customers, promote new products or services, and drive traffic to your website. However, it is important to follow best practices for email marketing, such as obtaining permission before sending emails and providing valuable content, to avoid alienating subscribers. Content marketing involves creating and distributing valuable, relevant, and consistent content to attract and retain a clearly defined audience. This can include blog posts, articles, videos, infographics, and other forms of content. By creating high-quality content that addresses the needs and interests of your target audience, you can build trust and credibility, generate leads, and ultimately acquire new customers. How to develop a customer acquisition strategies To develop a customer acquisition strategy, the first step is to identify your target audience. This involves understanding their needs, pain points, and demographics. This information can be gathered through market research, surveys, and analytics. Once you have a clear understanding of your target audience, you can develop a strategy to reach and acquire them. Setting clear goals and objectives is essential for any customer acquisition strategy. What do you want to achieve with your customer retention strategy? Do you want to increase brand awareness, generate leads, or drive sales? Once you know your goals, you can develop a plan to achieve them. Developing a customer journey map is a helpful tool for visualising the customer experience from the initial touchpoint to the final purchase. This will help you identify any gaps or friction points in the customer journey and make improvements to optimise the process. Creating compelling content is essential for attracting and engaging potential customers. This content can take various forms, such as blog posts, videos, infographics, and social media posts. Ensure that your content is relevant to your target audience and provides value to them. Customer acquisition metrics Metrics are essential for measuring the success of your customer acquisition efforts. This section will discuss the key customer data metrics you should track, including customer acquisition cost (CAC), customer lifetime value (CLTV), customer churn rate, marketing qualified leads (MQLs), and sales qualified leads (SQLs). Customer acquisition cost (CAC) is the total cost of acquiring a new customer. This includes all costs associated with marketing, sales, and customer onboarding. CAC can be calculated by dividing the total cost of new customer acquisition by the number of new customers acquired. Customer lifetime value (CLTV) is the total amount of revenue that a customer is expected to generate over their lifetime. This can be calculated by multiplying the average customer value by the average customer lifespan. Customer churn rate is the percentage of customers who stop doing business with a company over a given period of time. This can be calculated by dividing the number of customers who churned by the total number of customers at the beginning of the period. Marketing qualified leads (MQLs) are potential customers who have shown interest in a company’s product or service but are not yet ready to make a purchase. MQLs can be generated through various marketing channels, such as website visits, email campaigns, and social media. Sales qualified leads (SQLs) are potential customers who have been identified as being ready to make a purchase. SQLs have typically been through the MQL stage and have expressed a strong interest in a company’s product or service. Tracking these customer acquisition metrics can help you measure the effectiveness of your customer acquisition efforts and make adjustments as needed. By optimising your customer acquisition process, you can reduce your CAC, increase your CLTV, and improve your overall customer acquisition ROI. 3 customer acquisition strategy examples Here are three examples of customer acquisition strategies that businesses can use to grow their customer base: 1. Paid advertising Paid advertising is one of the most direct ways to reach new customers. By using platforms like Google AdWords, Facebook Ads, and LinkedIn Ads, businesses can target potential customers with specific ads based on their interests, demographics, and online behaviour. Paid advertising can be an effective way to generate leads, drive traffic to a website, and increase brand awareness. 2. Referral programs Referral programs are a great way to incentivise existing customers to bring in new customers. By offering rewards or discounts to customers who refer new business, businesses can tap into the power of word-of-mouth marketing. Referral programs can be especially effective for businesses with a loyal customer base. 3. Partnerships and collaborations Partnering with other businesses can be a great way to reach new customers and expand your market reach. By collaborating with complementary businesses, businesses can cross-promote each other’s products or services and access new customer segments. Partnerships can also be a great way to gain credibility and build trust with potential customers. These are just a few examples of customer acquisition strategies that businesses can use to grow their customer base. By understanding the target audience, setting clear goals, and creating compelling content, businesses can successfully attract and acquire new customers. Common customer acquisition challenges and solutions There are several common challenges businesses face in their organic customer acquisition strategies. These include: – Competition: In today’s competitive business environment, there are numerous businesses competing for the attention of the same potential customers. This means businesses need to find ways to stand out from the competition and differentiate their products or services. – Lack of brand awareness: For new businesses or those with limited brand recognition, creating awareness of their products or services can be a significant challenge. – High customer acquisition costs: Acquiring new customers can be expensive, especially if businesses rely heavily on paid advertising or other marketing channels that require significant investment. – Long sales cycles: For some businesses, the sales cycle can be long and complex, which can make it difficult to convert leads into customers quickly. – Customer churn: Once businesses have acquired customers, they need to focus on retaining them and preventing churn. This can be challenging, especially in industries with high levels of competition. To overcome these challenges, businesses can implement various solutions, such as: – Developing a strong value proposition: Clearly articulating the unique value proposition of a business’s products or services can help differentiate it from competitors and attract potential customers. – Investing in brand building: Building brand awareness through effective marketing and communication strategies can help businesses reach a wider audience and establish a strong reputation. – Optimising customer acquisition channels: Analysing and optimising the effectiveness of different customer acquisition channels can help businesses allocate their resources more efficiently and reduce customer acquisition and marketing costs further. – Streamlining the sales process: By simplifying the sales process and removing unnecessary steps, businesses can shorten the sales cycle and improve conversion rates. – Implementing customer retention strategies: Developing and implementing customer retention strategies, such as loyalty programs and excellent customer service, can help businesses measure customer acquisition, reduce churn and increase customer lifetime value. How LIKE.TG can help with customer acquisition LIKE.TG is a powerful customer relationship management (CRM) platform that can help businesses of all sizes acquire new customers. It provides a complete view of your customers across all touchpoints and channels, so you can understand their needs and preferences and tailor your marketing and sales efforts accordingly. With LIKE.TG, you can automate your marketing campaigns, nurture leads with personalised messaging, and manage your sales process from start to finish. You can also build a seamless omnichannel shopping experience for your customers, so they can easily purchase from you no matter how they choose to interact with your business. In addition, LIKE.TG provides robust analytics and reporting tools, so you can track your customer acquisition progress and make informed decisions about your marketing and sales strategies. By using LIKE.TG, you can streamline and improve your customer acquisition and process and grow your business faster. Here are some more customers’ specific examples of how LIKE.TG has helped businesses acquire new customers: – A leading technology company used LIKE.TG to create a personalised customer journey for each of its website visitors. By understanding the interests and needs of each visitor, the company was able to target them with relevant content and offers, which resulted in a 30% increase in conversions. – A major retailer used LIKE.TG to automate its email marketing campaigns. By sending targeted emails to its customers, the retailer was able to increase its open rates by 20% and its click-through rates by 15%. – A small business used LIKE.TG to manage its sales process. By tracking leads and opportunities, the business was able to increase its sales by 25%. These are just a few examples of the many ways that LIKE.TG can help businesses acquire new customers. If you’re looking for a CRM platform that can help you grow your business, LIKE.TG is a great option.

Small businesses often need to do more with less, so it’s critical to have the right tools. The right small business marketing tools can help your company be more efficient, and accelerate growth. If your small business marketing team wants to generate more quality leads, these tips can help you connect with customers more effectively and score big results. 1. Use Google Analytics to optimise your website Website optimisation is a priority for every marketer and a key marketing tool for any SMB, but especially for those that sell, advertise, and target online. Use Google Analytics to generate free data and performance reports, including: A conversions path report to identify tests. A path report shows you the route your visitors take on your website before they convert, including the path that yields the highest conversion rate. Is it “Homepage>product page>trial” or “Homepage>pricing>trial”? Pay attention to the path and use this insight to design tests and experiments to boost conversion rate. A basic channel report to track conversion. Detail how each marketing channel is driving conversions. Are your social ads driving any revenue? How is SEM performing against SEO? You can adjust your investments/budget based on the returns from your best performing channels. A funnel report to identify bottlenecks. This shows you where conversions drop off. Is most of your traffic going from the homepage to your lead form, then exiting? Maybe your lead form has too many fields or the content doesn’t resonate. (If so, change up your form to make it more customer friendly.) A cohort report to track how segments perform over time. This report looks at which segment converts at the highest rate. In a cohort report you might learn that, say, if you retarget a user within 15 minutes of them leaving your site, they’re more likely to convert. Get articles selected just for you, in your inbox Sign up now 2. Lean on marketing automation to sync sales and marketing data To get the most out of your marketing, information and tracking must get to sales. This is why marketing automation is one of the most important small business marketing tools. As a marketer, you need to know your customer — all that data is already in your customer relationship management (CRM) system. Access to it gives marketing and sales teams the information they need to create relevant and engaging campaigns. With integrated systems, you can put the customer’s needs first and provide relevant marketing messages to help nurture your leads. Eager for specifics? Here are some of the benefits outlined in our SMB Guide to Sales and Marketing Alignment: Increase the quality of leads available to sales reps, and prevent leads from slipping through the cracks. Ensure that sales reps can effectively engage prospects with the right content at the right time. Evolve prospecting from a solo act to a collaboration with marketers who have the expertise and reach to find more qualified prospects in half the time. Create a steady stream of sales leads. Close more sales in less time, build a more efficient sales funnel, enjoy shorter sales cycles, and ultimately, drive more revenue. 3. Use email marketing software to nurture customers and drive conversion The State of Marketing report shared that 76% of marketers use email to communicate with their customers. But many of us communicate with customers via email only to have the customer delete it. How do you avoid that in the future? Use a combination of technology and technique in your small business marketing tools: Build campaigns triggered by behavioural and intent actions. Are most people dropping off from a page on your website? Did someone get stuck in a trial? What triggered their first purchase? Talk to the sales and product teams about identifying high-converting actions. Rebuild your nurture email to drive the conversions you want. The anatomy of an effective marketing email is not just good copy. You need to couple that with data — someone’s search history, their trial experience, their titles, the size of the company they work for — to make your emails personalised. Would you rather receive an abandoned cart email offering you a discount? Or a generic email promoting a product you don’t care about? Relevance will improve your open rates, click rate, and ultimately, conversions. 4. Boost organic traffic with SEO small business marketing tools Using the right words is the key difference between gaining and losing organic website traffic. And traffic can make or break your small business online marketing. For example, prospects might search for “customer service tools” versus the industry term “help desk solutions.” Your company’s internal language around a product or service is most likely not the same as the words and phrases millions of people use to search. With keyword research, you’ll be able to understand all the ways prospects search to get to your (or your competitor’s) website. The best part? Determining the right search engine optimisation (SEO) terms doesn’t have to be costly. Diagnostic tools like Moz and SEMrush can pay for themselves over time. Use these tips to see big changes in your traffic: Check your website authority (the “strength” of your domain). Jot down what your domain authority is before optimisation. Use that as your baseline, and then check your domain authority again in three to six months. The score should go up and you should see your optimisations come to fruition. Benchmark this score against your competitors. Boost your domain authority with on-page optimisation. This includes a number of housekeeping things like adding keywords in title tags and meta descriptions, creating an intuitive site structure, having an easy navigation bar, and asking other sites to link back to yours whenever and wherever it’s relevant. Test your site speed and load time. Google search tools honour sites that are mobile optimised and load quickly. You can test your site for this diagnostic, and then optimise your site for speed as necessary. Above all, content should be relevant, deliver high value, and resonate with your prospects and customers. Campaigns that move prospects through the funnel with personalised email nurture campaigns, digital content, and virtual events are a great way to engage. 5. Improve collaboration with Slack Communicating and collaborating in real time is key for teams generating new leads. Slack connects everyone you work with — both those in your business and your key partners and customers. Slack also supports the way people naturally work together — in real time or not, in-person and remote, structured or informal. These features can help drive productivity for your business: Slack channels – to keep important information organised and at your fingertips Huddles – communicate in real time and eliminate unnecessary meetings Schedule messages – prep messages to post at a future date or time, so you can keep work flowing even when you’re away from your computer Workflows – automate tasks you do every day so you can work more efficiently Get your small business marketing tools up to par, and get the help you need to find new business and connect with customers.

As technology advances, the state of customer service changes along with it. Customers expect companies to adapt to their needs, and technologies like generative AI are playing a major role in meeting those evolving expectations. Here are five customer service trends to keep on your radar as you prepare for the future of customer service, based on new data from the “State of Service” report.1. Service organisations are now revenue generators — not cost centresCompanies are looking for new ways to drive growth and protect their margins, and many see service as a prime opportunity. In fact, 85% of decision makers say service is expected to contribute a larger share of revenue this year.So how can you take action? Our research shows that leading organisations are actively tracking key performance indicators (KPIs) that are linked to tangible business outcomes. In fact, the share of service organisations tracking revenue generation has nearly doubled since 2018, from 51% to an astonishing 91%. The share tracking customer retention rose by 29 percentage points over the same period. If those customer service analytics aren’t already on your radar, it’s time to make some changes — otherwise you risk getting left behind. (back to top)2. Self-service is a clear competitive advantageSelf-service helps customers resolve simple issues, freeing agents to spend more time on high-complexity, high-value interactions.Our latest research shows that high-performing organisations are much more likely than underperformers to provide self-service tools like knowledge-powered help centres, customer self-service portals, and chatbots powered by AI. When customers can interact with a chatbot to answer a question or use a guided journey to start a return, live agents have the time they need to manage more complicated requests. That’s critically important for the 69% of agents who report difficulty balancing speed and quality. (back to top)3. Connected data enables a better customer experienceMany organisations keep data in different silos or applications, so it’s difficult to get a complete view of the customer across all channels.Bringing customer data together is all about creating an end-to-end view of the entire customer journey. This way, you’ll have a continuous feedback loop between sales, service, and marketing, keeping everyone on the same page. Maybe that’s why 82% of high-performing organisations use the same customer relationship management (CRM) platform across all departments — up from 62% just two years ago.The stakes are high: 92% of analytics and IT leaders say the need for trustworthy data is greater than ever. That’s why privacy, security, and trust have already become a major competitive advantage in the evolving AI landscape.As companies try to stay one step ahead of these and other customer service trends, leading organisations are adopting programs that prevent large-language models (LLMs) from retaining sensitive customer data. More organisations are training their own domain-specific models to access a secure AI cloud while storing data on their own infrastructure. These efforts are essential to preserving customers’ loyalty and trust in the years to come.(back to top)4. Conversational AI is taking proactive, personalised service to the next levelWhen it comes to meeting customers’ sky-high expectations, proactive service is more important than ever. “It’s essential to proactively resolve issues before they have any significant business impact,” says Jules O’Donnell, LIKE.TG administration manager at Quickbase. “This means keeping up with technology solutions to scale service and meet customers where they are.”Our latest research backs that up: 95% of decision makers at organisations with AI report cost and time savings, and 92% say generative AI helps them deliver better customer service. It should come as no surprise, then, that 83% of decision makers plan to increase their AI investment over the next year, while only 6% say they have no plans for the technology whatsoever.The introduction of conversational AI assistants is one of the innovations that’s making these benefits possible. From the contact centre to the field, AI assistants surface the right information at the right time to enable proactive service, improve Net Promoter Scores, and increase loyalty.Here’s what that looks like in practice: Responding to customers with personalised, relevant answers grounded in trusted company knowledge across any preferred channel — including email, SMS, live chat, and social mediaResolving customer issues faster using generative answers seamlessly integrated into agents’ and technicians’ flow of workAutonomously completing tasks like auto-summarising intricate support cases and field work orders (back to top)5. AI is improving productivity and safety in the fieldOur research shows that mobile workers say innovative field service technologies make them feel safer and more effective at their jobs, empowering them to be better brand ambassadors. These technologies include intelligent scheduling, route optimisation, AI-generated reports, and augmented reality (which can create detailed 3D rendering of large areas in seconds).One of the emerging benefits of these and other technologies is the ability to generate insights and predict job duration. For example, workers can use AI to easily view asset condition as well as maintenance and repair history, then schedule proactive service to minimise downtime.Customers are benefiting, too. They can book and reschedule their own appointments using intelligent appointment assistance. And many customers have the ability to check when a technician is on the way, reducing no-shows and call volume for a better customer experience all around. (back to top)Staying one step ahead of customer service trendsA great strategy starts with the right questions. How can you pursue innovation while maintaining the integrity of your data? How do you build customers’ loyalty and trust? And what’s the secret to delivering faster, more effective customer service without breaking the bank?The right technologies can help you prepare for the future while keeping up with the latest customer service trends. That’s why your organisation must combine people, technology, and processes to deliver faster, more effective service at scale — with AI assisting you every step of the way.(back to top)

If you’re an email marketer, building a strong subscriber list is one of your top goals. But it’s also crucial to keep those email subscribers over the long term. Sending them great content is a given. But there’s an underrated factor that also determines success: email deliverability. Email platforms Gmail and Yahoo recently announced new measures to help prevent spam from reaching readers’ inboxes, meaning new domain validation requirements for bulk email senders. So if you want to avoid the spam filter, it’s time to take a step back and gain a better understanding of the three phases of email deliverability before you build your next campaign. Email deliverability is a message lifecycle that begins with a customer’s setup (the company sending the email) and extends out to the placement of a targeted message to their email subscribers. Subscriber engagement influences follow-on messaging. What are the phases of email deliverability? Email deliverability has three phases: setup, connect and curate. Your setup is the collection of products that comprise your account – these become personalised experiences and infrastructure for your campaigns. You utilise this infrastructure to then connect to your customers. In your customer outreach, permission is required and relevance is expected. Curate is when you analyse campaign results and take action based on email subscriber sentiment. Delivering an initial 15% off coupon is easy, but boosting engagement in month 15 and beyond is a challenge. Let’s take a deeper look at the elements of these phases and how they translate into long-term email subscribers. 1. Setup phase When we talk about “setup,” we’re referring to specific product offerings that help your mail reach the inbox of your email subscribers. These include: Sender Authentication Package (SAP): This ensures a customer has compliant, authenticated email messages that reflect their brand. Be sure to set up your SAP prior to sending to improve your odds of reaching the inbox. Private Domain: This authenticates a customer’s sending domain for use with email, but it does not come with a dedicated IP or any type of ‘branding’ within the account like a full SAP setup would. Dedicated IP Addresses:All customers who sendmore than 100,000 emails a month either in a single account or across their enterprise need to be utilising a dedicated IP address. This allows them to control the reputation of their sending IP(s). Multiple Dedicated IPs may be needed for high volume senders. Dedicated MTAs (Mail Transfer Agents): Dedicated infrastructure for high volume senders over 500 million annually. Ask your account executive if this solution is right for your company. SSL (Secure Sockets Layer): SSL Certificates are how a URL goes from being HTTP to HTTPS in the browser. SSL allows sensitive information to be uniquely encrypted and transmitted securely. Once your SAP domain is applied to your account, you can secure the domain via the application. You need to have the proper products (and the correct number of them) in place before you start sending email, so you can build out the best sender reputation possible. Your reputation as a properly authenticated sender of compliant mail helps you reach the inbox (and not the spam folder) of your email subscriber on a consistent basis. This helps improve email deliverability, ensuring you get the most for your money. Over time your company’s needs may change, due to organic email subscriber growth (you may need more sending IPs) or restructuring/mergers (changes in domains/SAP setups). It’s important to work with your account executive on a regular basis to review your exact situation. Get articles about marketing selected just for you, in your inbox Sign up now 2. Connect phase If you are in the “connect” phase of email deliverability, you likely already understand that email isn’t as simple as “batch and blast.” Email subscribers and receivers have become more sophisticated over time and expect relevant content sent at a reasonable frequency. Inbound filtering algorithms on the ISP (Internet Service Provider) end are tracking spikes in volume, engagement rates, spam complaints and bounce metrics in real time to determine whether mail should be placed in the inbox, the bulk folder or outright blocked. Here are the components of responsible sending: IP warming: Warming is an exercise all customers using dedicated IPs will need to go through. It’s how a legitimate sender introduces themselves to the ISPs by slowly building volume over time so it doesn’t overwhelm the ISPs like a spammer would and to allow metrics to be gathered for click/open/complaint rates. Permission based marketing: All addresses in a customer’s list must give explicit permission for the customer to send them email via the application. Relevant content/agreed upon frequency: Are you sending your emails more often than you promised? Less often? Is the content different from what you promised? Remember that communications are to be anticipated, personal, and relevant. If your users don’t anticipate your email or your content, reevaluate why you are sending them email in the first place. Easy to opt out: Ensure the unsubscribe process is easy and hassle-free for the end user. It’s better for a user to unsubscribe than mark your email as spam. List hygiene: Do you have addresses that you haven’t mailed to in six months? How about customers who haven’t opened an email or clicked a link in that same time period? These customers cost money to mail, reduce your results, and are more likely to register spam complaints against messages — harming your email deliverability. 3. Curate phase This phase is about monitoring campaign performance to better understand your email subscribers and their preferences. No single metric can tell you how well you’re doing – it takes a comprehensive view over time to really see trends and know where to adjust. Monitor campaign performance for trends: Utilise tracking andData Viewsto keep a close eye on trends around engagement (click rates) and complaints/unsubscribes. Listen to what your subscribers are telling you and adjust accordingly. Take action for high bounce rates: Proactively remove your bounced addresses before your next send. Even though the application automatically holds undeliverable emails after the third bounce, a bounce rate greater than 10% can dramatically harm your deliverability and ISP reputation. A bounce rate this high could suggest problems with the opt-in process for your email subscriber list. Make adjustments: List fatigue is real, as email subscribers tend to disengage over time as interests change or they find new brands. Sending the same 15% off coupon week after week may not be enough to keep an email subscriber engaged. Know that it’s okay to let unengaged subscribers go from your list. Saying goodbye can be hard. After all, you fought to earn that customer’s address. Here we are eight months down the road from their last click, and it seems like maybe they just don’t feel the same way they used to about you. Don’t take it personally – it’s okay to remove email subscribers who are no longer showing interest in your brand from your list. It doesn’t make you a bad marketer. It makes you a savvy one. Understanding where the ROI in your database comes from and nurturing that subscriber base optimises your ability to reach those highly engaged customers in their inbox. This vastly improves the odds of driving further interactions. Continuing to mail an unengaged subscriber base results in lower click/open metrics, more spam complaints and increased likelihood of receivers deciding your mail should be in the junk folder. Best practices to keep email subscribers engaged Our main point is that deliverability isn’t a ‘set forget’ situation. It takes ongoing maintenance of content, lists and products to achieve a consistent high level of success. You may find yourself re-entering the different phases of the email deliverability lifecycle periodically as you are constantly connecting with your email subscribers and curating those results, you may have a business need that requires you to re-enter the setup phase to accommodate increases in email subscriber volume or new lines of business.Setup: Have the proper products and number of products in place to meet your sending goals and ensure compliance.Connect: Send relevant content to opted-in subscribers. Don’t send more than expected and ensure you have a clear call to action to drive engagement.Curate: Review your results and act accordingly. What worked last month may not work today, adjust your audience, content frequency as needed. Mailbox providers Gmail and Yahoo recently announced a set of requirements for bulk mail senders that touches on authentication of the sending domain, use of a clearly defined opt-out method and the importance of keeping spam complaints to a minimum (under 0.3% per Gmail’s guidance). These have been long-standing best practices in email marketing, but enforcement at the major mailbox providers reinforces the need for senders to be aware of the importance of the messaging lifecycle. Ignoring it is the fastest way to find yourself in the spam folder or blocked and unable to reach the inbox of your customers. Start improving your open rates today Want to learn more about the foundation of successful email sending? Discover how on Trailhead, the free online learning platform from LIKE.TG. See how it’s done +400 points Module Email Deliverability Concepts

Customer Relationship Management (CRM) databases have become an essential tool for businesses of all sizes. By storing and organising customer data, CRM databases help companies track sales opportunities, manage customer relationships, and deliver better customer service. In this blog post, we will explore the benefits of using a CRM database, discuss how to get started with one, and provide tips for optimising your CRM data. We will also provide a specific example of a CRM database and explore the features and benefits of LIKE.TG, a leading CRM platform. What is a CRM database? A customer relationship management (CRM) database is a software platform that helps businesses manage customer interactions and data. It is a centralised repository for all customer-related information, including contact details, purchase history, support interactions, and more. By leveraging a CRM database, businesses can effectively manage customer relationships, track sales opportunities, and deliver exceptional customer service. A CRM database goes beyond simply storing customer data. It enables businesses to gain valuable insights into customer behaviour, preferences, and buying patterns. This information can be leveraged to personalise marketing campaigns, improve customer service strategies, and drive business growth. Additionally, CRM databases facilitate collaboration among different departments within an organisation, ensuring that all customer interactions are consistent and aligned with the company’s overall goals. In today’s competitive business landscape, having a robust CRM database is essential for businesses that want to succeed. It provides a comprehensive view of the customer journey, allowing businesses to make informed decisions, optimise their sales processes, and deliver a seamless customer experience. With its ability to streamline customer interactions, enhance sales performance, and drive business growth, a CRM database is an indispensable tool for modern businesses. Benefits of utilising a CRM database A CRM database offers a plethora of benefits to businesses of all sizes and industries. Here are some compelling reasons why utilising a CRM database is crucial: Streamlined communication and customer service: A CRM database centralises all customer interactions, making it easier for businesses to track and respond to customer inquiries, complaints, and requests. This streamlined communication enhances customer satisfaction and loyalty, as customers can seamlessly reach out to businesses and receive prompt assistance. Improved customer retention: By leveraging customer data and insights from a CRM database, businesses can develop targeted strategies to retain existing customers. This can be achieved through personalised marketing campaigns, proactive customer service, and loyalty programs. By nurturing customer relationships, businesses can increase customer lifetime value and reduce customer churn. Increased sales and revenue: A CRM database empowers businesses to identify and capitalise on sales opportunities. By analysing customer data, companies can gain insights into customer preferences, buying patterns, and pain points. This knowledge enables businesses to tailor their sales pitches, upselling and cross-selling opportunities, and pricing strategies, leading to increased sales and revenue growth. Enhanced decision-making: A CRM database provides businesses with valuable data and analytics that support informed decision-making. By analysing customer data, companies can gain insights into market trends, customer behaviour, and sales performance. This information empowers businesses to make data-driven decisions about product development, marketing campaigns, and resource allocation, ultimately driving business success. Better targeted marketing: A CRM database enables businesses to segment customers based on various criteria such as demographics, purchase history, and engagement levels. This segmentation allows companies to deliver personalised marketing campaigns that resonate with specific customer groups. Targeted marketing campaigns increase the effectiveness of marketing efforts, resulting in higher conversion rates and improved return on investment (ROI). Data migration: 5 steps to getting started with a CRM database Data migration is a crucial step in implementing a CRM database. Here are five steps to help you get started: 1. Define the Scope and Objectives of Your Data Migration Project: Before embarking on the data migration process, clearly defining your project’s scope and objectives is essential. This includes identifying the specific data that needs to be migrated, the source systems from which the data will be extracted, and the target CRM database where the data will be stored. Additionally, it’s crucial to set measurable objectives for the data migration process, such as ensuring data accuracy, completeness, and consistency. 2. Identify the Source Systems and Data to be Migrated: The next step involves identifying the source systems from which data will be extracted. This could include various systems such as spreadsheets, legacy CRM systems, customer support platforms, e-commerce platforms, and more. Once the source systems are identified, you need to determine the specific data elements that need to be migrated. This may include customer contact information, purchase history, support interactions, product information, and other relevant data. 3. Design the Target CRM Database Schema and Data Model: The target CRM database should accommodate the data migrated from the source systems. This involves creating a database schema that defines the tables, fields, and relationships that will store the data. Additionally, you need to develop a data model that specifies the data types, formats, and constraints for each data element. 4. Extract, Transform, and Load the Data: Once the target CRM database schema is designed, you can proceed with the data migration process. This involves extracting data from the source systems, transforming it to conform to the target CRM database schema, and loading it into the CRM database. Data transformation may include tasks such as data cleansing, data conversion, and data enrichment. 5. Test the Migrated Data and Ensure Data Integrity: The final step is thoroughly testing the migrated data to ensure its accuracy, completeness, and consistency. This involves verifying that the data has been correctly extracted, transformed, and loaded into the CRM database. Additionally, it’s essential to perform data validation checks to identify any errors or discrepancies in the data. Regular data quality checks should be implemented to maintain data integrity over time. CRM database example CRM databases are essential for businesses looking to manage customer interactions effectively and drive growth. Several popular CRM database software options are available, each with unique features and capabilities. Here, we will briefly explore four widely recognised CRM database software: LIKE.TG Sales Cloud CRM, Pipedrive CRM, Zoho CRM, and HubSpot CRM. LIKE.TG Sales Cloud CRM is a comprehensive CRM solution designed for businesses of all sizes. It offers a wide range of features, including sales force automation, customer service management, marketing automation, and analytics. LIKE.TG Sales Cloud CRM is highly customisable and can be integrated with various third-party applications, making it a versatile option for businesses with complex requirements. Pipedrive CRM is a user-friendly CRM software specifically designed for sales teams. It emphasises visual sales pipelines, allowing users to track the progress of their deals easily. Pipedrive CRM also offers features such as lead management, email integration, and mobile access, making it an excellent choice for sales professionals on the go. Zoho CRM is a cloud-based solution offering a wide range of features, including sales force automation, customer service management, marketing automation, and analytics. Zoho CRM is known for its affordability and ease of use, making it an excellent option for small businesses and startups. HubSpot CRM is a free CRM software offering various features, including contact management, lead generation, email marketing, and analytics. HubSpot CRM is an excellent option for businesses looking for a cost-effective CRM solution with powerful marketing capabilities. These four CRM database software options represent a fraction of the available choices in the market. Businesses should carefully evaluate their needs and requirements when selecting a CRM database to ensure they find the best fit for their organisation. Six ways to optimise your CRM data Optimising your CRM data is crucial to ensure its accuracy, consistency, and usability. Here are six effective ways to optimise your CRM data: 1. Identify and Eliminate Duplicate Data: Duplicate data can lead to errors, confusion, and wasted storage space. Regularly audit your CRM database to identify and eliminate duplicate records. This can be done manually or by using data cleansing tools. 2. Enrich Your Data with Additional Sources: Enhance your CRM data by integrating it with other data sources such as social media platforms, loyalty programs, and website analytics. This will provide a more comprehensive view of your customers and their interactions with your business. 3. Standardise Your Data Formats: Ensure consistency in data formats across all fields and records. This includes standardising date formats, currency formats, and measurement units. Data standardisation improves data accuracy and facilitates data analysis. 4. Regularly Cleanse Your Data: Clean your CRM data to remove outdated, incomplete, or inaccurate information. Data cleansing helps maintain data integrity and ensures your CRM system contains only relevant and useful data. 5. Implement Data Governance Policies and Procedures: Establish clear data governance policies and procedures to ensure customer data’s consistent and ethical use. This includes defining data ownership, access rights, and data security measures. 6. Train Your Team on Data Quality: Educate your team about the importance of data quality and provide training on proper data entry and management practices. Empower your employees to maintain accurate and up-to-date customer information. Getting Started with a LIKE.TG CRM Database This section will provide a step-by-step guide on getting started with a LIKE.TG CRM database. We will cover everything from signing up for a LIKE.TG account to importing your data into the database. Step 1: Sign up for a LIKE.TG account The first step is to sign up for a LIKE.TG account. You can do this by visiting the LIKE.TG website and clicking the “Sign Up” button. You must provide your name, email address, and password. You will also need to choose a plan. LIKE.TG offers a variety of plans, so you can choose the one that best fits your needs. Step 2: Import your data Once you have created your LIKE.TG account, you can import your data into the database. You can do this by using the LIKE.TG Data Loader. The Data Loader tool allows you to import data from various sources, including CSV files, Excel files, and other databases. Step 3: Customise your LIKE.TG database After you have imported your data, you can customise your LIKE.TG database to meet your specific needs. You can do this by creating custom fields, objects, and reports. You can also use the LIKE.TG AppExchange to install third-party apps that can extend the functionality of your database. Step 4: Train your team on LIKE.TG Once you have customised your LIKE.TG database, you need to train your team to use it. LIKE.TG offers a variety of training resources, including online courses, webinars, and in-person training. You can also hire a LIKE.TG consultant to help you train your team. Step 5: Go live with LIKE.TG Once your team is trained, you can go live with LIKE.TG. This means that you will start using LIKE.TG to manage your customer relationships. LIKE.TG can help you streamline your sales process, improve customer service, and grow your business.

2023 has been a uniquely challenging year for the insurance sector. Inflation, climate change, and supply chain issues have contributed to 10-year record high losses for some insurance companies. To mitigate these pressures, forward-thinking insurance companies in Southeast Asia are looking for new ways to use artificial intelligence (AI) to create efficiencies across the value chain. And, in doing so, they’re finding new opportunities for growth. In Singapore, for example, 50% of respondents to the LIKE.TG Connected Financial Services Report said they would switch to a new insurer if they offered a better digital experience. However, face-to-face engagement and the human advisory experience is still important in Southeast Asia to build customer trust, especially in high impact and personalised products like Life and Health (LH) insurance. While in the Property Casualty (PC) insurance business, there has been an increasing momentum to go full digital and touchless. There is a generational aspect to this human/digital balance too. For example, the more seasoned generations typically prefer some human touch in the customer journey, while young consumers usually prefer more digital experiences. Meanwhile, advancements in AI – particularly the rapid commercialisation of GenAI products through 2023 – have led insurers to adopt AI in various functions. 1. Adopt a data-first approach to leverage AI Data has always been crucial for managing risk, determining claims, and setting premiums. In addition, it’s also a critical tool actuaries use to set the prices and rules that give insurers confidence that they can cover claims while staying solvent and regulatory compliant. In this way, data is foundational to the insurance sector’s financial health and ability to mitigate risk. The advent of AI has heightened the importance of data in insurance to even higher levels, because AI is only effective when insurers use rich, interconnected, trusted datasets. For example, Thailand-based online car insurer, Roojai, is able to utilise granular data sets to focus on optimising the customer journey from origin to conclusion. This has contributed to a 25% reduction in cost per conversion, and a 16% increase in conversions. To adopt a similar data-first approach, insurers can use LIKE.TG Customer 360 to: Connect and unify customer data to enhance downstream applications with a 360-degree customer view. For example, LIKE.TG Customer 360and MuleSoftconnect your departments and customer data to provide a single, shared view of your customers. Benefit from generative AI without compromising data thanks to best-in-class security guardrails and enterprise security standards. For example, the Einstein Trust Layer uses guardrails like dynamic grounding, zero data retention, and toxicity detection, to protect data privacy and security and improve AI results accuracy. Grows deeper policyholder connections and increases productivity. For example, Data Cloud translates raw data into intelligence that enables insurance agents to visualise all customer engagement and activity, segment audiences, and prioritise cross-sell opportunities. Enhance customer engagement with personalised interactions and insights. For example, a connected CRM gives your teams access to a single source of customer data they can use to personalise content and experiences to meet your customers’ unique needs. Offer AI-driven insights, data analytics, and data visualisation across departments. For example, Tableau Analytics can be used to create intelligent experiences across the company with augmented analytics tools such as one-click storytelling with automated discovery, real-time recommendations, and narrative explanations with natural language generation. 2. Use automation technology to streamline operational processes Swift underwriting is essential to deliver a seamless insurance sales experience to customers. This has been a challenge for insurance companies because underwriting often requires extensive amounts of information processing and decision-making. Imagine managing extensive data on coverage, benefits, and pricing across numerous insurance plans, conducting rule validation, and workflows across various applications. This can significantly lengthen turnaround times. In addition, underwriting is more than just a desktop task. It involves collaboration with various partners and customers. For example, insuring a High Net Worth Individual (HNWI) in Asia might require assessments of health, lifestyle, and financials. This sometimes involves third-party services, which adds further complexity. Similarly, commercial property insurance requires thorough property assessments, sometimes with onsite surveys by risk engineers. These comprehensive processes all contribute to longer turnaround times for the customer. Insurers can streamline insurance processes by leveraging industry solutions. For example, LIKE.TG Financial Services Cloud automates underwriting and pricing, optimises workflows and collaboration, reduces turnaround time, and thus enhances sales conversion and Customer Lifetime Value (CLV) by empowering agents to focus on effective sales engagement and opportunities for cross-selling and up-selling.In addition, Slack, brings conversations, collaboration, and automation together, making collaboration and communication between underwriters, sales agents, product managers, and third party service providers organised and aligned. Past underwriting data and knowledge are made easily accessible through Slack’s AI-powered search. And real-time analytics and visualisation, along with natural language queries, empower employees to make informed decisions quickly. 3. Recruit multi-generational customers with innovative digital engagement As a new, affluent young customer group emerges in the region, insurance engagement is shifting from limited touchpoints to more frequent contact. For example, Singapore-based insurer Singlife understands the importance of connecting with their customers on their preferred channels, and replaced post-delivered policy documents with engaging digital experiences. The aim is to make buying insurance as seamless an experience as shopping on any other online platform. In addition, innovative telematics-based rewards, digital health concierges, health thought leadership, and engaging digital methods like embedded finance and gamified social media campaigns are all helping insurance companies to more effectively engage with customers online. At the same time, social media platforms that attract diverse user groups enable insurers to personalise their marketing efforts. LIKE.TG Marketing Cloud is one tool insurers are using to achieve this. It enables real-time, hyper-personalised engagement with native integration across digital platforms. Marketing Cloud also leverages Einstein AI for automated, customised customer journeys and sophisticated analytics for marketing performance and ROI insights. This approach ensures insurers remain competitive and effective in a rapidly evolving market. Seize today’s opportunities with next-gen solutions The insurance industry stands at a pivotal juncture, marked by both challenges and opportunities. For insurers, the path ahead includes adopting trusted AI and data strategies, automating and augmenting insurance processes, and captivating and retaining multi-generational customers through diverse channels. Leveraging platforms like LIKE.TG’s multi-cloud solutions will be crucial in integrating these initiatives, enabling insurers to not only satisfy current needs but also stay future proof. This strategic approach will drive sustainable growth and resilience in the ever-evolving insurance landscape in Southeast Asia.

Conversion rate is a crucial metric that measures the effectiveness of your website or app in converting visitors into customers. It’s the percentage of visitors who take a desired action, such as making a purchase, signing up for a newsletter, or downloading an app. Understanding conversion rate is essential for businesses looking to optimise their online presence and drive growth. In this blog post, we’ll explore what conversion rate is, how to calculate it, why it’s important, and how to improve it. We’ll also provide conversion rate benchmarks and tips for enhancing your website’s conversion rate with LIKE.TG, the world’s leading customer relationship management (CRM) platform. The definition of conversion rate Conversion rates are a key metric that measures the effectiveness of your website or app in converting visitors into customers. It is calculated as the percentage of visitors who take a desired action, such as making a purchase, signing up for a newsletter, or downloading an app. In simpler terms, it represents the ratio of the number of conversions to the total number of visitors or sessions on your website or app. Conversion rate serves as a valuable indicator of how well your website or app is achieving its intended goals. By monitoring and analysing conversion rates, businesses can gain insights into the effectiveness of their marketing campaigns, website design, user experience, and overall performance. It helps identify areas for improvement and allows businesses to make data-driven decisions to optimise their online presence and drive growth. Furthermore, conversion rate optimisation plays an essential role in tracking the success of specific initiatives, such as marketing campaigns, website redesigns, or the introduction of new features. By comparing conversion rates before and after implementing changes, businesses can quantify the impact of their efforts and make informed decisions about future strategies. Effective conversion rate optimisation strategies 1. Homepage Optimisation Opportunities The homepage serves as the initial point of contact for website visitors, making it crucial for a positive first impression and pivotal in retaining visitor interest to delve deeper into your site. Enhancements can include highlighting links to the product pages for details, promoting a free registration option, or integrating a chatbot to engage with visitors and answer their questions throughout their site exploration. 2. Enhancements for Pricing Pages The pricing page often determines whether visitors proceed to purchase. Utilising CRO techniques here can transform browsers into buyers by tweaking pricing structures (e.g., annual vs. monthly fees), elaborating on product features for different pricing tiers, providing contact options for direct inquiries, or introducing interactive elements like pop-up forms. An example of successful implementation is Hotjar, which added an email opt-in popup on its pricing page, resulting in over 400 new leads within three weeks. 3. Blog Conversion Strategies Blogs represent a significant opportunity for conversions by not only offering valuable industry insights but also by integrating effective CRO strategies to drive conversions and turn readers into leads. Tactics might include embedding multiple calls-to-action throughout posts, or promoting content offers like ebooks or reports in exchange for reader email addresses. 4. Landing Page Enhancements Given their direct call to action, landing pages typically exhibit the highest conversion rates among all types of signup forms—an impressive average of 23%. For example, an event landing page might feature a video from a previous event to boost registration rates for the current year, while a resource offering page could showcase snippets of content to entice downloads. Understanding When to Initiate CRO Knowing the best areas to apply CRO is key to conversion optimisation, and it’s equally important to recognise the right time to begin optimising your site for improved conversion rates. How to calculate conversion rates To calculate your conversion rate, you need to know the number of visitors who took the desired action and the total number of visitors to your website or app during a specific time period. The formula for conversion rate is: Conversion Rate = (Number of conversions / Total number of visitors) * 100 For example, if 100 people visit your website and 10 of them make a purchase, your conversion rate would be 10%. You can track your conversion rates over time using Google Analytics or other analytics tools. This will help you see how your conversion rates are trending and identify any areas where you need to improve. Conversion rates can vary depending on a number of factors, including: The type of website or app you have The target audience you are targeting The traffic source (e.g., organic search, paid advertising, social media) The landing page that visitors are directed to It is important to track and analyse your site’s conversion rate and rates so that you can make data-driven decisions to improve your website or app and drive growth. Conversion rates are a key metric for measuring the success of your business. They measure the success of your marketing and sales efforts, help you identify areas for improvement in your sales funnel, allow you to compare your performance and conversion funnel to industry benchmarks, and can help you to optimise your website and marketing campaigns. By tracking conversion rates, you can gain insights into the effectiveness of various channels and strategies. This enables you to allocate your resources more efficiently, focusing on the methods that yield the highest returns. Moreover, conversion rates help you identify potential bottlenecks in your sales funnel, allowing you to rectify issues and enhance the overall customer experience. Furthermore, conversion rates serve as a benchmark for measuring your performance against competitors. By comparing your conversion rates to industry standards, you can assess your competitiveness and make necessary adjustments to improve your position in the market. Additionally, conversion rates provide valuable data for A/B testing and website optimisation. By experimenting with different elements of your website or app, you can determine what works best for your audience and optimise the user experience accordingly. In essence, conversion rates are crucial for businesses aiming to grow their customer base and revenue. By closely monitoring and analysing conversion rates, businesses can make informed decisions, optimise their marketing strategies, and enhance their overall performance. Conversion rate optimisation (CRO) is the process of increasing the percentage of visitors to your website or app who take a desired action, such as making a purchase or signing up for a service. It involves a data-driven approach to analysing and optimising your website or app to improve its effectiveness in converting visitors into customers. There are five key areas to focus on when optimising your conversion rate: 1. Testing different versions of your website or landing page. A/B testing allows you to compare different versions of your website or landing page to see which one performs better. You can test different elements, such as the headline, call to action, or the web page layout, to determine what resonates most with your target audience. 2. Personalising your website or landing page to your target audience. Personalisation involves tailoring your website or landing page to the specific interests and needs of your target audience. This can be done by using data from your analytics platform to identify the demographics, interests, and behaviours of your visitors. 3. Using clear and concise calls to action. Your call to action (CTA) is what tells visitors what you want them to do next. Make sure your CTA is clear, concise, and easy to find. It should also be relevant to the content on the page and the needs of your target audience. 4. Making it easy for visitors to convert. The checkout process should be as simple and straightforward as possible. Avoid asking potential customers for unnecessary information, and make sure the payment process is secure and easy to understand. 5. Tracking your results and optimising your campaigns accordingly. It’s important to track your conversion rates so that you can see what’s working and what’s not. This will allow you to make data-driven decisions about how to optimise your website or app for better conversions. By following these five steps, you can improve your conversion rate, increase conversions, and drive more growth for your business. Conversion rate benchmarks This section provides conversion rate benchmarks for different industries, services and businesses. It discusses the average conversion rate for all industries, as well as the full conversion rate optimisation process and rates for high-performing websites, B2B websites, and mobile websites. The average conversion rate for all industries is around 2-3%. This means that for every 100 visitors to a website, 2-3 will take the desired action. However, there is a significant variation in conversion rates between different industries. For example, the average conversion rate for e-commerce websites is around 4%, while the average conversion rate for lead generation websites is around 2%. High-performing websites typically have a conversion rate of 5% or higher. These websites are typically well-designed, easy to use, and have a clear call to action. They also tend to have a strong value proposition and a targeted marketing strategy. B2B websites typically have a lower conversion rate than e-commerce websites. This is because B2B sales cycles are typically longer and more complex. B2B websites typically have a conversion rate of around 2-3%. Mobile devices and websites typically have a lower conversion rate than desktop websites. This is because it can be more difficult to design a mobile website that is easy to use and navigate. Mobile websites typically have a conversion rate of around 1-2%. It is important to note that conversion rates can vary significantly depending on a number of factors, such as the type of website or app, the target audience, the traffic source, and the landing page that visitors are directed to. Businesses should track and analyse their conversion rates over time to identify areas for improvement. Improving your conversion rate with LIKE.TG LIKE.TG is a powerful customer relationship management (CRM) tool that can help you improve your conversion rate in a number of ways. Here are a few tips: Track and test your landing web pages, forms, and CTAs: LIKE.TG allows you to track the performance of your landing pages, forms, and calls to action (CTAs). This information can help you identify which elements are working well and which ones need to be improved. You can then use this information to make changes to your website or app and improve your conversion rate. Create targeted and personalised marketing campaigns: LIKE.TG allows you to create targeted and personalised marketing campaigns based on your customer data. This information can help you send the right messages to the right people at the right time, which can increase your chances of converting them into customers. Track and analyse your customer journey: LIKE.TG allows you to track the customer journey from the moment they first visit your website or app to the moment they make a purchase. This information can help you identify any bottlenecks or drop-off points in your sales funnel and make changes to improve your conversion rate. Automate your marketing and sales processes: LIKE.TG can help you automate your marketing and sales processes, which can free up your time to focus on other tasks. This can help you improve your efficiency and productivity, which can lead to more conversions and increased sales and revenue. By following these tips, you can use LIKE.TG to improve your conversion rate and grow your business.

How much do your customers like their self-service customer service experience with your business? If they’re not impressed yet, here are three ways you can improve it. Self-service lets your customers find the answers they need on their own time, without the help of an agent. Most importantly, it’s what they prefer: our research found that 61% of customers would rather use self-service for simple issues. Enabling your customers to help themselves also increases efficiency. We found that 67% of organisations are now tracking case deflection, typically done through customer self-service or automated processes.To ensure your self-service customer service channels always make the biggest impact, what can you do quickly — even in just one hour? Turns out, it’s a lot. What you’ll learn: What is self-service customer service? Benefits of self-service How to set up for self-service customer support success What is self-service customer service? At a high level, self-service customer service simply means letting customers help themselves. To give customers this option, you can provide: A help centre, also known as a knowledge base, where customers can search for answers to common questions. A customer portal, a branded website customers log into to access personal information and complete actions related to their account. A customer community, where customers gather to share ideas, answer questions, and solve problems together. Chatbots that provide 1:1 assistance to customers without an agent having to step in. These self-service tools give your customers convenience, speed, and anytime availability. (Back to top) Benefits of self-service Self-service customer support is a win-win for both your customers and your business. Nobody likes to wait in a support queue for assistance. Self-service can also provide fast or even instant answers in some cases. This can lead to a better customer experience and higher customer satisfaction scores (CSAT). Your business benefits by reducing support costs and improving operational efficiency. By deflecting routine questions to self-service channels, you can allocate your resources more effectively. For example, your agents can focus on complex cases that require critical thinking and empathy. (Back to top) How to set up for self-service customer support success Your goal is to make it easy for your customers to find solutions independently. To set your business up to deliver successful self-service, let’s look at three key steps to take. 1. Address frequently asked questions Taking a few minutes to connect with your team will help you understand the most common customer questions to address in your self-service options. Even with advancements in AI, there’s still no substitute for human experience. You can use your team’s insights to make continuous improvements to your self-service customer service channels. Host a daily standup Round up your team and gather commonly asked customer questions and how your agents resolve them. (This can be done virtually or in-person). With advancements in artificial intelligence (AI) technology, soon your AI-powered customer service software may be surfacing commonly asked questions to you. Streamline collaboration with technologyEncourage agents to use collaboration tools like Slack to communicate and work together. Slack helps your team stay aligned and move fast. Use it to bring the right people into the right conversations – like swarming on a difficult case with experts from across your organisation. Make sure your help centre has an accurate knowledge base Knowledge provides the key foundation to building out your self-service experience. Create and consistently update knowledge base articles to make sure your users always have the most relevant information. It’s also the foundation for generative AI in self-service. Remember: the more advanced your knowledge base, the more robust the overall search experience will be to find the right answers faster. 2. Find ways to streamline workflows Simplifying processes by making it easier to find information goes a long way for customers, increases efficiency, and frees up agents from cases that can be handled with self-service. Make it easy to find self-service channelsCustomers don’t want to search for support. Make sure that when they search online for customer support, the results provide a clear path to your help site’s landing page.Your company’s contact information shouldn’t be displayed in the search results. A customer will be less likely to access your help site and use self-service.Once customers get to your site, make it easy to get self-service. With a simple widget or code snippet, you can integrate a fixed channel menu on your help site home page. This can direct customers to chatbots, your knowledge base, or a customer community, like the Serviceblazer community Route cases with chatbotsChatbots can streamline workflows – especially AI-powered ones, which are programmed to have human-like conversations. AI chatbots use natural language processing (NLP), which allows them to better understand human speech – including the intent behind what the customer is saying. These bots can interpret the context of what a customer types or says – and then, with generative AI, respond intelligently based on existing data.Rules-based chatbots also do a lot for self-service. They respond based on buttons a customer clicks or particular keywords the customer uses. If you have a rules-based chatbot, review the chatbot data to find specific keywords for easy answers, and be sure to have an FAQ database the chatbot can use to answer questions. But do consider upgrading – an hour spent learning about the latest in AI chatbot technology could be a step toward greater efficiency. Create guided processesIn your customer self-service portal, you can automate specific processes for customers and free agents from receiving a high-volume of calls. What are the simple issues that take up agents’ valuable time, but could be just as easily done by customers? By integrating a workflow on your end, such as canceling an order or booking a field service appointment, the automation process appears on their screen and walks customers through each step. This not only saves on cost, it increases productivity and efficiency. 3. Continuously improve with data and human review The quick ways we’ve discussed to improve your self-service customer support offering can make a big impact. But there’s even more you can do over time. Tackle the ideas below in one-hour chunks, and soon you’ll be well on your way to building an excellent self-service experience. Prioritise reviewing self-serviceRegularly assess your company’s self-service customer support experience from every angle. For example, how does your help centre home page look? How do your search features perform? Are your article pages consistent? Is your chatbot performing well? Does your site allow visitors to provide feedback – not only about your products, but also the support experience? This handy self-service assessment tool can help you identify any gaps where you’re currently not meeting best practices. It gives you helpful guidance on where to focus your improvement effort. Review your dataWhat are your objectives for self-service customer service? Perhaps you want to increase case deflection, so you can give customers the kind of assistance they prefer and reserve your agents’ energy for more complex matters. Or maybe you want to increase CSATs. Decide on the key performance indicators (KPIs) that matter to your team. Then use service analytics to track and monitor this data with easy-to-understand charts and graphs. Ready to get started? With a few simple updates, you can ensure your self-service customer support channels are working hard to help your customers quickly find the answers they need. In time, your customers will be getting an efficient and easy self-service customer service experience they’ll love. Then watch your CSATs soar. (Back to top)

Demand forecasting is an essential business practice in which companies are able to anticipate future market demands for their products or services. By accurately predicting these demands, businesses can optimise their operations, minimise costs, and effectively meet customer needs. Throughout this blog, we’ll take a closer look into the concept of demand forecasting, explaining its significance and exploring the various factors that influence it, whilst also discussing the benefits and providing practical methods and models to help businesses forecast demand more effectively. We’ll also continue to take a look at the latest trends in demand forecasting and how LIKE.TG can be leveraged to enhance and improve demand forecasting accuracy. What is demand forecasting? Demand forecasting is a key business process that enables companies to predict future market demands for their products or services. It involves analysing historical data, current market conditions, and other relevant factors to make informed predictions about future demand patterns. By accurately anticipating demand, businesses can optimise their operations, minimise costs, and effectively meet customer needs. Demand forecasting is essential for businesses of all sizes and across all industries. It plays a pivotal part in the decision-making processes related to production, inventory management, sales and marketing teams, and overall resource allocation. Effective demand forecasting helps businesses avoid overproduction, which leads to excess inventory and increased costs, as well as underproduction, which results in lost sales and dissatisfied customers. Demand forecasting techniques range from simple to complex, depending on the availability of data and the level of accuracy required. Some common techniques include historical data analysis, passive demand forecasting, trend analysis, market research, and econometric modelling. Businesses can also leverage advanced analytics and machine learning algorithms to enhance the accuracy of their passive demand forecasts. Accurate demand forecasting provides businesses with a competitive edge by enabling them to respond swiftly to changing market dynamics and customer preferences. It helps businesses optimise their supply chains, reduce inventory holding costs, and allocate resources efficiently. Effective demand forecasting supports data-driven decision-making, leading to improved overall business performance and profitability. Demand Forecasting Explained Within business, the ability to anticipate future demand for products or services is essential. Demand forecasting serves as a compass, guiding businesses through the ever-changing currents of the market. By predicting demand accurately, companies can optimise production schedules, maintain and manage inventory levels to their optimal amount, and craft effective marketing strategies. The significance of demand forecasting lies in its power to illuminate the path ahead. Armed with insights into future demand, businesses can make choices that propel them towards success. They can anticipate market trends, identify shifts in consumer behaviour, and navigate economic fluctuations with agility. Effective demand forecasting lays a solid foundation for strategic decision-making, enabling businesses to expand production capacity, increase operational efficiencies, introduce new products, and venture into new markets with confidence. The process of demand forecasting involves a meticulous examination of historical data, market trends, and a multitude of other relevant factors. Businesses employ a range of methodologies, from time-tested statistical models to cutting-edge machine learning algorithms, to make informed predictions about future demand. The choice of method hinges on the complexity of the product or service, the availability of data, and the desired level of accuracy. However, the path of demand forecasting is not without its challenges. Uncertainty looms as demand can be swayed by a myriad of factors – economic shifts, evolving consumer preferences, technological disruptions, and the actions of competitors. To navigate these uncertainties, businesses incorporate flexibility into their quantitative demand forecasting models, ensuring they can adapt swiftly to unforeseen market changes. Despite the challenges, demand forecasting remains an invaluable tool for businesses seeking to gain a competitive edge in the marketplace. By harnessing historical data, conducting thorough market research, and employing sophisticated analytical techniques, businesses can enhance the precision of their demand forecasts. This empowers them to make better decisions, optimise operations, and stay ahead of the curve in the ever-evolving business landscape. Why Is Demand Forecasting Important for Businesses? In a business setting, demand forecasting is an essential. Equipped with insights into future demand, companies can anticipate market trends, identify shifts in consumer behaviour, and navigate economic fluctuations with agility. This foresight allows them to optimise operations, reduce costs, and meet customer demand effectively. One of the key benefits of demand forecasting is its ability to improve efficiency and reduce costs. By accurately predicting future demand, businesses can optimise their production schedules, inventory levels, and workforce planning. This reduces the risk of overproduction, which can lead to waste and increased costs, as well as the risk of stockouts, which can result in lost sales and customer dissatisfaction. Another important benefit of demand forecasting is increased responsiveness to market changes. As business environments are ever-changing, companies that can quickly adapt to changing market conditions have a significant competitive advantage. Demand forecasting helps businesses identify emerging economic trends, and shifts in consumer preferences, enabling them to adjust their strategies and product offerings accordingly. This agility allows companies to stay ahead of the competition and capitalise on new opportunities. Enhanced customer satisfaction is another key outcome of effective demand forecasting. By accurately predicting demand, businesses can ensure that they have the right products and services available to meet customer needs. This reduces the likelihood of stockouts and backorders, which can lead to customer frustration and dissatisfaction. Demand forecasting helps companies optimise their customer service operations, ensuring that they have the resources in place to handle customer inquiries and complaints efficiently. Effective demand forecasting also supports better financial planning and budgeting. By having a clear understanding of future demand, businesses can more accurately forecast their revenue and expenses. This enables them to make sound financial decisions, allocate resources efficiently, and manage cash flow effectively. Accurate demand forecasting reduces the risk of financial surprises and helps businesses maintain financial stability. Finally, demand forecasting is a key player in improving supply chain management. By sharing demand forecasts with suppliers, businesses can ensure that they have the necessary raw materials and components to meet production requirements. This collaboration helps optimise the entire supply chain, reducing lead times, minimising inventory levels, and improving overall efficiency. Effective demand forecasting enables businesses to build strong relationships with suppliers and gain a competitive advantage in the market. What Factors Impact Demand Forecasting? Various factors influence the precision of demand forecasting, a crucial component of effective business planning. These factors can be broadly categorised into external and internal elements. External factors encompass the overarching economic landscape. Economic indicators like GDP growth, inflation rates, interest rates, and consumer confidence greatly impact consumer purchasing behaviours. When economic conditions are favourable, consumer demand for products and services flourishes, while economic downturns can lead to decreased demand. Market and consumer trends are another significant external influence. Changing consumer preferences, innovative product introductions, and technological advancements can reshape market dynamics and alter product demand. Businesses must continuously monitor market trends to stay ahead of demand shifts. Seasonal patterns can also affect demand and weather conditions contribute to demand forecasting. For instance, seasonal products like winter clothing or summer beverages experience predictable fluctuations in demand. Businesses must account for these seasonal variations to optimise their own inventory planning and production strategies. Competitor activity is another external factor that can impact the demand for a product. The introduction of competing products or services, changes in pricing strategies, or shifts in marketing campaigns can influence consumer choices. Businesses need to closely monitor their competitors’ actions to mitigate any negative impact on their demand for a product. Internal factors also contribute to demand forecasting accuracy. Production capacity, inventory levels, and marketing efforts all contribute to demand forecasters. Ensuring sufficient production capacity to meet demand, maintaining optimal inventory levels to avoid stockouts, and effectively promoting products through marketing channels are essential for managing demand successfully. By comprehending and analysing these external and internal factors, businesses can enhance the accuracy of their demand forecasts. This enables them to make well-informed decisions regarding production planning, inventory management, marketing strategies, and overall resource allocation, ultimately driving business growth and profitability. Benefits of Demand Forecasting Effective demand forecasting serves as a guiding compass, empowering businesses to navigate the ever-changing currents of the marketplace with precision and agility. One of its benefits lies within resource allocation. Through accurate demand projections, businesses can meticulously plan their production schedules, ensuring that they possess the essential resources – raw materials, skilled labour, and state-of-the-art equipment – to satisfy future customer demand, without the perils of overstocking or shortages. This strategic approach translates into reduced costs and enhanced operational efficiency, laying the foundation for sustainable growth. Another highlight of demand forecasting is its ability to cultivate customer delight. By maintaining optimal inventory levels, businesses ensure that their customers can effortlessly obtain the products or services they desire, when they desire them. This proactive approach minimises the frustrations of stockouts, backorders, and interminable wait times, fostering customer loyalty and satisfaction. Meeting customer demand with precision not only strengthens the business’s reputation but also arms it with a formidable competitive advantage. Demand forecasting also serves as a catalyst for enhanced profitability. By using sales forecasting and aligning production and inventory levels with anticipated demand, businesses can effectively combat waste and maximise revenue streams. This strategic alignment allows them to produce the right products, in the ideal quantities, and at the opportune time, thereby diminishing the risks of overproduction or underproduction. Armed with accurate demand forecasts, businesses can engage in strategic negotiations with suppliers, securing favourable pricing and terms that further bolster their financial position. Beyond its immediate impact on resource allocation, customer satisfaction, and profitability, demand forecasting also elevates decision-making across all echelons of an organisation. Armed with reliable demand projections, businesses can chart their course with confidence, making plans regarding product development, marketing strategies, and expansion plans. This enables them to cease market opportunities, introduce products or services that resonate with customer needs, and venture into new markets with a calculated approach. By aligning their decisions with the compass of demand forecasting, businesses can mitigate risks and amplify their chances of success, propelling them towards sustained growth and industry leadership. How to Forecast Customer Demand To derive accurate demand forecasts, businesses must embark on a series of meticulous steps. The initial phase of demand forecasting often involves comprehending the product lifecycle and industry trends that affect demand now. It’s to recognise the stage of the product’s lifecycle (introduction, growth, maturity, or decline) and understand how industry trends may influence the demand forecast. The next step entails identifying and analysing historical demand data. This involves gathering data on past sales, customer demand, and market trends. Analysing this historical sales data can reveal patterns and trends that can inform future sales and demand forecasts. Selecting the appropriate and accurate forecasting method is also critical. There are various forecasting methods available, each with its own strengths and weaknesses. Some common methods include moving averages, exponential smoothing, and regression analysis. The choice of method depends on the nature of the product, the availability of data, and the level of accuracy required. Collecting and analysing relevant data is another key step in forecasting sales further. This may involve gathering data on various economic trends and indicators, consumer behaviour, competitor activity, and other factors that can influence demand. Analysing this data can provide valuable insights into future demand trends. Finally, it is essential to make adjustments through active demand forecasting based on market conditions. Demand forecasts are not static; they need to be continuously monitored and adjusted based on changing market conditions and customer expectations. This may involve incorporating real-time data, such as sales figures and customer feedback, into the forecasting process. By following these steps and employing robust demand forecasting techniques, businesses can enhance the accuracy of their predictions that drive success. 10 Demand Forecasting Methods This section provides an overview of 10 demand forecasting methods, encompassing time series analysis, causal methods, judgmental methods, simulation, quantitative methods, and machine learning methods. 1. Time Series Analysis Time series analysis involves analysing historical demand data to identify patterns and trends. Common techniques include moving averages, exponential smoothing, and seasonal decomposition. 2. Causal Methods Causal methods establish a relationship between demand and various influencing factors, such as economic indicators, consumer behaviour, and competitor activity. Regression analysis and econometric models are commonly used causal methods. 3. Judgmental Methods Judgmental methods involve incorporating expert opinions and market insights into the forecasting process. These qualitative methods may include the Delphi method, executive opinion, and customer surveys. 4. Simulation Methods Simulation methods use computer models to simulate real-world conditions and generate demand scenarios. Monte Carlo simulation and system dynamics are examples of simulation methods. 5. Machine Learning Methods Machine learning algorithms can analyse large datasets and identify complex patterns. Artificial neural networks, decision trees, and random forests are commonly used machine learning methods for demand forecasting. 6. Moving Averages Moving averages calculate the average demand over a specified period, smoothing out short-term fluctuations. Simple moving averages (SMAs) and exponential moving averages (EMAs) are commonly used. 7. Exponential Smoothing Exponential smoothing assigns exponentially decreasing weights to past demand data, giving more importance to recent data. Single exponential smoothing (SES), double exponential smoothing (DES), and triple exponential smoothing (TES) are different types of exponential smoothing techniques. 8. Seasonal Decomposition Seasonal decomposition separates demand into seasonal, trend, and residual components. Seasonal indices are used to adjust demand forecasts for seasonal variations. 9. Regression Analysis Regression analysis establishes a statistical relationship between demand and one or more independent variables (e.g., price, advertising, economic indicators). Linear regression, multiple regression, and logistic regression are common regression techniques. 10. Econometric Models Econometric models are advanced statistical models that account for the interdependencies and dynamics of various economic factors influencing demand. These models often require extensive data and specialised expertise. Demand Forecasting Models Demand forecasting models are vital tools for predicting future demand and aiding businesses in making better choices. Several models can be employed for various types of demand forecasting, each with its own advantages and applications. Here are some commonly used demand forecasting models: Moving Average Model: The moving average model is a simple yet effective technique that calculates the average of past project sales and future demand, over a specified period. It assumes that future demand will follow a similar pattern to past demand. This model is suitable for stable demand patterns with minimal fluctuations. Exponential Smoothing Model: The exponential smoothing model is an extension of the moving average model that assigns exponentially decreasing weights to past demand data. This model gives more importance to recent demand data, making it more responsive to changing demand patterns. It is suitable for forecasting demand patterns that exhibit gradual trends or seasonal variations. Seasonal Autoregressive Integrated Moving Average (SARIMA) Model: The SARIMA model is a sophisticated time series analysis technique that combines autoregressive, integrated, and moving average components. It is beneficial for forecasting seasonal demand patterns. The SARIMA model identifies and accounts for seasonality, making it suitable for businesses with pronounced seasonal fluctuations in demand. Machine Learning Model: Machine learning algorithms, such as regression, decision trees, and neural networks, can be employed for demand forecasting. These models leverage historical demand data, along with other relevant factors, to make predictions. Machine learning models are particularly effective in capturing complex relationships and non-linear patterns in demand data. The choice of demand forecasting model depends on various factors, including the nature and types of demand forecasting the product or service, the availability of historical data, and the level of accuracy required. Businesses may use a combination of different models to enhance the accuracy of their demand forecasts. Demand Forecasting Examples Demand forecasting is used in various industries to predict future demand for products or services. Here are a few examples short term demand forecasting: Retail: Retailers use demand forecasting to optimise inventory levels and avoid stockouts or overstocking. By accurately predicting demand, retailers can ensure that they have the right products in the right quantities to meet customer demand. This helps reduce costs associated with holding excess inventory and improves customer satisfaction by ensuring that products are available when customers want them. Manufacturing: Manufacturers use demand forecasting to plan production schedules and manage supply chains. By accurately predicting demand, manufacturers can avoid production disruptions and through an efficient supply chain, they can ensure they have the necessary resources to meet customer demand. This helps reduce costs associated with production overruns or shortages and improves customer satisfaction by ensuring that products are available when customers need them. Transportation: Transportation companies use demand forecasting to plan routes and schedules and allocate resources. By accurately predicting demand, transportation companies can optimise their operations and ensure that they have the necessary capacity to meet customer demand. This helps reduce costs associated with empty vehicles or overloaded routes and improves customer satisfaction by ensuring that goods are delivered on time. Healthcare: Healthcare providers use demand forecasting to plan staffing levels, manage patient flow, and allocate resources. By accurately predicting demand, healthcare providers can ensure that they have the necessary staff and resources to meet patient needs. This helps reduce costs associated with understaffing or overstaffing and improves patient satisfaction by ensuring that patients receive timely and efficient care. Financial services: Financial institutions use demand forecasting to manage risk, plan investments, and allocate resources. By accurately predicting demand, financial institutions can look into how to allocate their capital and manage their risk exposure. This helps reduce costs associated with bad investments or excessive risk-taking and improves customer satisfaction by ensuring that customers have access to the financial services they need. Demand Forecasting Trends This section discusses the latest trends in demand forecasting, including the use of artificial intelligence and machine learning, real-time data and analytics, collaborative forecasting, and sustainability and ethical considerations. Artificial intelligence (AI) and machine learning (ML) are transforming demand forecasting by enabling businesses to analyse vast amounts of data and identify patterns and trends that would be difficult or impossible for humans to detect. By leveraging AI and ML algorithms, businesses can create more accurate and reliable demand forecasts, leading to better decision-making and improved business outcomes. Real-time data and analytics are a major component in modern demand forecasting. With the advent of the Internet of Things (IoT) and other data-generating technologies, businesses can now collect real-time data on various factors that influence demand, such as customer behaviour, market trends, and supply chain disruptions. By analysing this real-time data, businesses can make more informed and agile decisions, quickly adapting to changing market conditions. Collaborative demand forecasting method involves bringing together different stakeholders within an organisation to contribute their expertise and insights to the demand forecasting process. This approach combines the knowledge of sales, marketing, production, and other departments, resulting in more comprehensive and accurate forecasts. Collaborative internal demand forecasting also fosters a culture of shared responsibility and improves communication and alignment across the organisation. Sustainability and ethical considerations are increasingly becoming important factors in demand forecasting. Businesses are recognising the need to minimise the environmental impact of their operations and ensure ethical practices throughout the supply chain. Demand forecasting plays a large part in optimising resource allocation, reducing waste, and promoting sustainable practices. By considering sustainability and ethical factors in demand forecasting, businesses can align their operations with their values and meet the expectations of environmentally conscious consumers. These trends are revolutionising the field of demand forecasting, enabling businesses to make more accurate predictions, like the ability to predict demand, optimise their operations, and gain a competitive advantage in a data-driven business environment. Demand Forecasting with LIKE.TG Demand forecasting is an essential business process for optimising operations, reducing costs, and meeting customer demand effectively. LIKE.TG, a leading customer relationship management (CRM) platform, provides a variety of tools and capabilities to help businesses create accurate demand forecasts. One of the key features of LIKE.TG for demand forecasting is Einstein Discovery, a powerful artificial intelligence (AI)-powered tool that helps businesses analyse historical data and identify trends and patterns that can be used to predict future demand. Einstein Discovery uses machine learning algorithms to automatically detect relationships between different variables and generate accurate forecasts. LIKE.TG also allows businesses to leverage historical sales data to create demand forecasts. By analysing past sales data, businesses can gain insights into seasonal trends, sales trends, market fluctuations, and customer behaviour patterns. This historical data can be used to build statistical models and time series analysis to predict future demand. In addition to historical data, LIKE.TG enables businesses to incorporate predictive analytics into their demand forecasting process. Predictive analytics uses advanced statistical techniques and machine learning algorithms to analyse a variety of data sources, including customer demographics, market trends, economic indicators, and social media sentiment, to further generate revenue forecasts. LIKE.TG also allows businesses to integrate external data sources into their demand forecasting process. This can include data from market research firms, industry reports, and social media platforms. By combining internal data with external data points, businesses can gain a more comprehensive view of the market and make more accurate demand forecasts. LIKE.TG provides a user-friendly interface that makes it easy to create and manage demand plans and forecasts collaboratively with team members. Different users can access and update forecasts, share insights, and discuss assumptions, ensuring a collaborative and transparent demand planning process. Finally, LIKE.TG provides tools to monitor sales forecasts, make sales projections and track the performance of demand forecasts against actual results. This allows businesses to continuously evaluate the accuracy of their forecasts and make adjustments as needed. By analysing forecast errors and identifying the factors that influence demand, businesses can continuously improve their forecasting accuracy and optimise their operations.