Machine learning and artificial intelligence (AI) technologies have advanced significantly in recent years, impacting various industries, from self-driving cars to robo-advisors. These technologies can also enhance digital marketing strategies by swiftly analysing relevant data sets and automating repetitive tasks. For businesses seeking a competitive edge, integrating machine learning in marketing can lead to stronger campaigns, personalised marketing materials, and increased efficiency. Despite the perception of machine learning as a complex concept, it is a valuable tool for automating aspects of campaigns and harnessing valuable data. In digital marketing, machine learning is poised to revolutionise processes, simplifying tasks such as digital ad campaigns, content creation, and personalised recommendations. This article provides a comprehensive guide to machine learning in marketing, exploring its definition and practical applications. The goal is to help business owners and marketers understand how machine learning can be leveraged to create data-driven campaigns effectively. What is machine learning? Machine learning, falling under the umbrella of AI, refers to systems and software applications designed to “learn” through the analysis of data sets. Machine learning involves technology that processes input, identifies patterns, and independently adapts to new data to form solutions and solve problems. This versatile concept finds applications across various industries, including healthcare, retail, shipping logistics, and marketing. In marketing, machine learning often involves software programs that enable quick analysis and extraction of insights from large data sets. Additionally, these programs automate tasks such as analytics, report analysis, content optimisation, and audience segmentation, increasing marketers’ efficiency. Impact of machine learning on businesses Despite being a relatively new technology, machine learning is rapidly evolving and transforming how businesses operate. As businesses increasingly rely on large and complex data sets for decision-making, machine learning models help organise, manage, and interpret data, providing valuable insights. In the digital marketing industry, which continuously adopts new technologies, machine learning significantly optimises campaigns and increases automation, ultimately boosting the bottom line. Applications of machine learning in marketing Machine learning has diverse applications in marketing, spanning customer segmentation, analytics, campaign optimisation, customer service, and forecasting. Here’s a closer look at how machine learning can be utilised in these key areas: Customer Segmentation Machine learning facilitates automated and accurate customer segmentation, allowing marketers to target specific groups based on various characteristics. This automation streamlines the process, making it more efficient and less prone to errors. Analytics As digital marketing software becomes more sophisticated, machine learning assists in processing and organising large data sets quickly. This technology identifies complex patterns and performs predictive analytics, providing marketers valuable insights for informed decision-making. Optimising Marketing Campaigns Machine learning helps marketers make data-driven decisions for campaign optimisation. By leveraging existing data, machine learning models guide marketers on the most effective channels to reach their target audience, maximising return on investment (ROI). Customer Service Automation through machine learning is prevalent in creating chatbots and digital assistants for customer service interactions. These tools enhance customer satisfaction by addressing needs promptly and efficiently. Forecasting Machine learning aids in accurate forecasting by identifying patterns in data, enabling businesses to predict future events and make informed decisions regarding demand, customer lifetime value, retention rates, and other crucial metrics. Benefits of using machine learning in marketing Incorporating machine learning into marketing strategies offers various benefits for businesses: Lower Costs Contrary to assumptions, machine learning tools are often integrated into existing marketing software, making them affordable. These tools automate processes, saving time and lowering costs associated with manual tasks. Higher-Quality Data Analysis Machine learning excels in quickly and accurately analysing large data sets, providing high-quality insights. This capability enables the creation of specialised customer segments and informed campaign performance tracking. Automation of Processes Machine learning facilitates automation in various business and marketing processes, saving teams time, money, and energy. Automation can be applied to customer inquiries, marketing campaigns, and other tasks. Customer Satisfaction By incorporating machine learning tools, businesses can enhance customer satisfaction by creating chatbots and digital assistants that promptly address customer issues. Automation and data optimisation also contribute to positive interactions with the brand. Implementing machine learning models To harness the benefits of machine learning, businesses can integrate these models into their operations. Platforms like LIKE.TG’s Marketing Cloud offer marketing solutions incorporating machine learning and automation to streamline campaign management and efficiently engage prospects and customers across channels. It enables businesses to segment audiences, track engagement, align marketing and sales teams to nurture leads, and automate workflows, resulting in improved efficiency, personalised customer experiences, and increased ROI.

The future of customer service starts with learning what your customers expect today — a personalised and connected experience. The catch? You need to focus on productivity and cost savings without compromising on quality. The right technology can help. As we look ahead to the future of customer service, generative AI will play an essential role in finding cost-effective ways to meet customers’ changing expectations. Here are three emerging trends you should keep on your radar as you build your customer service strategy for 2024. 1. AI is an opportunity — not a threat According to our research, 45% of service decision-makers are using AI, up from 24% in 2020. That means AI is increasingly a part of customer service toolkits. It also means that over half of decision-makers have yet to adopt AI in customer service. So what’s holding them back? Some service organisations may be afraid that their people lack the skills to handle AI. Others may have reservations about trust and reliability. There’s also the concern that implementing AI would require a major investment in infrastructure. These fears are understandable, and all companies should practice caution and care when deploying any technology as powerful as AI. But one thing is clear: AI is already connecting, informing, and enriching every aspect of customer service. Companies that remain rooted in doubt and uncertainty are almost certain to be left behind — and forward-thinking organisations are getting more done using AI in a secure, trustworthy way. Wondering how you can start using AI to improve your service organisation? An all-in-one platform like Service Cloud Unlimited+ can help you quickly make the most of your service tech investment. Here are just a few examples of how AI will continue to transform the future of customer service, starting today: Human-AI collaboration: Customer service agents are working alongside AI technology and systems to provide faster and more accurate information for a more satisfying customer experience. This will create a new role in your contact centre — the high-value agent. High-value agents, with the assistance of AI, will shift their focus from resolving simple issues to engaging in more complex interactions that generate revenue. Employee onboarding: AI will play an increasingly central role in onboarding new employees — especially in field service, where recruitment has been a particular challenge in recent years. Knowledge creation: Forward-thinking companies will preserve and share knowledge across the business by connecting generative AI tools to their service consoles, automatically drafting knowledge base articles based on customer interactions and customer relationship management (CRM) data. These articles can be used in self-service portals, turning search engines into answer engines as customers answer their own questions faster. Tactical tip: The latest customer service training strategies will be key to turning your service professionals into high-value agents. Help your employees understand the powerful potential of AI to serve as a valued partner and close collaborator in delivering exceptional customer service. 2. Advances in field service will help attract and retain frontline workers We found that 65% of mobile workers feel the weight of customer expectations, more than any other type of service worker. And 82% struggle to balance speed with quality when providing field service. That can have a major impact on job satisfaction — and it’s part of the reason why attracting and retaining frontline workers is more challenging than ever. The right tools can help. Our data shows that 93% of service professionals in high-performing organisations cite job satisfaction as a major or moderate benefit of field service management software. As we move into 2024, successful field service organisations will continue to improve productivity, cut costs, and generate revenue with AI while creating a better experience for workers in the field. Here’s how AI can help the future of customer service: Predictive maintenance: It’s always better to maintain devices instead of waiting for a major problem to occur. That’s why AI will add value in 2024 by proactively monitoring machine health, then automatically scheduling service appointments as necessary. AI will even be able to specify the required tools, appropriate technician, and length of time required to complete the job. Work summaries: AI will help minimise errors and improve productivity by automatically generating work summaries both pre- and post-visit — no matter how complex the engagement. This enables mobile workers to resolve issues quickly and move on to the next job in less time. More options for self-service: We discovered that 61% of customers would rather use self-service tools for simple issues. With help from AI, they can book appointments and track the progress of service visits on the messaging channel of their choice. That’s a win for mobile workers, too, because it will allow them to spend less time performing administrative tasks and more time delivering great customer service in the field. Tactical tip: Don’t leave your frontline workers behind. Help them become more proactive and productive with a complete view of each customer, including purchase details, service history, and the status of connected devices. You can integrate AI into their everyday tools with generative responses and work summaries. How can you use AI in customer service? AI can help you deliver more efficient and personalised customer service. Explore Trailhead, LIKE.TG’s free online learning platform, to discover how AI-driven chatbots and analytics are transforming the customer experience. Learn about AI today +200 points Module AI for Customer Service 3. The future of customer service puts revenue generation front and centre In the months ahead, the lines between sales, service, and commerce will continue to blur as AI-driven cross-selling transforms customer service into a profit centre. Forward-thinking organisations will pursue an end-to-end view of the entire customer journey, creating a continuous feedback loop between sales, service, and other departments within your organisation. Here’s what that means for service leaders in 2024: Expanded access: Customer service agents and field service workers will gain even greater access to a complete view of the customer. They can then offer tailored solutions and recommendations that align with each customer’s preferences and buying history. AI-driven recommendations based on customers’ preferences will be increasingly at agents’ fingertips, enabling them to strengthen their relationships and be more valuable for customers. Shared goals: Metrics traditionally associated with sales (such as conversion rates) and customer service (like resolution time) will continue to converge. All functions will focus more attention on metrics that reflect customer satisfaction, loyalty, and overall lifetime value. AI-powered insights: AI is already playing a significant role in analysing customer behaviour, predicting trends, and making informed decisions based on trusted customer data. With this information readily available, agents’ focus can shift from reactive problem-solving to predictive assistance and proactive relationship-building. Teams can work together to anticipate customer needs, address issues before they arise, and offer value-added services that foster loyalty and generate revenue. Tactical tip: Consolidating tech investments will be the key to resilience in 2024. Consider adopting a unified platform that connects your sales, service, and marketing teams for seamless communication and data sharing among these departments. The result? More effective engagement, better issue resolution, and a more holistic view of each customer’s journey. Building your customer service strategy for 2024 A great strategy starts with the right questions. How can you bring your data together? How can you unify the customer experience? And how can you equip your service teams to meet customers’ changing expectations while also serving the needs of the business? No matter how you answer those questions, your mission remains the same: to embrace the future of customer service so you can deliver what your customers demand. 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.

Ever been overwhelmed by the sheer volume of sales data you’re tracking — and confused by the metrics that matter? You’re not the only one. Research firmMcKinseyhighlighted this as a troubling trend: Too much data and no focus has made it difficult for sales leaders to reach clear “aha” moments that drive confident decisions and sustainable growth. Fortunately, there’s a clear path forward. To ensure you’re maximising the ROI of tools, teams, and customer relationships, zero in on sales key performance indicators (KPIs) that make the most of what you have while delivering recurring revenue: a combination of tried-and-true targets, like lead conversion rate, and those that measure long-term value, like customer and employee retention. Below, we give you everything you need to know about sales KPIs that ensure a healthy, productive, and growing business. What you’ll learn: What are KPIs in sales? Why are sales KPIs so important? What are sales metrics vs. sales KPIs? What are the most important sales KPIs? How do you track sales KPIs? What sales KPI dashboards should you use? What are KPIs in sales? Key performance indicators (KPIs) in sales are the metrics used to measure how closely the performance of a sales team tracks to predetermined goals and how this performance impacts the business as a whole. This includes metrics like average leads generated per quarter and deal conversion rate. Why are sales KPIs so important? Instead of different reps focusing on different metrics — or leaders eyeing a definition of success that sales reps aren’t thinking about — KPIs keep everyone aligned on the metrics that contribute to company growth. It’s important to note that KPIs themselves are not sales targets, but metrics that gauge activity with significant business impact. Sales leaders define target KPIs to ensure teams are tracking to specific revenue goals. Here’s an example: Joy’s Toys, a toy manufacturer, is focused on growth but doesn’t have a clear target KPI for lead generation that incentivises reps to keep theirpipelines full. Fast-forward a quarter or two and its revenue is “stop-and-go” with reps scrambling to find new opportunities after periods of focusing only on closing deals already in the pipeline. As a result, company growth stalls. Competitor Saul’s Dolls, on the other hand, has mapped out a clear path to revenue growth that includes target KPIs forlead generation, quota attainment, and customer retention. These are shared with every rep so they can prioritise their time and efforts on prospecting, nurturing, and closing deals with new customers whileupselling existing customers — and no critical sales effort is ignored. With this focus, Saul’s Dolls is more likely to hit or surpass its revenue goals. What are sales metrics vs. sales KPIs? Your sales KPIs have a close relationship with your sales and business goals. For example, if the overarching business goal is 1,200 sales in a year, the KPI might be 100 sales each month. (100 sales per month x 12 months = 1,200 sales) Sales metrics are any quantifiable measure of sales performance. This could look like the number of activities completed by sales reps, the number of leads in the sales pipeline, or anything else sales-related that can be measured. The key difference is that your sales metrics don’t necessarily have to connect with these broader goals. What are the most important sales KPIs? Historically, sales KPIs have focused on things like new leads in the pipeline, number ofclosed dealsper quarter, and individual quotas. These are still important, but they often hinge on unpredictable one-off sales. To ensure your company is generating long-term, predictable revenue and maximising ROI, it’s important to track both foundational sales KPIs and those that gauge the lifetime value of customer and employee relationships. Here’s a closer look at the most critical sales KPIs: 1. Annual contract value (ACV) What it measures: The average sales amount of a customer contract over the course of a year. Why it’s important:ACVhelps sales reps and managers identify opportunities for upselling andcross-sellingthat increase customer contract value and, ultimately, company revenue. If upselling or cross-selling are not possible (due to product portfolio, pricing structures, etc.), a low ACV may indicate a need for new customers that can drive revenue growth. How to calculate: (Total sales value of contracts in a year) / (number of contracts) = Average ACV 2. Customer lifetime value (CLV) What it measures: The value of all purchases, including upsells, cross-sells, and renewals, that a customer makes over the course of their relationship with your company. Why it’s important:CLVis a clear indicator of how successfully your team is building the kind of trusting, value-first, and loyal customer relationships that lead to upsells, cross-sells, and renewals, and, as a result, predictable revenue.If your CLV is on the lower end, then try going over the call transcripts from your best customers. Use AI to generate call summaries that identify what moved the deal forward, then use these same tactics in future deals. How to calculate: (Average purchase value per year) x (average number of purchases per year for each customer) x (average customer lifespan in years) = Customer lifetime value 3. New leads in pipeline What it measures: The number of new leads added to each rep’s pipeline during a single quarter. Why it’s important: Based on your conversion rates (four deals closed for every seven leads, for example), you will likely need a specific number of leads to hit sales targets. If reps’ lead count falls below your target KPI, it can be a sign that you need to spend more time on prospecting.A popular way to engage with more prospects is to upyour presence on LinkedIn. Follow potentialprospects, interact with them by liking and commenting on their posts, and then send a connection request. 4. Average age of leads in pipeline Whatit measures: How long leads remain in the pipeline without becoming a closed deal. Usually calculated per rep. Why it’s important: Reps knowa full pipelineis a healthy one — but only if leads are actively moving toward a sale. Stalled deals are a drain on rep time that could be spent moving more viable deals down the pipeline. If you see a trend in stale leads for a particular rep, consider examining their pipeline and remove leads unlikely to close.AI insights help to quickly identify the stallers in real time so you’re not spending hours scanning through your pipeline and analysing the data. How to calculate: (Total age of all active leads per reps) / (Number of active leads) = Average age of leads in pipeline 5. Conversion rate What it measures: Also known as win rate, this is the percentage of each rep’s leads that are converted to closed deals. Usually tracked by quarter, per rep. Why it’s important: If a single rep’s conversion rate is higher than the target conversion rate, that rep may be using sales strategies or processes that are particularly effective and can be operationalised for the entire sales team. If lower, you might need to fine-tune or streamline sales tactics to increase conversions.Call recording and analysistools, alongside regular one-on-onecoaching, can help. How to calculate: (Number of deals closed during a quarter) / (number of leads in the pipeline) x 100 = Conversion rate 6. Rep retention What it measures: Percentage of reps who remain in your organisation a set period of time after hire. Typically measured yearly. Why it’s important: A low rep retention rate can disrupt carefully nurtured customer relationships, which can result in lost upsells/cross-sells — or just lost customers. It can also mean more money spent onboarding reps hired to replace those who leave. When rep retention is high, customer relationships remain intact and team stability is maintained. How to calculate: (Number of total reps at the end of the year – new reps hired during the year)/(total number of reps at the start of the year) x 100 = Rep retention 7. Average rep ramp time What it measures: The amount of time it takes a rep to get from the first day on the job to first prospect outreach. Why it’s important: A quicker ramp time indicates yoursales enablement platformand training are effective, your tools and processes are intuitive, and you’re hiring qualified candidates.This results in faster sales and more engaged reps. If you find ramp time is slow, consider revisiting onboarding programs and sharing AI transcripts of winning sales calls with new reps, changing your tools, or streamlining yourprocesses. How to calculate: (Total time in days it takes all new reps to get from day one to first prospect outreach) / (total number of new reps) = Average rep ramp time 8. Referrals What it measures: The number of referrals for new customers from existing customers secured by each rep during a given quarter. Why it’s important: When your customers are over-the-moon happy with your products or services, they can serve as advocates, promoting you to prospects who otherwise may not be familiar with your brand. This makes it easier for reps to sell, leading to fastersales cyclesand more closed deals. 9. Customer retention What it measures: The percentage of customers who continue to buy and use your products/services. The inverse is churn rate — the percentage of customers who decide to stop buying or using your products/services. Why it’s important: While new customers add to revenue, they also takesignificant resources to secure. By watching customer retention and focusing on opportunities to upsell and cross-sell, you’re generating predictable revenue with a loyal customer base — and maximising ROI. If you see customer retention slip, you may need to revisit rep engagement strategies to ensure your team is prioritising existing customer relationships. How to calculate: (Overall number of customers at the end of the year – net new customers acquired during the year) / (number of customers at the start of the year) x 100 = Customer retention How do you track sales KPIs? A CRM uses customer and sales performance data to gauge progress toward sales KPIs. To help with interpretation, most CRMs offer visualisation tools or dashboards that can be customised with the KPIs most relevant to your business. The dashboard provides a clear picture of sales and company health so everyone from sales reps to leaders can make decisions that keep revenue flowing. What sales KPI dashboards should you use? To make sure everyone is in the loop, you need dashboards that provide high-level status updates to C-suite executives and more granular, deal-based dashboards for your reps. You don’t have to worry about updating dashboards manually — automation andAI-powered CRMscan pull data directly into customised dashboards to help you see progress toward KPIs without manual lift.Use these insights to improve performance, liketracking the fastest rep ramp times and checking in with those reps to see what worked that you could replicate. Here are the dashboards we recommend for how to track sales KPIs: For chief revenue officers (CROs) and sales leaders: Home “State of the Union” Dashboard: This provides an overview of top-level, year-to-date performance by target KPIs. It gives you the most important metrics for your business on one screen, including notable open and closed deals (usually the biggest accounts by value), top sales reps by quota attainment, and overall sales performance vs.forecast. For sales managers: Pipeline Dashboard: Get a snapshot of each rep’s pipeline with this dashboard, including average sales cycles, average deal amounts, and conversion rates. You’ll get clarity on the progression of deals in each pipeline and identify problem areas you need to address quickly. Team Activities Dashboard: See what your team’s doing to stay on top of active deals. Look at their total, completed, and overdue tasks and review each rep’s call and email logs.Dive deeper into conversations by looking at AI-generatedcall summaries. Use these summaries to identify customer sentiment and help move deals forward. Overall, this dashboard is key for monitoring rep engagement andsales processefficiency. For sales operations (sales ops) teams: Performance Dashboard: Drill into closed deals by region, account, or product so you can see what’s contributing to high deal win rates or slowing conversions. Once you know the “why,” you can recommend strategy shifts for your team. Stage Analysis Dashboard: This dashboard shows how deals across all reps are moving through the stages of thesales process, revealing bottlenecks and at-risk opportunities.Trends and patterns identified with AI can reveal opportunities for process improvements. For sales reps: Rep and Team Leaderboard Dashboards: This is an overview of individual rep and team performance data, includingsales quotasattainment, leads in pipe, pipe generation, closed/won deals, average sales cycle time, and sales activities. For more guidance, check out our article on key salesKPI dashboardsthat can help you hit or exceed your revenue targets. Home in on the sales KPIs that matter to you There’s no shortage of sales KPIs to track — but zeroing in on the right ones depends on what’s important to your business right now. First, identify overarching goals. For example, are you focused on driving growth or maximising revenue with existing resources and investments? Once you’re aligned on larger goals, you can select relevant sales KPIs to track and target metrics that will ensure you hit your broader business goals. Be sure to set up dashboards in aCRMaccessible to all teams so you can see a clear view of progress toward the goals you’ve defined.

Would you believe there are some companies that attribute at least 20% of their earnings (before interest and taxes) to their use of artificial intelligence?What do these leading AI companies know that you don’t? It’s a question worth exploring. Many companies are meandering through the strategy phase of AI, especially gen AI, and are focused on defining a vision that aligns with their business. High performers, on the other hand, are past that phase. They invest more and use AI more broadly. We’ll highlight some of these high performers, including Schneider Electric, Rossignol, and General Mills. But first… The big trend In a report on the state of AI, McKinsey identified the following traits of leading AI companies, high performers that distinguish themselves from the rest: Their AI efforts are geared less towards cost reduction and more towards creating new businesses and sources of revenue. They’re more than 5x as likely to say they spend more than 20% of their digital budgets on AI. They use AI more broadly, implementing AI in at least four business functions. They’re more likely to use AI in product and service development, risk modelling, performance management, and more. What you need to do now Learn from, and adopt when appropriate, best practices from AI high performers. They don’t always get it right but they’re first-movers who can offer invaluable insight. Follow an AI strategy playbook that helps you build a trustworthy foundation: for example, deciding on an approach, readying your technology, and enabling your people. Schneider Electric, Rossignol, and General Mills are three companies (although not cited by McKinsey) that are AI high performers. Here are their stories. Leading AI companies: Schneider Electric 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. It has since 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. 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. Get articles selected just for you, in your inbox Sign up now AI analytics and predictive modeling helped Schneider reduce inventory levels to avoid a glut while balancing its ability to efficiently deliver products like transformers, switches, and prefabricated substations. 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,” said Madhu Hosadurga, global vice president of enterprise AI, adding that it plans to use new AI capabilities – it invests tens of millions each year in AI –to pare an additional five percent of inventory.Read the full Schneider Electric story Leading AI companies: Rossignol Rossignol, a century-old French pioneer of ski equipment, expanded into summer sports in 2016 with the introduction of mountain bikes. The goal – inspire people to spend more time in the mountains year-round, while leveraging its customer data and AI to do so. “This is a unique turning point,” said CMO Gabriel Authier. “With AI, we are able to engage with our consumers seamlessly across seasons, getting them the right product that inspires them. We can frame the AI, and give it guidance. CRM, our data, together with AI, is going to create a virtuous circle that will elevate the customer experience.” Creating that virtuous circle is a work in progress, but Rossignol is getting ahead of the curve by partnering with LIKE.TG to connect its myriad data touch points, and eventually infuse gen AI into marketing, commerce, and service for better efficiency, personalisation and customer experience. What might that look like in the future? Consider this example of Rossignol’s new Super Heretic bike, from a recent demo: When adding the bike description to its ecommerce site, merchandising teams could use Einstein’s gen AI capabilities to generate that content, based on past product descriptions and public information. Humans would vet, edit, and publish. Gen AI can instantly translate the content for its global audience, adhering to Rossignol’s brand voice and cultural phrasing so nothing is lost in translation. In marketing, teams may instantly access customer engagement data from within messaging app Slack, and create email campaigns. Using AI prompts, marketers look forward to generating suggested emails and subject lines based on historical data. For example, they could use a prompt like “Which three subject lines have had the biggest open rates?” to create a future high performing email. In service, gen AI will eventually answer its customer questions, after human vetting for accuracy, completeness, and brand voice. Because it has access to public data, gen AI could also provide information on local events and activities. Watch the full Rossignol story (including that cool demo) Leading AI companies: General Mills General Mills already had rich purchase and behavioural data from its online recipe sites and Box Tops for Education donation program. Capturing millions of recipe views and receipt scans, it connected and acted on data to recommend relevant content based on food preferences, diet, geography, and household composition. All that data gets dropped into a central repository. AI and automation analyse the data, make personalised recipe recommendations, and predict an appropriate email send cadence. The other upshot? It can more accurately segment audiences and send relevant content like quizzes or free samples based on past purchases and favourite recipes. This has helped the company triple consumer engagement, increase known site users 170% year over year, and even save millions in paid media. Its Pillsbury and Betty Crocker brands have also experienced a 40% increase in “buy now” clicks in its user content. Read the full General Mills story It’s never too late In less than one year (thanks to generative capabilities), AI has risen from a topic discussed mainly among the IT set to a top priority for company leaders. Given the constant stream of news stories, new tech tools, and emerging use cases for gen AI, you’d be forgiven for thinking every company is running their entire business with it. But they’re not. As McKinsey reports, “we’re in the early innings of gen AI,” and the share of organisations that have adopted AI overall has remained steady over the last few years. In fact, less than a third of respondents say their companies have adopted AI in more than one business function, suggesting that its use remains “limited in scope.” That said, those just jumping on the gen AI train have plenty of time to experiment, test, and learn from trails blazed by early-adopting high performers.

When it comes to long-term business success, customer loyalty is key. In today’s global marketplace, customers have become digital-first shoppers with thousands of suppliers at their fingertip. They can purchase any product imaginable in a heartbeat and being top of the shortlist when customers consider a purchase has never been more impactful, especially for SMEs. The key to making it there? Customer loyalty. What is customer loyalty and why is it important? Customer loyalty epitomises how customers feel about your brand, how willing they are to make a repeat purchase and whether they would recommend you to others. In today’s hyper-connected world, customer loyalty is much more important – and harder to earn. Customer expectation is at an all-time high and customers are more willing to switch brands if they have a bad experience. They also share their experiences, good and bad, more frequently. In the trust-based economy what buyers tell their friends has become key, and business success balances on customer satisfaction more than ever before. Loyal customers are also the biggest spenders. In fact, a 5% increase in customer retention has the power to increase profit by five times that amount. Just as significant, existing customers are 50% more likely to try new products and spend more on average than first-time buyers. Customer loyalty drives the bottom line and the digital economy has unlocked its impact. Quantifying loyalty The way customers shop online today has also opened up new lines of communication. More customers interact with brands directly rather than buying via third-party channels and personalising these interactions is an opportunity to make customer relationships more meaningful and appealing. With the right tools, your SME can leverage this connection and gain valuable insight into existing customer loyalty. As a result, you’ll also be able to develop tailored strategies to boost loyalty based on what your customers value most. The Net Promoter Score (NPS) Actionable data – and lots of it, is key to understanding your customers and increasing loyalty. Many successful businesses use the Customer Retention Rate (CRR) as a tool to gauge whether customers are choosing to stay with them. However, the CRR omits the human factor – how customers feel about your brand and your products. But a more complete picture can be obtained by using the Net Promoter Score (NPS), a simple, two-minute survey that transforms customer sentiment into an actionable metric. By asking customers how likely they are to recommend your brand to a friend on a scale of 0 to 10, the NPS subtracts the percentage of “Detractors” (scores of 6 and under) from the percentage of ‘Promoters’ (scores of 9 and above) to calculate a comparable loyalty benchmark. Simple, right? But the NPS is deceptively powerful, and high NPS scores are proven to correlate directly with growth. In fact, Harvard Business Review called the NPS the single best predictor of growth and the one number you need to grow as a business. While it provides a strong benchmark, the NPS alone is not complex enough to understand where loyalty originates and how to improve it. To overcome this, consider adding an open-ended question to your survey like “how can we improve your experience?”, or “what do you like most/least about our product?” By asking customers what matters most, you can develop a loyalty strategy that targets real needs, rather than blowing the budget on perks that don’t rate. Something to keep in mind is that the NPS varies greatly across industries. For example, if you’re in the car rental business, a score of 15 may be stellar – but in the streaming industry, the same score could be abysmal. Get articles selected just for you, in your inbox Sign up now Understanding customer needs Perhaps your SME already has solid NPS scores. Great! But now what? While the NPS is a starting point to benchmark future initiatives, the real prize is understanding what matters most to customers. Some are loyal to specific products and their price, quality or ease-of-use. For others, these perks play no role whatsoever; they are loyal to your brand, its reputation or image. Your customers are from all walks of life and won’t fit smoothly into one category or another. However, to develop a stellar strategy for boosting loyalty, it’s important you understand what drives your customers. A few basic categories your customers may fall into include: Satisfied customers: feel they receive high quality products Price-loyal customers: feel they receive the best value for their money Loyalty-programme customers: enjoy receiving freebies through loyalty memberships Convenience customers: enjoy the ease of a product or service Brand-loyal customers: love the company and its products By using the NPS to understand customer composition, you can set up a strategy and solutions that meet their specific expectations. Do your customers favour quick and easy fulfilment? Offer overnight shipping for the convenience crowd. Is price important? Consider special promotions and discounts for bargain-hunters. A sound loyalty strategy identifies key issues with tools like the NPS to make sure perks and promotions gel with what customers really want and are meaningful. Key loyalty builders Today, building customer loyalty goes beyond price and individual perks alone. To feel a connection, customers also want personalised experiences and new ways to engage. They want to feel heard across all the channels they use. In short, they want to feel seen as a real human, rather than a random entry in a spreadsheet. Our LIKE.TG State of the Connected Customer report reveals key insights on how to build loyalty by focusing on customer interaction. To boost loyalty: Offer Loyalty Programmes More than 50% of customers see companies as too impersonal today. Loyalty programmes build a more personalised connection by rewarding customers with exclusive events, early access and members-only benefits. Provide Multi-Channel Support Lack of support is a notorious loyalty killer. Customers want to be helped in ways they’re most comfortable with, especially when they have an issue. Research shows that customers place the most value on quick and easy points of contact and documentation, like well-maintained FAQs and real-time messaging support. Offer Different Ways to Engage Customers often welcome opportunities to connect outside formal channels. In fact, 72% of customers expect vendors to personalise engagement to their own needs. Online communities, a social media presence or a well-maintained blog offer customers more choice to engage with you. Build Trust by Being Generous Showing Gratitude 54% of customers don’t believe companies have their best interests in mind – 94% say trust is essential to become a loyal buyer. Sometimes, that means being generous, and incurring additional costs now for better engagement later. Forgiving return policies and warranty programmes build trust and create more loyal customers in the future. Evolve Your Business Over Time More than 50% of customers actively seek out the most innovative brands – and continuous evolution is key to keeping customers engaged and buying. By piloting new approaches, like user-generated content or gamification, businesses can innovate beyond the scope of their product and keep interacting interesting. The universal approach: customer experience While each of these strategies address some customers more than others, they all feed into a loyalty builder that appeals to almost everyone. Customer Experience – aka, the customer journey, encompasses everything including first contact, choosing a product to after-sales. In fact, 80% of customers say that they consider customer experience just as important as the product itself. What’s more, 70% say they would pay extra for a great experience. A great customer experience enables customers to engage with the brand and product at eye-level, at every stage with seamless handoffs along the way. Providing reliable support and letting the customer know their feedback matters – by way of the NPS for example – builds a two-way connection that invests customers in your brand in ways simply using the product cannot. The stark reality is that 67% of customers have recently switched vendors for a better experience. And the heyday of customer experience is only beginning: the younger the audience, the more likely they are to switch. Creating experiences that last Entrepreneur Tony Hsieh said: “Customer service shouldn’t just be a department; it should be the entire company.” Building enduring customer loyalty boils down to a simple but powerful truth: the customer comes first. By gauging loyalty with the NPS, using a data-driven approach to identify customer needs, and addressing them in a tailored and value-focused way, you can build lasting relationships, cut costs, drive sales and supercharge your brand. The reality is that it’s not the customer’s job to remember your business. But it is your job to ensure the customer has an outstanding experience that inspires them to become a loyal buyer and champion your brand.

We may live in the time of TikTok, where an unknown number calling your phone strikes fear, but in my 13 years of training sales teams, I’ve found nothing is more impactful than the cold call. In fact, cold calling accounts for up to50% of new deals, according toDale Carnegie Training. The hard truth, though, is that cold calling can be painful. Many sellers avoid it whenever possible, fearingconfrontation and rejection. But with the right cold calling tips, it’s actually easier than you think. With a few simple strategies — most rooted in solid research and planning — you can make successful cold calls without getting cold feet. What is cold calling andwhy are cold calling techniques still important? Cold calling is a type of sales solicitation from a salesperson to a prospect who has never interacted with the company before. The goal is to develop a business relationship with a new customer and, eventually, close a sale. While it can feel intimidating, the right cold calling tips can help you feel more confident going into each conversation. And it’s worth it. Using best practices for cold calling — rooted in solid research and planning — can help your reps turn successful cold calls into warm leads. Cold calls are also an effective prospecting method when you compare it to email or social media. Prospects can simply delete your emails and scroll past your social media posts, but a voice on the phone is immediate. You gain the opportunity to get real-time responses and address any concerns, gathering a lot of information in a short period of time. 10 cold calling tips that will help you land new leads The perfect cold call starts with preparation and research.Doing your homeworkwill help you tailor your message and communicate effectively. Use the following cold calling advice to break through initial fears and find success. 1. Research ahead of your call Before calling your prospects, research their biggest pain points and consider how your products or services can help them solve nagging problems. To streamline your research,John Barrows,CEO of JB Sales, recommends segmenting your list by industry and title. Then you can use AI sales tools, like LIKE.TG’sautomated research assistant, to speed up your research. Some can even pull data about your prospect into your CRM. Once you have a basis of industry knowledge, get to know your target companies. Review their websites (especially their blogs), their social media accounts, and news articles about their companies to see if they’re facing any challenges. Then, check yoursales engagement platformto see if the person you’re about to call has clicked through any emails and engaged with content. That may give you clues as to what they’re most interested in so you can better frame your product features as solutions they need right now. “For top-tier target accounts, segment about one hour a day to do the research,” said Barrows. “Spend some time on your prospect’s LinkedIn profile and find something specific that you can reference to make a direct connection to the value your service can provide.” 2. Collect case studies that show the success of your product You can brag about your product or service all you want, but your customers make the most compelling case for you. They’re seen as more relatable and objective. That’s why you need testimonials and case studies that speak to the value of your product and how it solves your prospect’s pain points. Additionally, collecting any available data on ROI or customer performance can help you quantify the benefits of your product or service.Have this information in hand, refer to it, and be ready to share it in real time during and immediately after your call. 3. Draft a call intro, not a whole script (with the help of AI) Once you’ve completed your research, draft a quick-hit intro script that ties together basic info about your company with an open-ended question. Doing so allows you to collect more information you can use to frame your solution.If you often struggle with wording and you havegenerative AI techbuilt in to yourCRM,use it for a spark of inspiration to get the language right. Cold call script example Plan tocreate a new script for each prospect.No two are alike, after all. A personalised approach will help you keep the call feeling genuine and focused on the help you can provide. Keep your intro short – less than 30 seconds. Barrows suggestedincluding the following core elements: A quick intro about you and what your company does – 10 secondsHi, Taylor! I’m glad we’ve connected. Jessie here at [company name and description]. A point of connection, like a referral name or something you share in common, to help build rapport – 10 secondsI saw you at the recent sales training conference and wanted to connect in person, but didn’t have the chance. So, I thought I’d give you a call. A note about why you’re calling, highlighting a key pain point or new valuable information for the prospect (this is where your research really comes in handy), followed by a prompt to gather more information – 10 secondsWe’ve seen lower quota attainment in the XYZ industry, and we’ve been working closely with others in the industry to [do something positive with our product or service]. I’d love to hear about what [company name] has been doing to overcome this challenge. If now isn’t a good time, can we schedule something later this week? This is really all you need to draft. The rest of the call will depend on how the prospect responds to your open-ended question, like, “What specific pain points or bottlenecks are you looking to address?” Make sureyou listen carefullyand ask more questions. Try to identify three key factors in their decision-making: time (when they need a solution), money (how much they can afford), and impact (what a perfect solution would look like, preferably framed by metrics they’re trying to hit). 4. Call at the best time – often in the middle of the day Recent layoffsmean smaller teams have to do the same amount of work with less resources. This may make it harder to get in touch with your prospects. To maximise your chance of getting them on the phone, avoid calling during busy parts of the work day — typically first thing in the morning and the end of the day. Calling in the middle of the day is a good bet. Give yourself even better odds with a little social reconnaissance. Check to see if your contact has obligations, like a major conference to attend, to make sure you’re reaching them when they’re able to pick up the phone. If calling during “best” hours doesn’t work,send an emailand schedule a time to call. Get articles selected just for you, in your inbox Sign up now 5. Practice (and breathe) before the call Armed with the best strategies for cold calling, getting into the right mindset is critical. If you rush in, anxious about the outcome, you’ll likely come across as frenzied. To ensure you’re confident and comfortable, do three important things: Anticipate common questions, especially negative ones, and plan quick answers that help direct the conversation in a positive direction. Practice your pitch in front of a mirror or, better yet, with a colleague. Take several deep breaths to calm your nerves before you pick up the phone. While that may sound simple, it doesn’t need to be complicated. Reps have used these cold calling tips for years, and nothing beats them. 6. Speak slowly and clearly — then listen With script in hand, remind yourself to take the first 10 seconds to build rapport. This can be a brief tidbit that sets your prospect at ease and helps you connect on a personal level. Smile when you speak — it comes through in your voice. Then, tell them you’re happy they answered and ask them an open-ended question. Speak clearly and slowly to be sure you’re understood. You want the prospect to know who you are, why you’re a standout, and what you have to offer. “Ask specific questions that show you know what you’re talking about,” said Barrows. “You can simply ask: ‘We’re working with other executives in your industry to address these three priorities: 1,2,3. How do these align with your priorities and what other ones are you specifically dealing with?'” After you ask your open-ended question, listen — really listen. Take notes as the prospect talks to help you frame follow-up questions. When there’s a natural pause,ask questions related to your research as well as anything that might help you collect information on time, money, and impact (see tip #3 above).People liketalking about themselves, so give them the opportunity. The secret to cold calling success? Authenticity Cold calling isn’t just a numbers game. Being authentic can help you turn cold calls into connections quickly. Learn more on Trailhead, the free online learning platform from LIKE.TG. Take your first step +200 points Module Cold Calling for Sales 7. Don’t mention your product until the end of the call Collected enough information to fully understand your prospect’s needs? Now, it’s time to plant a seed. As you get ready to close the call (try to keep it to 15 minutes), connect one of the pain points mentioned with something you have to offer — a product feature, a low-cost subscription, or increased ROI. Let them know you have a viable solution that can be tailored to their needs. Here’s the catch: Don’t give away the store. Use this connection as an opportunity to ask for a follow-up meeting so you can explain your solution further.After all, this isn’t asales call. It’s a cold call. 8. Be clear about next steps Many sellers rightfully put energy into gathering information or making a pitch while cold calling.Unfortunately, many also forget to plan how they’ll close out their cold call in a way that moves things forward. Before your call, plan for the three to four most likely outcomes and next steps for each. This ensures you will keep the deal moving forward. For example, if the prospect seems interested in product features,suggest scheduling a demo to walk them through your product. If they’re wavering on the real impact of your solution, you can send them an email with case studies and ask them for a good time when you can follow up. Before you hang up, make sure your next step is clear — and ideally on the prospect’s calendar. You can even summarise next steps at the end of the call to make sure there’s no confusion. 9. Have a plan of action if they don’t answer “Reps always ask me whether they should leave voicemails anymore since they almost never get a callback. My response is yes — as long as they are good ones,” said Barrows. What makes a good cold call voicemail? One that offers value. Even if you don’t get a callback the first time, you’ve used an opportunity to build name recognition and help your prospect associate it with something helpful. When planning what you’ll say during a voicemail, consider the research you did to inform what you’d say during a live call. Then use these do’s and don’ts to leave an effective cold call voicemail that stands out. DON’T open with your name. DO start with a greeting that’s immediately followed by why you’re calling, focusing on helpful information you’d like to share. DON’T ramble. DO keep your voicemail to a 30-second maximum. DON’T sell. DO try to pique their curiosity. 10. Take time to identify highs and lows after the call One of my biggest cold calling tips is to take some time after the call for a self-assessment,identifying what went well and what didn’t quite land.The more cold calls you make, the more data you have to learn about what works and what doesn’t — but that’s only possible when you take the time to analyse your calls. Make a note of call highs and lows as soon as you hang up. The good news: This only needs to take a few minutes following each call, especially if you use AI.AI for salestools, like Sales Cloud Einstein, not only generate short, actionable call summaries, but they also offer suggestions on next steps. As you continue to analyse your calls, you’ll identify patterns that can help you improve your cold calling scripts and make it easier to approach prospects in the future. Take the plunge into cold calling Cold calling may not be the newest technique in the sales game, but it’s still an effective way to generate new business — if you do it right. By doing your research, building rapport, and giving your prospect a chance to share their problem in detail, you’ll make it easy to position your product as the ideal solution. What are you waiting for? Pick up the phone and start turning those calls into customers.

Are you overwhelmed by generative AI yet? The questions about AI businesses need to ask themselves –about technology, skills, privacy, data, and organisational requirements, to name a few –are endless. It can be hard to know where to begin, and the most pertinent AI questions to ask, before diving in. “For a lot of our customers, this is about getting going for the very first time with AI,” said Marc Benioff, CEO of LIKE.TG. “They may have been using predictive AI, machine learning or even deep learning. Now they’re exploring this next generation of AI to understand how they’re going to take productivity to the next level.” Demand and potential are both great. But so are the risks. We’re here to help. The employee view 56% of workers in a recent survey said they believe generative AI (gen AI) will transform their roles. 65% believe gen AI will allow them to focus on more strategic work. Workers believe generative AI will save five hours a week. The exec summary Gen AI will add up to $4.4 trillion to the global economy annually. 75% of the potential value from gen AI is concentrated in four functions: customer operations, marketing and sales, software engineering, and RD. Gen AI, with other automation technologies, could add up to 3.3 percentage points annually to productivity growth, but companies will need to support employees along the way. Your next move Ensure your AI technology meets company guidelines and industry regulations. Develop a strategic plan with specific use cases. Ask the right questions about AI, starting with… How good is our data? Generative AI promises to significantly reshape how you manage your customer relationships, but it requires data that is accurate, updated, accessible, and complete. Why is this important? You may do something differently this quarter than you did last quarter, based on the latest data. But if your data is outdated or incorrect, that’s what the AI will use. When training your models for generative AI, you should first ensure data excellence from top to bottom. To get your data house in order, remove duplicates, outliers, errors, and other things that can negatively affect how you make decisions. Then connect your data sources — marketing, sales, service, commerce – into a single record, updated in real time, so the AI can make the best recommendations. McKinsey recently wrote, “Companies that have not yet found ways to harmonise and provide ready access to their data will be unable to unlock much of generative AI’s potentially transformative power.” How do we establish trust? Trust –that you will protect customer data and use AI ethically –is at the heart of how widely and successfully businesses and customers will embrace generative AI. Ask yourself, is your technology partner building AI safeguards into the fabric of their systems and apps? Large language models (LLMs), the computer programsupon which AI algorithms are based, contain huge amounts of data but they lack safeguards, controls and privacy features. Companies can leverage AI’s productivity gains without giving away data. Get articles selected just for you, in your inbox Sign up now Trust in AI data privacy requires special safeguards including data masking, toxicity detection, data grounding, zero retention, and more. These guardrails protect data and help ensure ethical use, boosting chances of AI success. Do we need to reorganise our company around AI? Research featured in Harvard Business Review found that AI initiatives face formidable cultural and organisational barriers, and that companies need to align their culture, structure, and ways of working to support and scale AI. Ask yourself, do all your stakeholders share that responsibility? Generative AI is such a game changer that some companies are establishing cross-functional AI task forces to determine how to proceed. Experts suggest establishing an AI governance committee to, among other things, guide development teams and set standards for explainability. That is, determining how and why AI makes the recommendations it does. Schneider Electric, for one, has formalised an AI program under a chief AI officer and scaled it to every corner of the company. It 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. “Every use case — and we have AI use cases in almost every function — has people from both the AI Hub and business,” said Madhu Hosadurga, global vice president of enterprise AI at Schneider. Do we have the right skills? AI is evolving at such a furious pace, the answer to this question (for most companies) is probably no. A recent survey found 67% of global business leaders are considering using generative AI, but roughly the same number of IT leaders say their employees don’t have the skills to use it. Being an AI-first company (like being digital-first or mobile-first before) requires a close evaluation of your talent. First, you need to determine your current capabilities relative to what you want to accomplish. Identify the gaps, then prioritise building those AI skills. Of course, these will vary by your company’s industry and specific needs. You will likely have to develop a hiring plan to acquire talent, and train workers to use generative AI. You can do the latter by providing access to on-demand learning for critical skills, incentivising workers to learn new skills and prioritising upskilling as part of the job. “Change management starts with people,” said Clara Shih, CEO of AI at LIKE.TG. “It’s going to take all of us to reskill and learn about these new capabilities.” What gen AI terms do I need to know to carry on a conversation? You don’t need to be a software engineer or data scientist to understand gen AI or speak with authority about it with technical people. But business leaders should be able to think about AI holistically, including benefits and risks, where it fits into the company’s culture, mission, and what type of governance and infrastructure it requires. Business leaders can’t help lead AI programs to success if they can’t engage with the tech teams. We’ve put together a list of the most essential AI terms that will help everyone in your company — no matter their technical background – understand the power of generative AI. Each term is defined based on how it impacts both your customers and your team, a crucial element in understanding the power of AI. Gen AI technologies (and adoption) are growing extraordinarily fast. As it informs more business decisions and transforms your relationships with customers, leaders at all levels must understand its potential, its use cases, and its risks. How do you do that? Start by asking the right questions about AI.

Predictive analytics stands at the forefront of data-driven decision-making, striving to answer a pivotal question – “What could happen next?” Within the realm of business data science, predictive analytics has emerged as a key player, evolving hand in hand with the growth of big data systems. This evolution has expanded the horizons of data mining, enabling organisations to unearth invaluable predictive insights. The convergence of big data and advanced machine learning techniques has been pivotal in unlocking the full potential of predictive analytics. Understanding Predictive Analytics Predictive analytics is the art of employing data to make well-informed forecasts about future outcomes. This sophisticated process encompasses data analysis, machine learning, artificial intelligence, and statistical models to uncover patterns that can help predict future behaviours and events. Leveraging historical and current data, organisations can anticipate trends and behaviours, ranging from seconds to years into the future, often with remarkable precision. The Inner Workings of Predictive Analytics Predictive analytics is not a crystal ball; instead, it’s a structured methodology that data scientists utilise to make predictions based on data patterns. The process can be distilled into five core steps: Problem Definition The journey into predictive analytics commences with a crystal-clear definition of the problem at hand. Whether it’s detecting fraudulent activities, optimising holiday season inventory levels, or forecasting potential flood levels during severe weather, a well-defined problem lays the foundation for choosing the appropriate predictive analytics method. Data Acquisition and Organisation Organisations typically possess vast data repositories, accrued over time or continuously streaming in through customer interactions. Before predictive analytics models can be constructed, data sources must be identified, and datasets should be meticulously organised, often within a data warehouse. Data Pre-processing Raw data seldom arrives in a form ready for predictive analysis. The pre-processing stage involves cleansing the data to eliminate anomalies, missing data points, or extreme outliers, which may result from input or measurement errors. Predictive Model Development Data scientists employ a repertoire of tools and techniques to build predictive models, selecting the most suitable approach based on the problem and the nature of the dataset. Common predictive model types include machine learning algorithms, regression models, and decision trees. Validation and Deployment Once a predictive model is created, it’s essential to validate its accuracy and fine-tune it if necessary. Once satisfactory results are achieved, the predictions can be disseminated to stakeholders through various means, such as applications, websites, or data dashboards. Predictive Analytics Techniques Predictive analytics employs various techniques to extract insights from data and make forecasts. Here are some key techniques often used in this field: Regression Analysis: Regression is a statistical analysis technique used to estimate relationships between variables. It is beneficial for identifying patterns in large datasets and understanding the correlation between different inputs, such as how a price increase might affect product sales. Decision Trees: Decision trees are classification models that categorise data into different groups based on specific variables. They are highly valuable when trying to comprehend an individual’s decision-making process. The tree-like structure represents potential choices, with each branch leading to a specific outcome. Decision trees are straightforward and work well when dealing with datasets that contain missing variables. Neural Networks: Neural networks are machine learning methods that model complex relationships within datasets. They excel at recognising intricate patterns and are most effective in predicting nonlinear relationships when no known mathematical formula exists to analyse the data. They are often employed to validate the results of decision trees and regression models. Applications of Predictive Analytics Predictive analytics finds applications across a wide array of industries, offering opportunities to streamline operations, boost revenue, and mitigate risks. Some notable use cases include: Fraud Detection: Predictive analytics monitors real-time activities to detect anomalies that may signify fraud or vulnerabilities. Conversion and Purchase Prediction: Businesses can leverage predictive data to retarget online ads and reach visitors with a higher likelihood of converting and making a purchase. Risk Reduction: Credit scoring, insurance claims assessment and debt collection utilise predictive analytics to assess and predict the likelihood of future defaults. Operational Improvement: Predictive analytics models help companies forecast inventory needs, manage resources more efficiently, and optimise operations. Customer Segmentation: Marketers use predictive analytics to divide customer bases into specific groups, enabling more tailored content and forward-looking decisions. Maintenance Forecasting: Organisations can predict when routine equipment maintenance will be needed, allowing them to schedule it proactively and prevent issues or malfunctions. In the age of data abundance, predictive analytics has become an indispensable tool for businesses looking to gain a competitive edge and foresee what lies ahead. With the ability to make data-driven predictions, organisations can make informed decisions that propel them towards success. Enhancing Predictive Analytics with Advanced Modeling Now that we’ve explored the fundamentals of predictive analytics, let’s turn our attention to how advanced modeling techniques can elevate the capabilities of predictive analytics. Predictive models are the linchpin that enhances data-driven decision-making and unlocks new insights into what the future holds. The Role of Predictive Models Predictive models are the backbone of predictive analytics. They are the mathematical or statistical algorithms that crunch through data to identify patterns, relationships, and associations. These models allow predictive analytics to make informed predictions about future outcomes. Here’s how they fit into the predictive analytics workflow: Data Input: Predictive models require high-quality data as input. This data typically includes historical information relevant to the problem at hand. Whether it’s customer behaviour data, financial metrics, or sensor readings, the data forms the foundation upon which predictive models operate. Feature Selection: Within the input data, specific features or variables are selected as the building blocks for predictive modelling. These features can range from customer demographics to product attributes, depending on the nature of the problem. Model Building: The predictive model is the heart of the operation. It’s here that data scientists apply various algorithms and techniques to uncover hidden relationships and make predictions. The model is trained on historical data to learn from patterns and behaviours. Prediction: Once the model is trained and validated, it can be used to predict future outcomes. By feeding it with new data, the model leverages its learned patterns to make forecasts. These predictions can range from sales forecasts to equipment failure predictions. Evaluation and Deployment: The performance of predictive models is closely monitored and evaluated to ensure their accuracy. Once validated, the models are deployed to make predictions accessible to stakeholders through various platforms. Types of Predictive Models Predictive models come in various forms, each tailored to specific types of problems and data. Here are some common types of predictive models: Regression Models: These models are ideal for predicting continuous numerical values. Whether it’s forecasting product prices, patient recovery times, or energy consumption, regression models are the go-to choice. Classification Models: When the outcome of interest is categorical or binary, classification models come into play. They are used for customer churn prediction, sentiment analysis, and quality control. Time Series Models: Time series models specialise in forecasting future values based on historical time-ordered data. They are essential for predicting stock prices, weather patterns, and seasonal sales trends. Clustering Models: Clustering models group data points into categories based on similarities. They are instrumental in customer segmentation, anomaly detection, and recommendation systems. Machine Learning Models: A diverse range of machine learning models, including decision trees, random forests, support vector machines, and neural networks, offers unparalleled flexibility. They are applied across various predictive tasks, from image recognition to natural language processing. Applications of Advanced Predictive Models The applications of advanced predictive models are boundless, spanning numerous industries: Finance: Predicting stock market trends, optimising investment portfolios, and identifying fraudulent transactions. Healthcare: Forecasting disease outbreaks, patient readmission risks, and personalised treatment plans. Marketing: Enhancing customer segmentation, predicting market trends, and optimising digital marketing campaigns. Manufacturing: Preventing equipment breakdowns through predictive maintenance, optimising supply chains, and ensuring product quality. Retail: Predicting customer demand, optimising inventory, and personalising the shopping experience. Transportation: Optimising logistics, predicting maintenance requirements, and managing traffic flow efficiently. By incorporating advanced modelling techniques into predictive analytics, organisations can harness the full potential of their data. Predictive models are the linchpin that elevates data-driven decision-making, enabling businesses to unlock new insights and foresee the future.

As digital technology advances and plays an ever-bigger part in our daily lives, businesses have to keep up with the times. From a broad perspective, it’s simple: Keep up or fall behind. Understanding what digital transformation means to your business requires a bit more exploration, however. What are the drivers in digitalisation and digital transformation? The root of any change in business starts with customers. It has to: Customer happiness is how you win in business. Modern customer expectations are being driven by largely digital technology and digital innovations. The always-connected customer is always seeing new possibilities. When they see new things elsewhere, they want them from you, too. And if you can’t offer them, they’ll find someone else who can. The digitally connected world makes it easier than ever for customers to comparison shop and move from one brand to another, often with minimal effort required. Digital innovation shapes business across all industries. Digital transformation impacts every industry. Whether your business generates revenue through client services, digital media, or physical goods, technological innovations can transform your means of production, distribution, and customer service. Depending on your business, your customer could be a consumer or a business-to-business (B2B) client. Let’s extend our perspective to also include your employees. As we’ll talk about in a moment, employee expectations are being driven by their own consumer experiences, particularly when it comes to digital innovation in the workplace. Customers expect digital technology and innovation. Today’s customers are connected and empowered by the digital era. They’re connected 24/7, and increasingly want and expect that same around-the-clock access to the companies they do business with. The key drivers behind this change in consumer behaviour? Mobile devices and social media. Over half of customers surveyed for LIKE.TG’s report “State of the Connected Customer” said that technology has significantly changed their expectations of how companies should interact with them. More specifically,73% of customers prefer to do business with brands that personalise their shopping experience, according to the Harvard Business Review. LIKE.TG’s researchalso reports that 57% of consumers said it’s absolutely critical or very important for companies they purchase from to be innovative. Otherwise, they might just look for new companies to buy from: 70% of respondents said new technologies have made it easier for them to take their business elsewhere. Employee empowerment drives digital solutions. The Apple iPhone is often mentioned as a key driver in the adoption of consumer technology in the workplace. The iPhone wasn’t originally marketed to businesses, but it quickly became popular, to the point that corporate IT departments had to accommodate employees wanting to use iPhones in lieu of other devices. Once a few big employers opened their doors, acceptance of iPhones in the enterprise spread quickly. The iPhone disrupted the status quo for technology adoption in the workplace. Instead of IT leaders telling employees which approved devices to use, enough workers asked for iPhones that IT departments eventually acquiesced. This trend continues today, with more “consumer-grade” technologies making their way into the workplace. Maybe even more noteworthy is the flip side of the trend: Enterprise software has started taking design and functionality cues from the consumer world. Long live ease of use! Digital-first employees are connected employees. Millennials — more than any other subset of the workforce — are proponents of the digital-first mentality. Having come of age on PCs, consumer electronics, and phone apps, millennials expect to enjoy the same powerful, easy-to-use digital tools in the workplace as they do in the rest of their lives. Digital transformations apply this digital-first state of mind to empower all your employees. In the same way that consumers look for businesses ready and willing to connect with them 24/7 via social media and other digital channels, today’s employees thrive in environments that make it easy to collaborate, access information, and work anytime and from anywhere. Digitalisation is a powerful ally of the empowered employee. For small businesses, the upside to building a digital business can be game-changing. Not only is digitalisation key to meeting customer expectations and empowering employees, but it can also help small businesses do more with less. The efficiencies afforded by going digital — having one comprehensive database shared across your entire business, leveraging customer data to create personalised messaging and service strategies, enabling employee connectivity from mobile devices, for example — can free small teams up to spend more time winning and keeping new customers. Bonus: When you build digitally from the beginning, it’s much easier to scale systems as your business grows. Digital innovations are transforming industries. Employees aren’t the only ones benefiting from easy-to-use, always-on access to information in the workplace. Machines themselves are getting smarter, too. Artificial intelligence (AI), the Internet of Things (IoT), cloud analytics, and sensors of allsizesand capabilities are transforming manufacturing, production, research — virtually all facets of business across all industries. The examples are never ending. Digital innovations like AI and the IoT are driving all manner of advancements in the production of everything from consumer goods to cars and trucks. Optimised manufacturing processes adapt to changing consumer demand. Cloud-based software affords real-time visibility into supply chain logistics. Customer experience mapping powered by machine learning surfaces key insights to help product planners, marketers, and budget makers alike do their jobs better. Together, these and many more innovations like them are changing the way we do business, from every conceivable angle. Why do businesses need to transform in the digital era? Digital transformation is business transformation. It’s a transformation that’s being driven by the basic desire to make work better for everyone, from employees to customers. The drivers we just walked though are some of the biggest reasons behind the massive changes rippling through the business world right now. Add to that the need every business has to compete for — and win — customers. If your competitors are leveraging digital transformation to streamline production, expand distribution, build a better workplace for employees, and improve the overall customer experience, you’d better up your game, too. But how are these changes taking shape? What does digital transformation look like in practice, across different parts of an organisation? Let’s take a look at some examples. Overview: What Is Digital Transformation?Chapter 2: Examples of Digital TransformationChapter 3: How to Digitally Transform Your Business

A digital transformation is a complete business transformation. It’s crucial to keep this in mind if you’re seriously considering transforming your business. It’s not just about updating IT systems and apps.It’s a cultural shift, and a reimagining of all of your company’s processes and ways of doing things. As we said previously, small businesses — even those just getting off the ground — can leverage a digital transformation mindset to build digital first into their company culture. What better way to imagine how digital innovation can benefit customers than by being a digital native yourself in all aspects of growing and running a business? Before we get into how to build a framework for your digital transformation, let’s first go through some of the signs that your business is, in fact, in need of transforming. Signs that a business needs a digital transformation. Signs that your business is in need of a digital transformation can appear across different parts of your organisation. They may not scream “It’s time to go digital!” or “Why aren’t you on Instagram?” Instead, they could manifest as a diverse set of business problems. If one or more of the items on our checklist rings true, it might be time to think seriously about developing a digital transformation strategy. You’re not getting the referrals that you used to get.More and more referrals are now shared online, via social media, apps, email, and messaging. If your business doesn’t have a strong, easy-to-share digital presence, you could be missing out on referrals. Repeat business isn’t repeating like it used to.Customers not coming back to do business with you again isn’t necessarily a sign that your products and services aren’t measuring up. Losing repeat business could be due to competitors’ promotions, lack of follow-up communication on your part, or any number of other reasons. A digital transformation of your messaging strategy could shed light on why your repeats have been dwindling. Tried-and-true promotions are no longer generating leads.Why aren’t your killer promotions effective any more? Are you measuring their impact? It’s hard to pinpoint the impact of print campaigns, and even last year’s best digital strategies may no longer be effective. If your promotions aren’t bringing in leads, it may be time for a new, bottom-up approach to marketing. Cross-departmental complaints are mounting about a lack of collaboration and information sharing, teams operating in silos, and so on.The idea that sales and marketing just don’t get along has gone the way of the dinosaurs. Collaboration is the operative word in today’s progressive business cultures, and getting your data out of silos and in front of whoever needs it is key. At the core of every digital foundation is a plan to make business data accessible and useful across departments. Your technology systems feel old — employees are asking for features they’re used to from consumer apps.Spreadsheets are great, but you shouldn’t be using them for everything. Modern business apps that serve specific needs, integrate with one another for data sharing, and offer user-friendly experiences across desktop and mobile are where it’s at. If your current technology doesn’t offer employees most, if not all, of the above, maybe it’s time to look at atechnology platformthat can. Digging past the surface to understand the root causes of these problems often leads to the realisation that you don’t have the proper visibility into business data necessary to make good decisions. Many SMBs are built on a patchwork of applications that don’t talk to each other. Fixing your technology infrastructure to facilitate sharing and analysing data across your business is a key step toward better, more informed decision-making. A digital transformation strategy is a business transformation strategy. Remember that just as digital transformations are about business first, and digital second, problems with your business data may be signals to look more closely at how your company is doing business generally. Laurie McCabe, Co-Founder and Partner at SMB Group,said it well: “In fact, it’s usually situations like these that make you realise you don’t have great visibility into your own business data or, even worse, have lost touch with what your customers want and need.” If you’re seeing red flags and realising that your business data isn’t centralised, accessible, and working for you, what’s next? It’s time to craft a digital transformation strategy. How small business leaders can think about a digital transformation strategy. Start with an internal assessment to identify gaps, problems, and areas where you may experience difficulties. What’s your biggest problem? What’s the key to your survival? For very small and very new businesses, the answers may be short and sweet: We need customers and sales. We need a few key processes and systems we can run with. It’s important to involve everyone at your company. All will be part of your digital transformation over time, and you may have more stakeholders than you think. Even if your company is small and new, and the path to digital transformation seems clear now, remember that you’re building for the future. And future you will be bigger. Whether that means more employees, more revenue, or both, your business will grow. Flexibility and the ability to stay nimble as your business evolves should be built right into your digital transformation strategy. Connecting with aLIKE.TG MVPonline or in person can be a great — and free — resource as you start thinking about your small business digital transformation strategy. Consider outside help in mapping a digital transformation strategy. Working with consultants, partners, and tech vendors can be great for SMBs because they have the depth of experience and knowledge to help you figure out the best paths to success. Experienced partners have likely helped other companies in similar situations, and so can help you find the most direct paths to meaningful transformation. A great place to look for consulting partners is theconsultants directory on the LIKE.TG AppExchange. Many small business leaders hear the word “consultant” and instinctively flinch while reaching a hand to guard their wallets. Don’t assume that getting help is always too expensive — that’s simply not true. Many large companies offer free advice or trainings for SMBs, likeLIKE.TG Trailhead. Beyond free offerings, there are all sorts of ways to get advice without spending a lot. You don’t have to create your digital transformation roadmap alone. Remember that the point of hiring or partnering with an external group to craft your digital transformation strategy is to draw upon their expertise. They bring something to the table that you don’t have — experience and industry expertise across many different clients — and can provide value and best practices. Your short-term investment in their time is designed to help your business reap bigger benefits over the long haul. Tapping the right partner to consult on your transformation strategy lets you come up with a better plan than you could on your own, while also letting you stay focused on your core business. It will also help you avoid some of the rookie mistakes that inevitably happen when you go it alone. Collaborate on technology decisions and investments when leading a digital transformation in your organisation. If you’re leading a digital transformation in your organisation, keep this rule of thumb in mind as you consider decisions and investments: Be collaborative. If you have 10 employees, all 10 will be affected by the change, so you need to get them on board. Don’t make decisions in a vacuum. The changes brought by digital transformation will impact everyone’s daily workflow, and are meant to empower employees. Get everyone involved early and solicit ideas. Not only will you get better buy-in, you’ll get a better outcome, too. Avoid common mistakes in your digital transformation framework. Technology integration is key. It’s perhaps the number one area SMBs should be investing in. One of the biggest, easiest-to-make mistakes that businesses make is investing in a bunch of different technologies that don’t integrate. Unfortunately, it’s hard to unwind the resultant snarl of information when your platforms and apps don’t work together. SMBs need to stay focused on getting thecapabilities they need now in a way that will scale as their businesses grow. Today’s business ecosystems and platforms make it easy for vendors and developers to build apps tailored to helping SMBs grow. Adopting a scalable platform will help ensure that the processes and information in your company can flow as easily as possible. That’s the foundation upon which everything else can be built. Build bridges to connect your data, employees, and customers. You don’t need to scrap everything and start over when beginning a digital transformation, even if you’re transitioning from a snarl of apps that don’t talk to each other. In fact, the most effective solution is to bridge data silos, and pull all information into a central space — rather than completely starting over. The second part of the process is to unify your data, with the aim of creating a single, unified view of the customer. Once you’ve built bridges between fragmented information, you’ll be able to surface useful insights into customer behaviour and maximise the potential ofnew technologies like AI. Looking at your business anew with the benefit of new insights and tools is what digital transformations are all about. Overview: What Is Digital Transformation?Chapter 1: Why Are Businesses Going Through Digital Transformations?Chapter 2: Examples of Digital Transformation

What does digital transformation look like in practice, and how has it already changed the way we do business? Let’s take a look at examples of digital innovations in marketing, sales, and service that build closer customer relationships and empower employees across all industries. Examples of digital transformation in marketing. At a high level, the goal of digital transformation in marketing is to find more customers while spending less money. More specifically, awesome digital marketing generates more quality leads and helps you get closer to all of your customers, whether they’re new to your brand or longtime loyalists. The shift from analog to digital marketing materials helps these efforts in two key ways. First, digital materials are generally cheaper to produce and distribute than analog media. Email, in particular, is far less expensive than print-and-mail campaigns. Second, digital marketing opens the door tomarketing automation, analytics tracking, and dialogue with customers in ways that analog never could. Instead of planning aone-size-fits-alltrip down the funnel, marketers can build 1-to-1 journeys that observe customer behaviours and shape the experience along the way to best suit each individual buyer. And instead of going on instinct and gut feelings alone, marketers now have data-driven insights at hand to help craft those journeys. Digital transformation helps marketers connect with individual customers. Let’s look at some examples that detail how digitally transforming your messaging strategy can increase customer engagement and reduce your costs. Examples of digital transformation in sales. There’s a good reason that the traditional roles of marketing and sales are being redefined in the digital age. It’s all about the data. The ability to collect large amounts of precise data on consumer behaviour lets marketing and sales teams, in particular, approach their work in ways never before possible. Looking at consumers as individuals, and studying their behaviour from the first touchpoint all the way through the buying journey, brings to light the natural bond between marketing and sales. Nurture that bond, and magic happens when these historically separate groups work together. Data makes every sales rep productive. Salespeople particularly benefit from access to more and better data. When marketing and sales teams share information across aCRM, and individual sales reps enter sales activity and keep their pipelines up to date on the platform, information flows freely throughout an entire organisation. From there, two big things happen. First, more eyes on the same information means more opportunities to share intelligence across your entire business. Maybe someone from marketing ops sees a sales rep’s note about a prospect in the CRM, and shares marketing campaign activities related to the prospect that helps move the deal along. Second, as information flows and gathers within your company, you set yourself up to leverage cutting-edge digital innovations like artificial intelligence. Digital transformation creates AI-driven sales techniques. Artificial intelligence systems can be incredibly helpful in their ability to comb through vast amounts of data in search of useful patterns and other insights. As AI services evolve, they’re studying sales and marketing data not only from the end-consumer standpoint, but also to determine the effectiveness of sales techniques and strategies themselves. In addition to surfacing insights around, say, which demographics are more likely to buy at what times of the year, AI can shed light on which sales strategies have proven most effective over time, or what promotions and product bundles bucked long-term trends to move the revenue needle. With more and more datasets available from external sources, AI systems can mine marketplace information as well as your own sales history. From there, the systems look for correlations, patterns, and even anomalies to give your teams a competitive edge when going after accounts. Combining AI-driven insights with the tribal knowledge of your teams is perhaps the ultimate realisation of digital transformation for sales. Social selling strategies are a key component of digital transformations. Social media is everywhere, mashing up news, entertainment, and brand interactions alongside interpersonal connections. PricewaterhouseCoopers recently found that 78% of consumers were in some way influenced by social media during their buying process. And nearly half of consumers said their buying behaviour was directly affected by reviews and comments they came across on social. Consumer participation in social media has changed the buying process, so any successful digital transformation needs to incorporate a social selling strategy. This uniquely digital medium is full of opportunity for the savvy salesperson to connect and build relationships with prospects and longtime customers. As theDigital Marketing Instituteaptly said, “Successful social sellers can be regarded as thought leaders, or even trusted consultants, by prospective customers as they provide value through industry insights, sharing expertise and offering solutions to common consumer questions through creating or sharing insightful content.” Examples of digital transformation in service. Customer service, and ourideas around where service begins and ends, are being upended by the digital era as much as any other part of business. Maybe more so. The “on-demand economy” has quickly grown from a few upstart apps that hire errand runners and hail cars for busy urbanites to a global movement to, asForbesput it,“Uberisethe entire economy.” A combination of smartphone ubiquity, electronic payment systems, and apps designed to match demand (consumers) to supply (gig workers) in real time has created a world in which nearly anything you might want is just a swipe and tap away, around the clock. Talk about digital transformation! With everything from pizza delivery to child care now available at their fingertips, customers are expecting more and more companies and industries to embrace digital as their primary means of doing business. For service departments, that means greater expectations for 24/7 problem-solving on the customer’s channel of choice. But it also means greater opportunities to delight buyers and win more business. Social media is the new customer service desk. Listening and responding to customers across all social media channels sounds pretty daunting if you’re just getting started with the Twitter and LinkedIn apps on your own phone. But a host of tools designed for social service makes it easy to highlight customer needs, integrate social channels into your service workflows, and start measuring brand sentiment and activity across social media. Meeting your customers where they already are is a big part of winning business in our digital world. Approaching social service with a digital transformation mindset can really spell the difference between struggling to keep up with customer needs and turning service calls into opportunities to grow your brand. Collaboration across the different parts of your business is key. The LIKE.TG “State of the Connected Customer” report made that clear: 84% of high-performing marketing leaders say that service collaborates with marketing to manage and respond to social inquiries and issues, while just 37% of underperformers say the same. When information is freed from silos, teams collaborate more, and businesses perform better. Self-service is a service agent’s best friend. Remember the days when everything from canned goods to kitchen appliances came with a toll-free customer service number, and that 800 number was your only avenue for everything from product questions to warranty claims? Callcentersaren’t quite a thing of the past, but the digital age brings so much more flexibility when it comes to finding the right medium for serving customers in different ways. Theself-service portalis a great example. These user-facing tools offer features like password reset, self-logging of incidents, service requests, and knowledge base searches. They can also include more interactive services like collaborative spaces, chat services, and embedded social media feeds that are relevant to service issues. User-friendly design, including search fields that offer suggestions, and user profiles that leverage customers’ purchase and service histories, can go a long way toward personalising self-service for your customers. A good self-service portal can reduce the demands on your service agents. Andcustomers like self-service: 59% of consumers and 71% of business buyers say self-service availability impacts their loyalty,according to our research. AI plays a key role in the digital transformation of service. Bringing artificial intelligence into your service organisation is a prime example of the power of digital transformation. AI-powered chatbots that answer simple customer inquiries serve as a welcoming presence on your website, reducing the time customers have to wait to reach an agent. Deploying chatbots to handle level one inquiries also frees up service personnel to spend time on more sensitive cases. AI-powered bots can serve as the entry point into intelligent case routing systems. When a customer’s query is too complex for the chatbot to handle, natural language processing helps map the question to the best available expert to resolve the situation. Examples of digital transformation across industries. We’ve talked a lot in this chapter about specific examples of digital transformation in marketing, sales, and service. All digital transformations start with the move from analog to digital — that is, taking information off of paper and putting it into the digital realm. From there, these basic ideas apply to all businesses and industries: Meet customers in the digital channels they already frequent Leverage data to better understand your customers and the marketplace as a whole Free your data and share intelligence across your entire business Encourage once-separate groups like marketing, sales, and service to collaborate Digital transformation is helping many industries. Let’s look at how these ideas are being applied in a few specific ones. Examples of digital transformation in banking. Banking has been radically transformed by digital technologies in ways that have greatly benefited many consumers. Not so long ago, the majority of transactions were handled in person by bank tellers. Automated teller machines (ATMs) came along and streamlined the basic transaction process, extending business hours and reducing wait times and dependencies on human employees for cash withdrawals and other popular transactions. Over time, ATM technology has evolved to accommodate cash and check deposits, more secure transactions, and support for multiple accounts, including credit cards and mortgages. More recently, PCs and mobile devices have given way to online and mobile banking, and cashless payment systems. Consumers now conduct more and more bank business via the web, including paying bills and sending funds directly to friends and family. Mobile banking apps let users take snapshots of paper checks to make remote deposits, and a new wave of payment systems, including PayPal and Apple Pay, let consumers pay for everyday purchases with accounts linked directly to their phones, no cash or plastic card required. Examples of digital transformation in retail. Retail has also been radically transformed in the digital era. Digital transformation has both impacted the in-store retail experience and ushered in the age of ecommerce. Digital technologies have improved the retail experience for consumers and proprietors alike, enabling everything from loyalty cards and e-coupons to automated inventory and retail analytics systems. Shoppers who used to clip coupons from newspapers and magazines now just show their phones at checkout to access in-store discounts and deals. When they do this, their purchases are tallied by digital systems that track consumer behaviour trends, tie into inventory and purchasing systems, and trigger individualised customer journey events like email and SMS messaging. Additional personalisation of the in-store experience can be enabled by digital beacons that link to mobile apps to sense when particular shoppers enter the store. From there, anything from a phone alert to a personal concierge can be deployed to enhance the retail experience. Retailers are now even experimenting with subscription-style sales using Internet of Things technology. Amazon, for example, has Dash Buttons: IoT-enabled devices with buttons that trigger automated reordering of an item. Branded Dash Buttons are available for a growing number of household goods and other items regularly in need of replenishment. Just click the button when you’re running low and a refill — billed to your Amazon Prime account, naturally — will be dispatched right away, just like that. Examples of digital transformation in insurance. The impact of digital transformation in the insurance industry is similar to our other examples in that consumer expectations are driving change. Web- and app-based self-service portals make it easy for consumers to comparison shop, enroll in coverage, use multiple agents and carriers for different types of insurance (home, car, life, and so on), and file claims. In fact, much of this is now possible without the need to actually speak to an agent, which saves time for consumers and money for the insurance companies. What’s notable about digital transformation in insurance is the role the Internet of Things is playing in revamping the industry. Inexpensive, IoT-enabled sensors are giving insurers access to a wealth of data that’s informing industry forecasting and claim reviews alike. Take auto insurance as an example: In-vehicle sensors monitor actual driving habits, rewarding consumers who routinely drive safely under the speed limit or log fewer-than-average miles. Sensors connected to phones could also be used to deter texting while driving by disabling a driver’s messaging apps while their car is in motion. Connecting vehicles to wearable devices with blood alcohol measurement capabilities could help prevent drunk driving by temporarily disabling the engine, cutting risk for insurance carriers while also making roads safer for everyone. Overview: What Is Digital Transformation?Chapter 1: Why Are Businesses Going Through Digital Transformations?Chapter 3: How to Digitally Transform Your Business

In today’s fast-paced world, customers seek immediate engagement and swift support. This is precisely why numerous businesses are embracing live chat support. However, more than merely implementing live chat on your website is required. To ensure the success of your live chat service, you must align your support representatives and tools effectively. In this article, we’ll explore the concept of live chat, its functionality, and how to implement it to meet your customers’ needs seamlessly. Live chat is a tool that connects customers with real human support representatives, enabling them to resolve issues in real-time. With live chat, customers can swiftly obtain answers, reducing their time waiting for solutions or searching through your website’s knowledge base. While live chat shares real-time support characteristics with chatbots, it differs significantly. Live chat connects customers with human support representatives, whereas a chatbot is an automated program. However, chatbots can serve as customers’ initial point of contact, potentially leading to a transfer to live chat when necessary How Live Chat Works Live chat operates by connecting website visitors with company representatives through instant messaging within a private browser window. After clicking a link or button, customers can initiate a conversation with a live support agent who is ready to assist. The convenience and immediacy of live chat make it an attractive communication channel for both customers and businesses. Typically, live chat software is embedded as a widget within your website’s code. This widget loads an icon or link on your web pages, allowing visitors to click and open a chat window. You can customise the appearance and location of the chat widget to suit your preferences. Benefits of Live Chat Let’s delve into some of the advantages of incorporating live chat into your business: Omnichannel Experience: Live chat provides a seamless integration, allowing customers to connect directly with support or sales teams without leaving your site. This reduces bounce rates, enhances the customer support experience, and creates opportunities for upselling and cross-selling. Reduced Average Handling Time: Live chat minimises the frustration of long holds and wait times for customers. They can ask follow-up questions and clarify responses in real-time without filing additional cases. Automation Opportunities: Live chat easily integrates with other customer service tools, including chatbots. Chatbots can automatically respond to common inquiries, freeing up human representatives to handle more complex issues. Case Distribution: Live chat support can lead to a decrease in case volume for phone and email channels. Customers can choose the most suitable communication medium based on the urgency and complexity of their inquiry. Now that you’re familiar with the benefits of live chat, let’s discuss the proper ways to utilise it. How Live Chat Doesn’t Work Effective business decisions should revolve around improving customer service. The value of communication channels lies in their ability to connect with customers and gather valuable context on customer inquiries. The goal is to engage customers across various channels, creating a comprehensive view of their needs and preferences. Incorporating multiple communication systems provides a more comprehensive understanding of your customers. This approach, rather than a single self-contained system, allows for a richer customer profile. Live Chat Best Practices To optimise live chat, consider the following best practices: Optimise Support Systems for Speed: Ensure that your support team is equipped with up-to-date tools and systems to respond swiftly to customer inquiries. Develop an Offline Strategy: Implement chatbot support to assist customers outside of business hours, offering immediate responses and directing them to self-service resources. Respond Quickly and Clearly: Enhance your support team’s response times and clarity of communication by using tools to expedite typing and provide concise, informative responses. Make It a Teaching Moment: Use live chat as an opportunity to educate customers, share relevant knowledge and anticipate future needs. Provide Closure: Conclude live chat interactions positively, confirming that the customer’s question has been addressed, and offering further assistance if necessary. Live chat with LIKE.TG LIKE.TG’s web chat functionality comprises four essential components, each designed to streamline and enhance the customer support experience: Chat Console: Our Chat Console empowers support agents to seamlessly send and receive messages, facilitating efficient customer communication. Omnichannel: Omnichannel intelligently routes chat requests to the most suitable agent, considering availability and qualifications, and ensuring customers receive assistance from the right expert. Embedded Service: With Embedded Service, you can create a personalised chat window for customers to access the help they need. These chat windows are mobile-optimised, offering a frustration-free chat experience across all devices. Einstein Bots: These intelligent computer programs are your support agents’ allies, not their replacements. Einstein Bots can handle routine requests and gather pre-chat information, saving your agents and customers valuable time. When these four components come together, they create a seamless web chat experience for your customers and support team.

If your business struggles to stand out in a competitive market, a potent value proposition is your ace in the hole. The value proposition is a statement that defines why potential customers should choose your offerings over others in the market. It’s not just a catchy phrase; it’s the foundation of your business strategy, clarifying the unique benefits your product or service brings. In this article, we’ll delve into the essential elements of a compelling value proposition and guide you on creating one that showcases the distinctive advantages of your enterprise. Let’s transform your business with a top-tier value proposition! Key Takeaways Craft a compelling value proposition: Recognise the key elements that attract customers and give you a competitive edge. Identify your target audience: Analyse your competitors, define unique selling points, and communicate your value proposition effectively. Regularly test and refine: Ensure your value proposition remains relevant in the market. Understanding the Importance of a Value Proposition A well-crafted value proposition is the cornerstone of business success. It defines the benefits customers can expect when they choose your products or services, and it highlights what sets you apart from competitors. An effective value proposition is a powerful tool, providing potential customers with an incentive to choose you over alternatives, giving you a compelling competitive advantage in marketing. When crafting your value proposition, focus on identifying the problems your product or service solves and the advantages it offers over others. This consideration extends to your existing target audience and potential future customers. Tailoring your value proposition around these unique benefits makes creating a compelling statement much easier and yields desirable results. This, in turn, attracts more customers to your offerings, ultimately increasing your business’s revenue. Key Components of a Great Value Proposition A successful value proposition incorporates key elements to be effective. These elements include clarity, simplicity, specificity, relevance to customer needs, uniqueness compared to rivals, and a strong customer focus. We will explore each of these components in more detail, offering valuable insights on using them effectively in your value proposition. Clarity and Simplicity To be effective, a value proposition must clearly articulate the value of your product or service and the benefits it delivers to customers. Clarity ensures potential customers quickly understand why they should choose your offering over competitors. Refrain from muddled messaging that can deter customers from understanding the advantages of your product or service, leading to hesitation in making purchase decisions. Present your key message concisely and avoid technical terms and jargon. Emphasise only the most essential benefits offered, ensuring that customers can easily comprehend what they are getting into when choosing your product or service. Specificity and Relevance A unique value proposition that resonates with your target audience is key to attracting and persuading customers to choose your offering. Start by identifying the specific benefits and features that set your business apart from what’s currently available. Use this specificity to address potential customers’ pain points effectively. The more your value proposition speaks directly to customer needs, the more attractive it becomes to potential customers and effective in retaining existing ones over time. Customer Focus A customer-centric value proposition should ensure that the products and services offered meet the needs of the target audience. Understanding the challenges prospective clients may face is crucial in crafting a compelling, personalised message that maximises its impact. Consider offerings like the Juniper Print Shop or Hulu, which address the needs of their specific target markets by providing inexpensive artwork or subscription bundles. To develop a unique value proposition, focus on the benefits of delivering personalised products or services that meet today’s consumers’ desires, setting yourself apart from similar businesses in the market. Differentiation To create an effective value proposition, you must differentiate your product or service from competitors. Emphasise your distinct advantages and make your offering stand out to potential customers. Crafting a value proposition should outline these unique characteristics, distinguishing your business from others in the market. By highlighting benefits and values that draw attention, your value proposition can successfully set you apart from the competition. Crafting Your Value Proposition: A Step-by-Step Guide Now that you know the essential components of a successful value proposition, it’s time to create your own. This step-by-step guide will lead you through identifying your target audience, analysing competitors, and defining your unique selling points. Finally, it will help you articulate your value proposition clearly and effectively. 1. Identify Your Target Audience Understanding your target market is the first and most crucial step in creating value propositions that engage customers and meet their needs. Gathering insights into potential customers’ aspirations, problems, and preferences allows your business to craft focused objectives tailored precisely to them. 2. Analyse Your Competitors When crafting a value proposition, you must analyse your competitors to understand their strengths and weaknesses. This analysis helps you create an offer that stands out from the rest by providing greater benefits to potential customers. It also allows you to differentiate yourself by emphasising unique features or advantages exclusive to your business. 3. Define Your Unique Selling Points (USPs) When creating your value proposition, focus on what makes your product or service distinct from competitors. Emphasise the specific advantages and worth it provides. By spotlighting these unique selling points, you can craft a message tailored toward your target market that stands out among rival brands. Creating an effective USP should be a core step when making a persuasive case for choosing your offering. Knowing precisely why customers should select your product over others can make all the difference in business and marketplace dealings. Clarify this notion, so potential clients understand the value of investing in your services, giving you an advantage over competitors vying for their attention. 4. Communicate Your Value Clearly To ensure your value proposition is effective, it must be communicated clearly to potential customers. Express this proposition with simple, straightforward language that allows your target audience to understand what makes you stand out from competitors. When crafting a value proposition, avoid jargon that could hinder understanding. Focus on highlighting the essential advantages of choosing your offering over other options. A compelling message that sets you apart is critical for acquiring and retaining clients while achieving commercial success. Real-Life Examples of Successful Value Propositions To illustrate the efficacy of precise, customer-oriented messaging in value propositions, let’s explore some practical examples: LIKE.TG’s CRM: “An Intuitive and Powerful Customer Relationship Management (CRM) Solution” – designed to empower businesses with a user-friendly platform that streamlines operations, enhances productivity, and fosters lasting customer relationships. With LIKE.TG’s CRM, you can unlock the full potential of your business, ensuring seamless communication, data-driven insights, and exceptional customer satisfaction. This value proposition appeals to businesses seeking an easy and productive solution. Slack: Emphasises benefits like enhanced productivity and convenience, making work more enjoyable. Slack continues to stand out as a quintessential example. This collaborative tool seamlessly streamlines communication, making it a beloved choice for enterprise teams and scrappy startups. Slack’s value proposition centres on saving time by breaking down communication and system silos, transforming work into an efficient, even enjoyable process. This unique value proposition sets Slack apart in a crowded field, positioning it as the fastest-growing SaaS startup, with a client base of 77% of Fortune 500 companies. These examples demonstrate how focusing on customer desires can give you a competitive edge and boost your marketing strategies. Examining different types of value proposition examples can enhance your understanding when developing persuasive value propositions for various target audiences. Common Mistakes to Avoid When Creating a Value Proposition When crafting a value proposition, it’s critical to avoid common mistakes that can reduce its effectiveness. Some of these mistakes include: Focusing on Features Over Benefits: Emphasising what customers can achieve with your product or service is more persuasive than highlighting technical features. 2. Using Jargon: Avoiding excessive jargon or overly complex phrases that hinder understanding is crucial. Using simple, communicative language helps connect with your audience. 3. Lack of Differentiation: Distinguishing your offering from competitors can lead to a lack of competitive advantage and customer loyalty. Ensure your value proposition highlights what sets you apart. Testing and Refining Your Value Proposition Testing and refining your value proposition is essential to keep it appealing and competitive. You should regularly assess the changing needs of your market to adapt your offering accordingly. You can evaluate its success through various methods, such as consulting potential customers, experimenting with ideas, using A/B testing, Lean Startup methods, and test-versus-control approaches. Periodic refinements help you identify areas that require improvement, ensuring your value proposition remains relevant to customer wants and gives you an edge over rivals. Maximise Your Business Potential with LIKE.TG By understanding the importance of a compelling value proposition and incorporating key components like clarity, specificity, customer focus, and differentiation, you can attract potential customers, acquire more clients, and grow your business substantially. A well-crafted value proposition is essential in acquiring new customers, retaining existing ones, and expanding your enterprise. To unlock the full potential of your value proposition and streamline your business processes, explore LIKE.TG’s comprehensive solutions today Frequently Asked Questions 1. What is a value proposition and example? A value proposition is a statement that communicates why potential customers should choose your product or service over alternatives. It should highlight the unique benefits and advantages your offering provides. 2. What are the three elements of a value proposition? A value proposition typically consists of three key elements: meaningfulness, differentiation from the competition, and credibility. 3. What is the value proposition of a business model? The value proposition of a business model is a promise to customers that outlines the unique value your product or service offers, addressing their specific needs and increasing its perceived worth. 4. How can I create a value proposition that resonates with my target audience? To create a value proposition that resonates with your target audience, identify their needs and preferences, research your competitors’ strategies, define your unique selling points, and communicate your value. Tailoring your value proposition to your target audience ensures it addresses their specific needs and desires.

It’s no secret that marketers are under immense pressure to do it all: tailor campaigns to customer demand, build strong data-driven strategies, and think of outside-the-box ideas – fast. That’s why generative AI for marketing is so powerful. It can take care of many of those things in seconds, allowing you to focus your time on strategic and creative thinking. As General Manager of Marketing Cloud at LIKE.TG, I have a front row seat to the evolution of generative AI and its capabilities. In this early stage, it’s also crucial to understand marketers’ pain points and needs to push AI to deliver what they need. On this note, we recently asked marketers how they’re using and plan to use generative AI. The results were eye opening. Here’s a look at what we found, and three solutions that illustrate how generative AI for marketing will create new opportunities. 1. You can gain instant analysis from trusted first-party data With the end of third-party cookies on the horizon, 63% of marketers say trusted customer data is required for generative AI to work. And 31% deem it critical in the successful use of AI tools. Actionable customer insights based on accurate first-party data have evolved from being a supporting player to a fundamental component for businesses. The generative AI for marketing solution: Generative AI can uncover hidden patterns and deliver recommendations, helping you be more efficient. Marketers will be able to fuel creativity with new experiences that “speak your language.” You’ll be able to ask almost any question about your customers, past content, future campaigns, ROI — you name it — and get an answer instantly from first-party data. But remember, your data needs to be unified (not siloed in disparate systems) for generative AI to work well. 2. You can save time by using automation to optimize campaigns The top four marketing AI use cases revolve around automation: customer interactions, data integration, personalization, process optimization. They emphasize the significance of scaling up speed and effectiveness within existing resources. The generative AI for marketing solution: This technology will save countless hours on everything from researching customer opportunities and writing campaign briefs, to creating segments and content, to optimizing performance. Generative AI can help improve efficiency across the entire marketing campaign lifecycle. You’ll save time and customers will receive the personalized experience they desire. Get articles about marketing selected just for you, in your inbox Sign up now 3. You can deliver the personalization that customers want While 65% of consumers say they’ll remain loyal to companies that offer a more personalized experience, only 26% of marketers are confident that their organization has a successful strategy for personalization. Our latest research found that more than half (54%) of marketers say using generative AI for personalization would transform how they work. The generative AI for marketing solution: Generative AI will help marketers scale better personalization by using AI to help build customer journeys, create content, and offer recommendations based on real-time customer profiles. It will be across every marketing channel and content format – and the broader customer experience across commerce, sales, and service. It’s a great time to start learning about generative AI for marketing As businesses start using generative AI, we found that 71% of marketers say this technology will help their organization get more out of their other investments. And 51% of marketers are already using or experimenting with generative AI at work. Developing generative AI skills can help you get the most value out of this technology right now. We are excited about the future and how marketers can harness the next level of safely connected AI-assisted technologies to achieve more. We’ll share more information about this exciting technology at our upcoming Connections event — streamed live on LIKE.TG+.

As a strategic account executive at LIKE.TG, I spend my days advising companies on how to use emerging technology to grow their business. I can tell you this: C-suite leaders are nervous about generative AI in business. They know they need to do something but don’t have clarity on what, or how. I’ll tell you what I tell them. But first, here’s a sampling of what they’ve told me: A chief technology officer said, “We’re well aware of the importance of generative AI, but we’re struggling to figure out how to make it work for our business.” A chief digital officer said their teams brainstormed close to 100 high-level use cases, posing a challenge in prioritisation and execution. A chief information officer said he’d make exceptions to the company’s standard budget process to fund any generative AI technology with a one-year payback. There’s no mistaking it: Generative AI is the top priority, and every organisation I’ve spoken to has elevated it to a boardroom-level discussion. It’s natural to feel discomfort and anxiety around a technology that’s fast-moving and changing rapidly. I know that when I’m overwhelmed with a huge decision, I break it down into steps — and that’s what I’ve done for the C-suite leaders I advise. Here are six steps to follow as you navigate generative AI. Step 1: Identify the right use cases for generative AI in business Any application of generative AI has to start with the question, “what business problem am I trying to solve?” To find a relevant use case, review your current processes from end to end, and identify any friction points — those processes that lead to frustration, wasted time, lost opportunities, and in some cases, staff turnover. A tip: Focus on the problem, and avoid the temptation to fit the solution to the problem. As alluring as generative AI is, it may not be the right solution for every problem. Here’s an example: As part of their cost-saving measures, a client of mine asked their IT leader to lower call centre expenses without adversely affecting customer satisfaction. Their customers were experiencing long wait times (a key cause of customer attrition) while call centre representatives tried to find answers to their questions. Initially, the company wanted to use generative AI to instantly scour knowledge articles for the right answer; however, after some analysis, they realised that predictive AI was the better fit, allowing them to arm agents with insights such as a customer’s propensity to churn. As you evaluate your current process, think of generative AI as supercharging your workforce, powering it to work faster, and more efficiently and creatively. Step 2: Prioritise for impact, then execute and iterate Once you’ve identified one or more use cases for generative AI in business, prioritise them by considering these factors: ease of implementation strategic importance potential revenue cost savings time savings overall impact customer and employee satisfaction Which of these factors is most important to you? The answer will be different in every case. For one of my clients, a chief medical officer of a large neurological institute, the patient journey is at the centre of their generative AI use case. Their initial focus is the post-operative care division, which receives highly technical calls from patients that the staff is not generally trained to answer. This knowledge gap affects patient care. The first iteration of the solution for this use case is a chatbot to address high-level questions from patients. Future iterations will evolve the chatbot’s responses with contextual knowledge and more nuanced patient questions. The company’s vision is to transform the post-operative care division within two to three years, enhancing its staff’s capabilities and equipping them with more advanced tools to best serve patients. Get articles selected just for you, in your inbox Sign up now The most successful organisations are embracing curiosity and learning as they adopt generative AI in their business. They focus on impact and set regular checkpoints to optimise for value. They adopt a beginner’s mindset and thrive on an iterative approach with short development cycles and consistent feedback. Step 3: Create a playbook If you’re struggling on the implementation of the use cases, an organisational playbook can help. This playbook is your comprehensive guide. It outlines your organisation’s approach to rapid evaluation, iterative testing, effective cost management, impact measurement, goal setting, continuous improvement, security, and expansion into various generative AI applications. The playbook should include specific guidance like how generative AI fits with other applications the team uses, as well as general guiding principles that give your teams flexibility to push boundaries and get creative. Additionally, consider establishing a Centre of Excellence (CoE) that fosters collaboration between business and IT teams for the creation and review of this playbook. Business teams might own business problems the applications solve for, while IT might own infrastructure set up and security. The teams would join efforts around continuous improvement of the applications and output quality with regular audits, testing, and touchpoints. Implementing generative AI in your business can be overwhelming, so give people time and space to focus on learning, adapting, and creating, and allow them to fail and grow. Step 4: Develop a strong data strategy A solid data strategy is key to the success of AI in business. Ultimately, accessible, high-quality, secured data is essential for high-quality output. If your data is in any way flawed, the information the AI gives you will be, too. When fine-tuning AI models, make sure the AI has easy access to relevant data. That means the data is accurate, updated, and complete. The CIO of a leading media company told me he was excited about having a conversation with their data by asking it questions to inform strategy. Chief data officers face the challenge of helping companies get the most value from generative AI. A strong data strategy ensures high-quality data throughout the lifecycle: building enhanced capabilities into the data architecture; securing sensitive data while staying compliant with evolving regulations; and investing in data engineering talent. LIKE.TG Data Cloud, for example, provides the data for Einstein Copilot, LIKE.TG’s new generative AI conversational assistant, and makes sure the outputs are contextually relevant. Using Data Cloud and Einstein AI, you can create targeted marketing segments, personalise website landing pages based on consumer behaviour, and customise emails for specific campaigns with the help of Einstein Copilot. Step 5: Determine what success looks like You can’t know if you’re successful if you haven’t defined what success is. That’s why I advise companies to determine this at the outset. A senior vice president of a large technology company said his company’s biggest threats are a) not adopting new ways of using AI fast enough and therefore falling behind, and b) finding and retaining talent with AI expertise. They’re exploring whether AI can use data analytics to improve their business. For example, they hope AI can help them quickly process market, competition, and customer data so they can optimise products and services more efficiently. In this pursuit, they’re defining the parameters of success and pinpointing key performance indicators (KPIs) to assess the technology’s impact. As you build your generative AI use cases, define your metrics and categorise them as primary and secondary KPIs. The primary KPIs are your business KPIs. For example, your service department might look at minutes saved on customer calls. Sales and marketing departments might look at sales qualified leads and growth in marketing, respectively. Did generative AI help your company meaningfully affect these KPIs? That’s what you’re looking for. Do you have higher-quality leads and are you converting them at higher rates? The secondary KPIs are your generative AI KPIs. These include accuracy/error rate, output quality, training time, scalability, training cost/resources, and productivity gains. If you don’t hit your targets, go back to the beginning and reassess your use case. Was generative AI the right solution for the problem? If so, consider whether you used the right foundation model for the task, whether you trained it on the right data, whether that data was high quality, and whether it was grounded in business context relevant to the task, among others. Step 6: Bring your business and IT teams together If your company hasn’t aligned its business and IT teams yet, now’s the time. Doing so will let your teams focus on well-defined priority use cases for generative AI in business, and will make sure solutions are tailored to meet tactical and specific business objectives. Earlier, I mentioned establishing a CoE to write your playbook, and in general, a CoE is a great direction to take: It can be your collaborative hub for shaping best practices and ensuring that generative AI aligns with your company’s strategic goals. It’s abundantly clear generative AI is not the flavour of the month — it’s the future we’re stepping into. We know you’ve got questions and concerns. With the above guidelines, think about how you want to reshape your current processes and tap into AI’s power.

AI-powered marketing automation has emerged as a leading trend, surpassing industry buzz. This transformative technology, particularly when integrated with LIKE.TG, reshapes how brands engage with their audience, providing exceptional experiences while concurrently reducing operational costs. What is AI (Artificial Intelligence) Marketing Artificial Intelligence (AI) marketing represents a paradigm shift in how businesses approach marketing strategies. By harnessing the power of AI, companies can automate and enhance various marketing processes, unlocking a world of possibilities and revolutionising how they engage with their target audience. AI marketing leverages data, analytics, and machine learning to optimise marketing efforts, personalise customer experiences, and elevate overall marketing efficiency and effectiveness. This data-driven approach empowers businesses to make informed decisions and accurately identify trends, patterns, and customer preferences. With these insights, companies can tailor their marketing campaigns to resonate with their audience, increasing engagement, conversions, and customer loyalty. Moreover, AI marketing streamlines marketing operations by automating repetitive and time-consuming tasks, allowing marketing teams to focus on more strategic and creative pursuits. This automation encompasses content generation, social media management, email marketing, and customer service, ensuring a consistent and engaging presence across multiple channels. AI marketing is not just a buzzword; it’s a transformative force reshaping the marketing landscape. By embracing AI, businesses gain access to a wealth of new tools, data science and technologies, empowering them to reach, engage, and convert their target audience with unparalleled precision and effectiveness. In today’s competitive business environment, AI marketing has become an indispensable tool for businesses seeking to thrive and stay ahead of the curve. AI Marketing encompasses various use cases, including: 1. Data Analysis: Automating the collection and analysis of large volumes of marketing data from different campaigns and programs, eliminating the need for manual sorting. 2. Content Generation: AI generates both short and long-form content for marketing purposes, including video captions, email subject lines, web copy, blogs, and more. 3. Media Buying: Predicting a business’s most effective advertisement and media placements, maximising the return on investment (ROI) of marketing strategies while reaching the target audience. 4. Real-time Personalisation: Modifying a customer’s experience with marketing assets, such as web pages, social media posts, or emails, to align with their past preferences and encourage specific actions, such as clicking a link, signing up, or purchasing. 5. Natural Language Processing (NLP): Utilising AI to generate human-like language for content creation, customer service bots, personalised experiences, and more. 6. Automated Decision-Making: Assisting businesses in deciding which marketing or business growth strategies to employ based on historical or external data inputs. By adopting AI marketing strategies, businesses unlock the potential to deliver exceptional customer experiences, build stronger customer relationships, and drive sustainable growth in the ever-evolving digital landscape. AI marketing represents the future of marketing, and businesses that embrace its transformative power will be well-positioned to succeed in the years to come. How does AI Marketing work? AI marketing uses artificial intelligence technologies to automate and enhance marketing processes. These technologies can be applied to a wide range of marketing tasks, with AI marketing platforms including: Marketing Automation: AI automates lead generation, scoring, and customer retention tasks. By leveraging AI, marketers can identify potential customers and engage with them at the optimal time when they are most likely to respond positively to marketing messages. Content creation: AI can generate marketing content, such as blog posts, articles, and social media posts. This can save businesses time and money while still producing high-quality content. Customer service: AI can provide customer service by answering questions and resolving issues. This can free up human customer service representatives to focus on more complex tasks. Predictive analytics: AI can analyse data and predict customer behaviour. This information can be used to personalise marketing campaigns and improve customer engagement. Personalisation: AI technologies create customer profiles based on data gathered from their purchase history and interactions. Marketers can then deliver targeted advertisements, offers, and new products that align with customers’ preferences. Targeted marketing, powered by customer data, enhances engagement, conversion rates, and overall marketing return on investment (ROI). Forecasting: AI serves as a valuable tool for predictive analytics and forecasting. Predictive analytics utilises data from past customer interactions to anticipate their future actions. When applied to larger audience segments and scaled up, AI can also forecast business metrics such as revenue outcomes, providing valuable insights for strategic decision-making. Overall, AI marketing is a powerful tool that can help businesses reach, engage, and convert their target audience. By leveraging AI, companies can gain a competitive edge and thrive in the digital age. How is AI changing digital marketing? Artificial Intelligence (AI) has emerged as a transformative force that is reshaping the digital marketing landscape. Its capabilities are automating mundane tasks and empowering marketers to delve deeper into strategic initiatives. One remarkable application of AI lies in crafting personalised customer experiences. By leveraging AI-driven tools, marketers can analyse customer behaviour, preferences, and past interactions to deliver tailored product or service recommendations. This personalised approach fosters stronger customer relationships and enhances the overall brand experience. Furthermore, AI is pivotal in aiding marketers to comprehend their customers more profoundly. Through meticulous customer data analysis, AI uncovers hidden patterns, trends, and insights. With this knowledge, marketers can devise targeted and effective marketing strategies that resonate with their audience. Beyond enhancing customer experiences, AI also revolutionises how marketers measure their campaigns. Real-time tracking and analysis of campaign performance empower marketers to make informed adjustments, ensuring optimal allocation of resources and budget. This data-driven approach minimises guesswork and maximises return on investment. In content creation, AI proves to be an invaluable asset. AI identifies topics and interests that captivate the target audience by analysing customer data. This enables marketers to craft highly relevant, engaging content that resonates with their customers. Compelling content fosters brand loyalty and drives conversions, propelling businesses toward success. Examples of AI’s practical applications in digital marketing abound. Chatbots, powered by AI, simulate human conversations, providing round-the-clock customer support and assistance. Personalised recommendations, driven by customer data, enhance user experience and boost sales. Dynamic pricing, enabled by AI, optimises pricing strategies based on market dynamics, maximising profits while ensuring competitive offerings. Fueled by AI’s data analysis capabilities, predictive analytics empowers marketers to anticipate customer behaviour and trends. This foresight allows for developing highly targeted and successful digital marketing campaigns. As AI evolves, its impact on digital marketing will only deepen. The future holds boundless opportunities for marketers to harness the power of AI, unlocking new frontiers of innovation and driving unprecedented success. What Are the Types of AI Marketing Tools? In the marketing realm, artificial intelligence (AI) has emerged as a game-changer, offering many solutions that automate and enhance various tasks. 1. Machine Learning: Machine learning, driven by AI, utilises computer algorithms to automatically analyse information and enhance digital marketing campaigns based on experience. By leveraging relevant historical data, machine learning devices can inform marketers about effective strategies and help avoid repeating past mistakes. 2. Big Data and Analytics: The rise of digital media has generated a significant amount of “big data,” presenting opportunities for marketers to gain insights and accurately assess value across different channels. However, the abundance of data can be overwhelming. AI marketing comes to the rescue by rapidly sifting through the data, filtering it to its essentials, and providing analysis. It can also recommend the most valuable elements for future marketing campaigns. 3. AI Marketing Platforms Tools: Effective AI-powered marketing solutions offer digital marketers centralised platforms for managing the vast amounts of collected consumer data. These AI marketing platforms extract valuable marketing intelligence from the target audience, empowering marketers to make data-driven decisions about effective outreach strategies. For instance, frameworks like Bayesian Learning and Forgetting aid marketers in understanding a customer’s receptiveness to specific digital marketing efforts, leading to improved campaign targeting. 4. Content creation: Generating high-quality articles, blog posts, social media updates, and personalised emails. By analysing vast amounts of data, AI tools identify trends, customer preferences, and relevant topics, allowing businesses to create content that resonates with their target audience. Additionally, AI optimises content by recommending keywords and phrases that enhance search engine rankings and drive organic traffic. 5. Personalisation: Another critical aspect of modern marketing. AI algorithms analyse customer data to gain profound insights into individual preferences, behaviours, and pain points. This information is then leveraged to create personalised marketing campaigns, product recommendations, and customer experiences. By delivering tailored content and offers, businesses enhance engagement, conversions, and overall customer satisfaction. 6. Voice search: With the proliferation of voice-activated devices like smart speakers and smartphones, voice search has become a critical channel for businesses to connect with customers. AI-powered voice search optimisation ensures businesses are easily discoverable to customers using voice commands, maximising their visibility in voice search results. These examples illustrate the diverse spectrum of AI marketing solutions available. As AI technology evolves, we can anticipate even more innovative and advanced solutions to revolutionise how businesses market their products and services. What Are the Benefits of Leveraging AI in your Marketing strategy? Integrating AI into marketing practices has unlocked a treasure trove of benefits that can dramatically transform a business’s approach to reaching and engaging its target audience. One of the most profound advantages of AI marketing is its ability to elevate customer experiences. By deploying AI-powered chatbots and virtual assistants, companies can provide immediate and personalised responses to customer inquiries, ensuring a smooth and enjoyable customer journey. Moreover, AI-driven analytics offer businesses a profound understanding of customer preferences and behaviours, enabling them to tailor their marketing strategies precisely and deliver content that resonates deeply with their audience. Another significant competitive advantage of AI in marketing lies in its remarkable potential to enhance operational efficiency. AI automation seamlessly handles repetitive and time-consuming tasks such as data entry, automated email marketing campaigns, and social media management, liberating marketing teams to devote their attention to more strategic and creative endeavours. This streamlined workflow optimisation increases productivity and significant cost savings, allowing businesses to allocate resources judiciously and effectively. AI’s role in improving decision-making and optimisation within marketing campaigns is genuinely remarkable. By meticulously analysing vast amounts of data and uncovering hidden patterns and trends, AI-powered analytics empower marketers with invaluable insights that guide strategic decisions. This data-driven approach allows businesses to optimise their campaigns with surgical precision, target specific segments more accurately, and maximise their return on investment, propelling their growth trajectory to unprecedented heights. Personalised marketing and customer engagement soar to new heights with the advent of AI marketing. AI algorithms can delve deep into customer data, extracting profound insights that enable the creation of personalised experiences and marketing messages tailored to individual preferences. This profound level of personalisation fosters deep customer engagement, cultivates unwavering brand loyalty, and dramatically elevates the likelihood of conversions. AI-powered recommendations and dynamic pricing strategies amplify personalised customer experiences, driving exponential revenue growth and unparalleled customer satisfaction. Finally, AI injects a refreshing dose of agility and innovation into marketing strategies, propelling businesses to the forefront of industry trends. As AI technology continues its relentless march forward, it unveils many new opportunities for businesses to stay ahead of the curve and outpace competitors. By wholeheartedly embracing AI and fearlessly experimenting with its innovative applications, companies can differentiate themselves from the crowd, drive unprecedented growth, and shape the future of marketing. From AI-generated content creation to predictive modelling, the possibilities are boundless, empowering businesses to unleash their full potential and achieve extraordinary success in the dynamic digital age. Cost Efficiency: AI minimises costs by streamlining expenses across various marketing tactics, from SEO and content marketing to paid ads and influencer outreach. This enables digital marketing teams to redirect resources towards strategic objectives, ensuring a more efficient budget allocation. Personalisation: Addressing the increasing demand for personalised experiences, LIKE.TG AI analyses past customer data platforms to deliver tailored messages at scale. This not only enhances customer engagement but does so without the need for manual intervention. Predictive Analysis: LIKE.TG AI empowers businesses to anticipate industry trends and customer behaviour. Organisations can inform their marketing strategy by analysing customer behaviour and market trends and optimising product launches and campaigns for maximum impact. ROI Optimisation: Leveraging AI-driven insights, LIKE.TG AI contributes to a boost in return on investment. It is a reliable tool for identifying growth opportunities, optimising ad spend, and enhancing the overall customer experience. Time Effectiveness: LIKE.TG AI marketing automation liberates time from repetitive tasks, allowing teams to focus on high-value activities. This includes content creation, nurturing customer relationships, and fostering a more strategic marketing approach. What Are the Pitfalls and Challenges of AI Marketing? Artificial intelligence (AI) is a powerful tool that can be used to improve marketing campaigns. Still, it is essential to be aware of the potential pitfalls and challenges before you invest in AI marketing solutions. This section will discuss some of the key challenges and considerations businesses should consider when using AI for previous marketing campaigns. One of the biggest challenges of AI marketing is the potential for bias. AI algorithms are trained on data, and if the data is biased, then the AI algorithms will also be biased. This can lead to unfair or inaccurate marketing practices, damaging a company’s reputation. For example, an AI algorithm trained on data biased towards white males may be more likely to recommend products and services to white males, even if there are other groups of people who are more likely to be interested in those products and services. Another challenge of AI marketing is the need for more transparency. AI algorithms are often complex and challenging to understand, making it difficult for businesses to know how they make decisions. This can lead to a need for more trust in AI marketing solutions, making it difficult for companies to get the most out of them. Additionally, the cost of AI marketing programs can be a barrier for some businesses. AI marketing solutions can be expensive to develop and implement, making it difficult for small businesses to compete with larger firms with more resources. Finally, there is the potential for AI marketing to be used for malicious purposes. AI algorithms can create fake news stories, spread misinformation, and target people with personalised marketing campaigns designed to exploit their vulnerabilities. This can hurt society and make it difficult for businesses to use AI marketing responsibly. Despite these challenges, AI marketing has the potential to be a powerful marketing tool for businesses. By being aware of the potential pitfalls and challenges, companies can take steps to mitigate these risks and use AI marketing to improve their marketing campaigns. What Are the Examples of AI in Marketing? AI-powered chatbots and virtual assistants are becoming increasingly common on business websites and apps. These AI-driven tools can provide customer service 24/7, answer questions, and help users navigate websites. Chatbots can also collect data and provide insights into customer behaviour. AI-driven personalised email campaigns and content recommendations are another powerful way businesses use AI in digital marketing strategy. By using AI to analyse customer data, companies can create personalised email campaigns that are more likely to be opened and clicked on. AI can also be used to recommend products and content tailored to each customer’s interests. AI-generated product recommendations and upselling opportunities are other ways businesses use AI to boost sales. By using AI to analyse customer data, companies can identify products that customers are most likely to be interested in and then make personalised recommendations. AI can also create upselling opportunities by suggesting complementary products or services that customers might not have considered otherwise. AI-powered social media marketing analytics and optimisation tools are helping businesses to get more out of their social media marketing efforts. Using AI to analyse social media data, companies can identify which posts are most popular, track brand sentiment, and find influencers who can help them reach their target audience. AI can also optimise social media campaigns by automatically posting content at the best times and targeting the right audience. AI-enabled image and video recognition technology is used for content moderation and optimisation. By using AI to analyse images and videos, businesses can automatically identify inappropriate content and remove it from their websites and social media channels. AI can also be used to optimise images and videos for search engines and social media platforms, making them more likely to be seen by potential customers. How to use AI marketing To effectively utilise AI marketing tools and platforms, businesses must first identify their target audience and understand their needs and preferences. This can be done through market research, data analysis, and customer feedback. Once the target audience is identified, businesses can choose the right AI marketing tools and platforms to reach and engage with them. Many different AI marketing tools and platforms are available, each with unique features and capabilities. Businesses should carefully evaluate the options and select the best fit for their needs and budget. Once the AI marketing tools and platforms are in place, businesses can create a data-driven AI marketing strategy. This strategy should outline the goals and objectives of the AI marketing campaign, as well as the specific tactics that will be used to achieve those goals. The fully integrated AI marketing platform and strategy should also include a plan for measuring the campaign’s success and adjusting as needed. One of the most important aspects of AI marketing is personalisation. AI can collect and analyse customer data, which can then be used to create personalised marketing campaigns tailored to each customer’s needs and interests. This can lead to increased engagement, conversions, and customer loyalty. AI can also automate repetitive marketing tasks, such as sending emails, scheduling social media posts, and generating reports. This can free up marketing teams to focus on more strategic tasks, such as developing new marketing campaigns and analysing data. Businesses can gain a competitive edge using AI marketing and deliver more personalised and relevant customer experiences. This can lead to increased engagement, conversions, and customer loyalty. What are Generative AI Marketing Predictions and Trends? The realm of marketing stands on the precipice of a transformative era as generative AI reshapes the landscape, empowering businesses to craft personalised and captivating content, automate mundane tasks, and, with data science talent, unlock profound insights into customer behaviours. As the accessibility and sophistication of AI technology surge, the coming years will undoubtedly witness an exponential embrace of AI-driven marketing strategies. One pivotal trend is the meteoric rise of AI-powered content creation tools, capable of generating high-quality and distinctive content at an unprecedented pace, thereby liberating marketing teams to channel their efforts into more strategic endeavours. Concurrently, AI-driven chatbots are undergoing rapid advancements, offering businesses an avenue to engage with customers round-the-clock, promptly addressing queries with remarkable efficiency. Another trend that warrants close attention is the ascent of AI-powered predictive analytics. This technology empowers marketers to comprehend their target audience better and tailor their marketing endeavours with pinpoint accuracy. By meticulously analysing customer data, AI uncovers hidden patterns and trends, paving the way for personalised marketing campaigns that resonate profoundly with customers, fostering stronger connections and driving business growth. Furthermore, generative AI holds immense promise in elevating customer experiences to unprecedented heights. AI-powered virtual assistants emerge as invaluable assets, providing real-time assistance to customers and swiftly resolving their queries and concerns with remarkable efficiency. Additionally, AI seamlessly personalises the customer journey, presenting tailored recommendations and enticing offers that align precisely with individual preferences, ensuring unparalleled customer satisfaction and loyalty. In essence, the future of AI marketing brims with boundless potential. As AI marketing technology embarks on its inexorable evolution, we can anticipate many groundbreaking innovations that will revolutionise marketing strategies, propelling businesses to unprecedented heights of success. How can you best implement AI? To successfully implement AI in your marketing efforts, there are several key steps to follow: 1. Identify your goals and objectives: Clearly define what you want to achieve with AI marketing. Whether improving customer engagement, increasing lead generation, or optimising campaign performance, having clear goals will guide your AI implementation strategy. 2. Assess your current marketing efforts: Evaluate your existing marketing processes, data sources, and technology infrastructure. Identify areas where AI can complement or enhance your current efforts and prioritise those areas for AI implementation. 3. Choose the right AI tools and platforms: Select AI tools and platforms that align with your goals and objectives. Consider factors such as functionality, scalability, ease of use, and compatibility with your existing systems. 4. Integrate AI into your marketing workflows: Seamlessly integrate AI into your existing marketing workflows to ensure a smooth transition. This may involve training your marketing team members on using AI tools, establishing data-sharing protocols, and updating your marketing processes to accommodate AI-driven insights. 5. Monitor your results and make adjustments as needed: Continuously monitor the performance of your AI marketing initiatives and analyse the results. Use data-driven insights to identify what’s working and what’s not, and make necessary adjustments to optimise your AI marketing investments and implementation. 6. Consider ethical implications: Ensure your AI marketing practices adhere to ethical standards, including data privacy, transparency, and fairness. Implement robust data security measures and obtain consent from customers before using their data for AI-powered marketing. By following these steps, you can effectively implement AI in your marketing efforts and unlock its full potential to drive growth and success. Utilising AI Marketing with LIKE.TG LIKE.TG Einstein is a powerful AI tool that can be used to enhance your marketing efforts. With Einstein, you can create personalised marketing campaigns, automate marketing tasks, and gain insights into your marketing data. Einstein offers a variety of features that can be used for ai enabled marketing campaigns, including: Einstein Lead Scoring: Einstein can help you identify your most qualified leads so you can focus your sales efforts on the most likely to convert. Einstein Segmentation: Einstein can help you segment your customers based on their demographics, interests, and behaviours so you can create more targeted marketing campaigns. Einstein Content Recommendations: Einstein can help you recommend the most relevant content to your customers based on their interests and past behaviour. Einstein Predictive Analytics: Einstein can help you predict customer behaviour and trends so you can make more informed marketing decisions. You can improve your marketing efficiency, effectiveness, and reach by using Einstein. Einstein can help you create more personalised and relevant marketing campaigns, automate marketing tasks, and gain valuable insights into your marketing data. This can lead to increased leads, sales, and revenue. To start with Einstein, you can sign up for a free trial. Once you have signed up for the AI marketing program, you can access Einstein from the LIKE.TG Marketing Cloud. Commonly Asked Questions About AI in Marketing As AI marketing continues to gain traction, it’s natural for businesses to have questions about its implications and applications in marketing program. Here, we’ll address some frequently asked questions about AI in marketing to provide a clearer understanding of this transformative technology. 1. How does AI affect traditional marketing strategies? AI complements traditional marketing strategies by enhancing their effectiveness and efficiency. It automates repetitive tasks, allowing marketers to focus on more strategic and creative aspects. AI also enables personalised customer experiences, data-driven decision-making, and improved campaign performance. 2. What are the limitations of AI in AI marketing tools? While AI offers immense potential, it also has limitations. These include the requirement for high-quality data, the inability to completely replace human creativity and emotional intelligence, and the potential for bias if not adequately managed. 3. How effective is AI for small businesses? AI is highly effective for small businesses as it levels the playing field by providing access to advanced technologies and analytics that were previously only available to larger enterprises. AI can assist small businesses in optimising their marketing efforts, reaching a wider audience, and competing more effectively. 4. Will AI replace human marketers? AI is not meant to replace human marketers but to augment their capabilities. AI handles routine tasks, freeing up marketers to focus on strategic decision-making, creative problem-solving, and building meaningful customer relationships. 5. What are the ethical considerations of using AI in marketing? The use of AI in marketing raises ethical considerations, including data privacy, transparency, and potential bias. Businesses must implement ethical guidelines, obtain customer consent, and ensure responsible AI practices. By addressing these commonly asked questions, businesses can better understand AI marketing and its implications. AI can revolutionise marketing strategies, enabling enterprises to succeed tremendously and deliver personalised customer experiences.

Comparison isn’t always the thief of joy. When’s the right time to hit ‘send’? What subject line should you use? Which image is best? These might seem like small choices, but they are things that email A/B testing can help you figure out. According to our State of the Connected Customer report, nearly two-thirds of customers expect companies to adapt to their changing needs and preferences. Gauging results of an audience with a single email can be difficult. But sending multiple versions of an email – with different subject lines, preview text, copy, calls-to-action, or timing of delivery – can give you a basis for comparison. With email A/B testing, you get all the valuable data you need to improve your next round of outreach. In this blog, we’ll guide you through how email A/B testing works, including its challenges, best practices, and ways artificial intelligence (AI) can help. What is email A/B testing? How does email A/B testing work? What are 4 best practices for email A/B testing? What are the biggest challenges of email A/B testing? How can email A/B testing increase performance? How to get started with email A/B testing How does AI factor into email A/B testing? What’s ahead for email A/B testing? What is email A/B testing? Email A/B testing is a marketing strategy where you provide different versions of a campaign to your audience. The “A” version is displayed to some of your audience, while another subset gets the “B” version. It can be anything from subject lines to body copy to offers to images. Email A/B testing can help you optimise your campaigns. Testing two variations of an email will provide insights to see which version your audience likes the best. There are a lot of reasons why email is one of the easier methods for A/B testing. The main one is that email A/B testing includes binary responses – clear-cut, two-option reactions or actions recipients can take. These include things like clicking or not clicking a link or opening or not opening an email. Email is a good candidate for A/B testing for its ability to test the following: Subject line: Testing different subject lines to see which one generates higher open rates. Preheader: A preheader refers to a line of preview text of the content in the email. So in this case, you can test different introductory phrases to gauge which one captures readers’ attention. Call-to-action (CTA): Testing various CTAs to find out which one leads to more conversions. Content: Testing variations of the email copy to determine which content resonates best with your audience. Image: Testing variations of the selected images to determine which your audience responds to better. Timing: Testing the timing of email delivery to identify the most responsive time of day or day of the week. How does email A/B testing work? Let’s break down the way email A/B testing works a little further. Selection of variables: Marketers choose specific elements of the email to test. Common variables include the subject line, sender’s name, email content, CTA buttons, images, headlines, and more. Creation of variations: Different versions of the email are created – each with a single variable changed. For example, if testing the subject line, you might create two versions of the email with different subject lines but keep the rest of the content identical. Random assignment: The email list is divided randomly into groups, with each group receiving one of the email variations. This ensures that the test is conducted with a representative sample of the audience. Distribution: The email variations are sent to their respective groups within the same time frame to minimise external factors affecting the results. Data collection: As the emails are opened, clicked, and acted upon, data is collected on the performance of each variation. Marketers track metrics like open rates, click-through rates, conversion rates, and revenue generated. Analysis: After a predetermined time, the results are analysed to determine which variation was more effective in achieving the desired goal. The winning variation is then chosen. Optimisation: Based on the results, marketers can apply the winning elements to future email campaigns and continue to refine their email marketing strategy. Sending two (or three or ten) versions of the email will provide you with lots of data to work with. This kind of data-driven approach is a way to increase open rates, click-through rates, conversion rates, and other key performance indicators. What are 4 best practices for email A/B testing? While trial and error is one way to determine the best possible outcome from your email A/B testing, these methods will help your team make more informed decisions. Identify realistic goals: Before conducting an A/B test, clearly define what you aim to achieve. For instance, if the goal is to boost open rates, set a realistic target percentage increase, such as a 5% rise in open rates over the current benchmark. Make sure to have a control variable: Establish a baseline for comparison by keeping all aspects except one consistent, so that you can test one element at a time. If testing email subject lines, for example, keep the content and layout of the email constant while altering only the subject line. When testing a CTA button, ensure that all other elements remain the same, such as the email copy, layout, and imagery. Isolating and testing one variable in each A/B test can accurately determine its impact. Define your audience properly: Segment your audience based on relevant demographics or behavioural data. For example, if testing a product-related email, target a specific group of subscribers who have previously shown interest in similar products or services. Factor in variables: Consider external factors that could affect the test results. Different email clients, for example, might receive the email across various platforms. Another thing to consider is different send times could mean biased results. Remember, taking the long view and being patient during the process is key. Building tests based on the best available data will ensure that performance metrics, customer preferences, and industry benchmarks are taken into account. And, learning from your results involves a ton of data analysis. Make sure to take the time to delve into the nuanced details of each A/B test, looking at the overall outcome and any subgroups that displayed notable behaviour. View individual results as a piece of a puzzle – and the large view when you’re “done” putting the pieces together is a deeper understanding of your audience’s preferences and behaviours. Integrating insights into your future A/B testing strategies will help you refine your strategies so you can maximise the effectiveness of your email campaigns. Become the email marketing GOAT Tired of your email campaigns not delivering results? Level up with this lesson on Trailhead, LIKE.TG’s free online learning platform. Start learning now +500 points Module Email Marketing Strategies What are the biggest challenges of email A/B testing? Email A/B testing has its own set of issues. One major hurdle to clear is making sure your testing aligns with your campaign objectives. As marketers, we know that any strategy demands consideration of metrics that reflect success. Now is the time to ask yourself how you want to implement and analyse your strategy. Did a subject line fail because it ended in a question mark and the other one didn’t? Or was it simply because the verbiage was bad? Ensuring an adequate sample size is another challenge. Without it, your results may not be representative of the broader audience. You risk misinterpreting results when data is inaccurate. Remember, the success of your email A/B testing doesn’t hinge on a single factor; rather, it depends on the interplay of all elements – such as content, design, audience preferences. Taking a holistic view is critical for drawing adequate conclusions. How can email A/B testing increase performance? The beauty of A/B testing is its snowball effect. As you refine messaging, you gradually sculpt communications into their most successful versions. This process helps uncover nuances that resonate with your audience. Those improved messages lead to better data, which lead to even more improved messaging, which leads to even better data. The result is better open rates, increased click-throughs, and ultimately, elevated conversion rates. Through continued testing, each email that reaches your subscribers is crafted to achieve a bolstered performance. How to get started with email A/B testing You can take these six steps to get your email A/B testing program off the ground. Determine the right platforms: You can do this by defining your goals about what you want from your testing. Are you interested in click-through rates, or conversions? Are you seeking insights about user behaviour patterns, or demographic segmentation? Define your strategy: A clear strategy outlines the elements you intend to test and the metrics you’ll use to measure success. The more detailed you get about things like subject lines, call-to-actions, and visuals, the more effective you’ll be in the testing process. Create a large subscriber base: Email A/B testing is a numbers game, so building up your subscribers is crucial. This might look like investing in targeted lead generation efforts such as content marketing, webinars, and personalised landing pages. Gather accurate data: You can identify areas for improvement and create targeted test groups by analysing email performance and customer behaviour. Make time for checks on your validation processes so that you know your performance metrics are reliable. Implement a segmentation strategy: Developing an understanding of your audience allows you to tailor tests to specific audience segments – and maximise the impact of your experiments. This might mean using customer surveys to gain insights into your audience’s preferences and purchasing behaviours. Automate the process: You can save tons of time and resources with email automation. For example, you can configure reviews and cancellations or make edits or duplicates and track results. By automating repetitive tasks, you can focus on interpreting and implementing the results of your A/B tests. It’s important to ask yourself if you can support the demands of A/B testing. Without a solid infrastructure, there’s no guarantee you can track and analyse results. Take the time to set yourself up with a testing process that makes the most of your results. How does AI factor into email A/B testing? AI is all the buzz these days – and for good reason. Predictive AI, for example, can serve as a force multiplier for A/B testing things like send times. AI can determine the optimal time to send emails to each recipient based on their past behaviour. This increases the chances of emails being opened and read. It helps you refine your macro strategy as you learn about specific customer tendencies. This allows you to get more accurate with future segmenting. Generative AI, on the other hand, makes it faster to create scalable variant content for testing, allowing you to test bigger changes without having to generate two entirely different sets of copy from scratch. Generative AI models can assist in generating email content, including subject lines, body text, and personalised recommendations. This can help you create compelling and personalised emails for A/B testing, saving time and resources in content creation. It’s worth it to familiarise yourself with these definitions as you consider all the new ways AI can inform better results: Generative AI for content creation: Generative AI models can assist in generating email content, including subject lines, body text, and personalised recommendations. This can help you create compelling and personalised emails for A/B testing, saving time and resources in content creation. Segmentation and personalisation: AI can analyse vast amounts of data to segment email lists based on various factors like demographics, behaviour, and preferences. This allows for highly targeted and personalised email content, leading to better A/B testing results. Predictive analytics: AI can predict which email variations are likely to perform better for specific segments of your audience. It uses historical data to make these predictions, making email A/B testing more efficient. Content optimisation: AI tools can analyse email content and suggest improvements based on historical data and best practices. This can help you create more engaging and effective email content for A/B testing. Send time optimisation: AI can determine the optimal time to send emails to each recipient based on their past behaviour. This increases the chances of emails being opened and read. Subject line optimisation: AI can help generate and test subject lines to find the ones most likely to capture the recipient’s attention and increase open rates. Dynamic content: AI allows for dynamic content within emails, where the content changes based on the recipient’s behaviour or preferences. This can improve engagement and conversions. Predictive scoring: AI can assign predictive scores to email recipients to indicate their likelihood of opening, clicking, or converting. You can use this data to prioritise your email A/B testing efforts. Automation: AI can automate the A/B testing process by continuously testing and optimizing email campaigns in real-time, without the need for manual intervention. Multivariate testing: AI can handle more complex multivariate testing, where multiple elements within an email are simultaneously tested. Natural language processing (NLP): AI-powered NLP can analyse the sentiment and tone of email content to ensure that it aligns with your brand’s messaging and is more likely to resonate with your audience. A/B testing insights: AI can provide insights and recommendations based on A/B testing results. It can identify patterns and correlations that might not be obvious to human marketers. Feedback analysis: AI can analyse the feedback and responses to email campaigns, helping you understand the reasons behind success or failure and suggesting improvements. What’s ahead for email A/B testing? Email A/B testing is poised for a transformative journey, especially with the expanding role of AI. Predictive and generative AI will provide marketers with more and more avenues for stronger customer engagement. With predictive AI, you can better anticipate customer preferences, which allows you to tailor A/B tests to individualised segments. Generative AI holds the promise of creating tailored content variations based on intricate customer data. The result is a more nuanced and effective A/B testing process – and tangible business outcomes. On top of that, advancements in AI analytics will yield more actionable test results. Data-driven email A/B testing has the power to deliver the targeted content that customers demand. Welcome to a new era, where personalisation – and comparison – wins the day.

Some ecommerce trends and technologies pass in hype cycles, but others are so powerful they change the entire course of the market. After all the innovations and emerging technologies that cropped up in 2023, business leaders are assessing how to move forward and which new trends to implement.Here are some of the biggest trends that will affect your business over the coming year. What you’ll learn: Artificial intelligence is boosting efficiency Businesses are prioritising data management and harmonisation Conversational commerce is getting more human Headless commerce is helping businesses keep up Brands are going big with resale Social commerce is evolving Vibrant video content is boosting sales Loyalty programs are getting more personalised User-generated content is influencing ecommerce sales Subscriptions are adding value across a range of industries Ecommerce trends FAQ 1. Artificial intelligence is boosting efficiency There’s no doubt about it: Artificial intelligence (AI) is changing the ecommerce game. Commerce teams have been using the technology for years to automate and personalise product recommendations, chatbot activity, and more. But now, generative and predictive AI trained on large language models (LLM) offer even more opportunities to increase efficiency and scale personalisation. AI is more than an ecommerce trend — it can make your teams more productive and your customers more satisfied. Do you have a large product catalog that needs to be updated frequently? AI can write and categorise individual descriptions, cutting down hours of work to mere minutes. Do you need to optimise product detail pages? AI can help with SEO by automatically generating meta titles and meta descriptions for every product. Need to build a landing page for a new promotion? Generative page designers let users of all skill levels create and design web pages in seconds with simple, conversational building tools. All this innovation will make it easier to keep up with other trends, meet customers’ high expectations, and stay flexible — no matter what comes next. 2. Businesses are prioritising data management and harmonisation Data is your most valuable business asset. It’s how you understand your customers, make informed decisions, and gauge success. So it’s critical to make sure your data is in order. The challenge? Businesses collect a lot of it, but they don’t always know how to manage it. That’s where data management and harmonisation come in. They bring together data from multiple sources — think your customer relationship management (CRM) and order management systems — to provide a holistic view of all your business activities. With harmonised data, you can uncover insights and act on them much faster to increase customer satisfaction and revenue. Harmonised data also makes it possible to implement AI (including generative AI), automation, and machine learning to help you market, serve, and sell more efficiently. That’s why data management and harmonisation are top priorities among business leaders: 68% predict an increase in data management investments. 32% say a lack of a complete view and understanding of their data is a hurdle. 45% plan to prioritise gaining a more holistic view of their customers. For businesses looking to take advantage of all the new AI capabilities in ecommerce, data management should be priority number one. 3. Conversational commerce is getting more human Remember when chatbot experiences felt robotic and awkward? Those days are over. Thanks to generative AI and LLMs, conversational commerce is getting a glow-up. Interacting with chatbots for service inquiries, product questions, and more via messaging apps and websites feels much more human and personalised. Chatbots can now elevate online shopping with conversational AI and first-party data, mirroring the best in-store interactions across all digital channels. Natural language, image-based, and data-driven interactions can simplify product searches, provide personalised responses, and streamline purchases for a smooth experience across all your digital channels. As technology advances, this trend will gain more traction. Intelligent AI chatbots offer customers better self-service experiences and make shopping more enjoyable. This is critical since 68% of customers say they wouldn’t use a company’s chatbot again if they had a bad experience. 4. Headless commerce is helping businesses keep up Headless commerce continues to gain steam. With this modular architecture, ecommerce teams can deliver new experiences faster because they don’t have to wait in the developer queue to change back-end systems. Instead, employees can update online interfaces using APIs, experience managers, and user-friendly tools. According to business leaders and commerce teams already using headless: 76% say it offers more flexibility and customisation. 72% say it increases agility and lets teams make storefront changes faster. 66% say it improves integration between systems. Customers reap the benefits of headless commerce, too. Shoppers get fresh experiences more frequently across all devices and touchpoints. Even better? Headless results in richer personalisation, better omni-channel experiences, and peak performance for ecommerce websites. 5. Brands are going big with resale Over the past few years, consumers have shifted their mindset about resale items. Secondhand purchases that were once viewed as stigma are now seen as status. In fact, more than half of consumers (52%) have purchased an item secondhand in the last year, and the resale market is expected to reach $70 billion by 2027. Simply put: Resale presents a huge opportunity for your business. As the circular economy grows in popularity, brands everywhere are opening their own resale stores and encouraging consumers to turn in used items, from old jeans to designer handbags to kitchen appliances. To claim your piece of the pie, be strategic as you enter the market. This means implementing robust inventory and order management systems with real-time visibility and reverse logistics capabilities. 6. Social commerce is evolving There are almost 5 billion monthly active users on platforms like Instagram, Facebook, Snapchat, and TikTok. More than two-thirds (67%) of global shoppers have made a purchase through social media this year. Social commerce instantly connects you with a vast global audience and opens up new opportunities to boost product discovery, reach new markets, and build meaningful connections with your customers. But it’s not enough to just be present on social channels. You need to be an active participant and create engaging, authentic experiences for shoppers. Thanks to new social commerce tools — like generative AI for content creation and integrations with social platforms — the shopping experience is getting better, faster, and more engaging. This trend is blurring the lines between shopping and entertainment, and customer expectations are rising as a result. 7. Vibrant video content is boosting sales Now that shoppers have become accustomed to the vibrant, attention-grabbing video content on social platforms, they expect the same from your brand’s ecommerce site. Video can offer customers a deeper understanding of your products, such as how they’re used, and what they look like from different angles. And video content isn’t just useful for ads or for increasing product discovery. Brands are having major success using video at every stage of the customer journey: in pre-purchase consultations, on product detail pages, and in post-purchase emails. A large majority (89%) of consumers say watching a video has convinced them to buy a product or service. 8. Loyalty programs are getting more personalised It’s important to attract new customers, but it’s also critical to retain your existing ones. That means you need to find ways to increase loyalty and build brand love. More and more, customers are seeking out brand loyalty programs — but they want meaningful rewards and experiences. So, what’s the key to a successful loyalty program? In a word: personalisation. Customers don’t want to exchange their data for a clunky, impersonal experience where they have to jump through hoops to redeem points. They want straightforward, exclusive offers. Curated experiences. Relevant rewards. Six out of 10 consumers want discounts in return for joining a loyalty program, and about one-third of consumers say they find exclusive or early access to products valuable. The brands that win customer loyalty will be those that use data-driven insights to create a program that keeps customers continually engaged and satisfied. 9. User-generated content is influencing ecommerce sales User-generated content (UGC) adds credibility, authenticity‌, and social proof to a brand’s marketing efforts — and can significantly boost sales and brand loyalty. In fact, one study found that shoppers who interact with UGC experience a 102.4% increase in conversions. Most shoppers expect to see feedback and reviews before making a purchase, and UGC provides value by showcasing the experiences and opinions of real customers. UGC also breaks away from generic item descriptions and professional product photography. It can show how to style a piece of clothing, for example, or how an item will fit across a range of body types. User-generated videos go a step further, highlighting the functions and features of more complex products, like consumer electronics or even automobiles. UGC is also a cost-effective way to generate content for social commerce without relying on agencies or large teams. By sourcing posts from hashtags, tagging, or concentrated campaigns, brands can share real-time, authentic, and organic social posts to a wider audience. UGC can be used on product pages and in ads, as well. And you can incorporate it into product development processes to gather valuable input from customers at scale. 10. Subscriptions are adding value across a range of industries From streaming platforms to food, clothing, and pet supplies, subscriptions have become a popular business model across industries. In 2023, subscriptions generated over $38 billion in revenue, doubling over the past four years. That’s because subscriptions are a win-win for shoppers and businesses: They offer freedom of choice for customers while creating a continuous revenue stream for sellers. Consider consumer goods brand KIND Snacks. KIND implemented a subscription service to supplement its B2B sales, giving customers a direct line to exclusive offers and flavours. This created a consistent revenue stream for KIND and helped it build a new level of brand loyalty with its customers. The subscription also lets KIND collect first-party data, so it can test new products and spot new trends. Ecommerce trends FAQ How do I know if an ecommerce trend is right for my business? If you’re trying to decide whether to adopt a new trend, the first step is to conduct a cost/benefit analysis. As you do, remember to prioritise customer experience and satisfaction. Look at customer data to evaluate the potential impact of the trend on your business. How costly will it be to implement the trend, and what will the payoff be one, two, and five years into the future? Analyse the numbers to assess whether the trend aligns with your customers’ preferences and behaviours. You can also take a cue from your competitors and their adoption of specific trends. While you shouldn’t mimic everything they do, being aware of their experiences can provide valuable insights and help gauge the viability of a trend for your business. Ultimately, customer-centric decision-making should guide your evaluation. Is ecommerce still on the rise? In a word: yes. In fact, ecommerce is a top priority for businesses across industries, from healthcare to manufacturing. Customers expect increasingly sophisticated digital shopping experiences, and digital channels continue to be a preferred purchasing method. Ecommerce sales are expected to reach $8.1 trillion by 2026. As digital channels and new technologies evolve, so will customer behaviours and expectations. Where should I start if I want to implement AI? Generative AI is revolutionising ecommerce by enhancing customer experiences and increasing productivity, conversions, and customer loyalty. But to reap the benefits, it’s critical to keep a few things in mind. First is customer trust. A majority of customers (68%) say advances in AI make it more important for companies to be trustworthy. This means businesses implementing AI should focus on transparency. Tell customers how you will use their data to improve shopping experiences. Develop ethical standards around your use of AI, and discuss them openly. You’ll need to answer tough questions like: How do you ensure sensitive data is anonymised? How will you monitor accuracy and audit for bias, toxicity, or hallucinations? These should all be considerations as you choose AI partners and develop your code of conduct and governance principles. At a time when only 13% of customers fully trust companies to use AI ethically, this should be top of mind for businesses delving into the fast-evolving technology. How can commerce teams measure success after adopting a new trend? Before implementing a new experience or ecommerce trend, set key performance indicators (KPIs) and decide how you’ll track relevant ecommerce metrics. This helps you make informed decisions and monitor the various moving parts of your business. From understanding inventory needs to gaining insights into customer behaviour to increasing loyalty, you’ll be in a better position to plan for future growth. The choice of metrics will depend on the needs of your business, but it’s crucial to establish a strategy that outlines metrics, sets KPIs, and measures them regularly. Your business will be more agile and better able to adapt to new ecommerce trends and understand customer buying patterns. Ecommerce metrics and KPIs are valuable tools for building a successful future and will set the tone for future ecommerce growth.

Influencing an audience to take action is tough business. Often, leads aren’t ready to buy, and when they are, it’s a challenge to keep them engaged. Thankfully, email makes it possible for you to gain influence with your desired audience over time. And with drip marketing, emails don’t have to live on an island unto themselves.A marketer’s job is to find and understand high quality leads, then help convert them to sales. Drip marketing is a solution for that, since it focuses on “dripping” emails into inboxes over time. And according to our research, email is still an extremely popular form of messaging. In fact, its use has increased year over year, accounting for 80% of all outbound messaging.In this piece, we’ll give you a clear understanding of drip marketing and how you can use it. We’ll dive into how you can build drip email campaigns that build on themselves organically and make your customers feel like every message they get is relevant for them.What is drip marketing?Drip marketing is a strategy that uses a series of automated, targeted emails to nurture potential customers. Content is pre-written or dynamic, based on the maturity of the company using drip marketing. The way these messages are sent out, or “dripped,” is usually tailored to match the behaviour or status of its target recipients. A drip marketing campaign aims to influence its target market over time, delivering a series of messages that follow a predetermined course.Successful drip marketing requires personalisation. Once you get your leads, you can figure out how to provide relevant content over time, which helps you chart and respond to the different stages of your customers’ journey. When you tailor messages based on customer behaviour, you can more easily segment your audiences.For example, your ongoing emails can take advantage of when a prospect abandons a web page. When someone leaves a product page without making a purchase, filling out a form, or watching a video, you can message them with product updates, offers, or ads that are relevant to the specific item or site the prospect was interested in.Other examples include when:A marketer promoting a software platform uses a series of emails to guide its new users through their platform’s key features, perhaps providing them with a free tutorial video.A marketer focused on online shopping offers personalised product recommendations based on a lead’s browsing and purchase history.A marketer promoting a new application engages customers with free trials demonstrating how the app works.What works well for one company may not be applicable to another. Understanding your target audience is the biggest challenge.Why use drip campaigns?Once you write up your messaging and schedule it through automation, you’ll really see how drip marketing can help. Within that efficiency lies the potential for your customers to take a direct action – a response like opting in to your email list or visiting your landing page.Consider the many ways drip marketing campaigns can keep your customers engaged:Personalisation: Drip campaigns allow for highly personalised and targeted communication. You can tailor content based on the recipient’s behaviour, preferences, and interactions with previous emails. It’s this personal touch that fosters a stronger connection between you and your customer.Automation: Your series of emails are designed to be sent at predetermined intervals or triggered by user actions. This consistent communication enables you to stay top-of-mind with your audience without all the manual effort.Nurturing leads: The content in an email drip marketing campaign, while part of a series, must also be able to stand on its own. For example, if a prospect doesn’t open the first two emails, but engages with the third, they ought to be able to understand the CTA regardless of the email they open. Don’t assume a subscriber reads every email in a series of emails; rather, ensure every email expresses context and relevance. That way, you’ll gain the trust and credibility required to nurture leads.Segmentation: You can segment drip campaigns based on criteria like demographics, behaviour, or previous interactions. When you’re able to send targeted content to specific audience segments, you know that recipients get relevant information according to their needs.Behavioural triggers: Drip campaigns can be triggered by specific actions like clicking a link or downloading a resource. If a user clicks on a particular product, subsequent emails can provide more information or special offers related to that product.Gradual relationship building: Instead of filling customers’ inboxes all at once, drip campaigns deliver content in a sequenced manner. This helps build interest and engagement over time, inspiring a meaningful and lasting relationship.Optimisation: You can easily adapt drip campaigns based on performance metrics and customer feedback. Ongoing analysis helps you refine your strategy, improving the relevance and effectiveness of your drip marketing emails.Become the email marketing GOATTired of your email campaigns not delivering results? Level up with this lesson on Trailhead, LIKE.TG’s free online learning platform.Start learning now+500 pointsModuleEmail Marketing StrategiesHow do you get started with drip marketing?An effective campaign strategy is only as good as its strategic foundation. Here’s how you can start a winning drip marketing campaign.First, have an outline of your campaign objectives. What is it, specifically, you want to achieve with your automated emails?Second, define your audience. Do you have a strategy for finding their demographics and behaviour?Next, research how to select the right marketing automation tools. (More on that later.)Even with those steps in place, you still need to pay attention to best practices.Let’s take a look at online shopping again. In terms of workflow, a drip campaign would begin with a welcome email, followed by product education or special offers. An automation trigger like a user sign-up sets the next stage of the customer journey.You will get much closer to guiding your lead toward conversion by making sure the content in each email is aligned with your brand messaging. Remember, establishing the trust and credibility of a prospect means building each email to be sequential but also with relevant, contextualised content that can stand alone.The consistency in your messaging throughout this journey reinforces your identity, making it no question who you are and what you offer. Tracking key metrics such as engagement and conversion rates helps you identify which emails resonate most with your audience. When you regularly test, you can make informed adjustments throughout the drip sequence.You’ve identified the goals like trigger-based emails, personalised content, and analytics tracking. The question remains: How to select the right automation tools?A few questions to consider as you do your research:Integration capabilities: Does the automation tool integrate seamlessly with your existing customer relationship management (CRM) tech, email platform, and other relevant systems to centralise data and streamline workflows?User-friendliness: How intuitive is the interface? Will it facilitate easy campaign setup, monitoring, and adjustment? What kind of learning curve will your employees face and how will you address it?Assess scalability: Can it scale with your growing needs? Will it accommodate an increasing volume of leads and allow for expansion?Review analytics and reporting: How robust are its analytics and reporting features? Remember, you’ll want to track key metrics, analyse campaign performance, and make data-driven optimisations.Personalisation options: Does it enable you to tailor content based on user behaviour, demographics, or other relevant factors? Using new generative and predictive artificial intelligence (AI) capabilities are an option.Security: To what degree does it comply with data protection regulations? Will it safeguard sensitive customer information throughout the automation process?How do you optimise drip campaign performance?Think about how much easier it is to personalise messaging when you actually know something about the person you’re addressing. To make your drip marketing campaigns on email even better, you should analyse user behaviour to understand how people interact with your brand.The first-party data you collect from website visits, clicks, and product or service usage helps you craft targeted messages or re-engage users who have shown interest in the past. You can collect data about demographic information (age, gender, location), behavioural data (purchase history, website interactions), and psychographic details (interests, values, lifestyle) by using analytic tools and CRM software.If your product or service has regional variations or if you want to target specific locations with location-specific content or offers, geographic segmentation can be valuable. Use surveys, social media monitoring, or customer feedback to gain a deep understanding of your audience’s interests.Often, a combination of segmentation criteria provides a better view of your audience. For example, you might target young professionals in specific industries who have shown interest in specific product categories. Evaluate and compare performance within each segment so that you know your personalised content resonates with the right segment.Your drip campaign’s success relies on other metrics, like engagement rates – the percentage of recipients who open and respond to your emails.A few other tips for improving your drip marketing results:Pay attention if your recipients are unsubscribing or if your emails bounce. You want to address deliverability or content issues quickly so that your communication is effective and well-received. Maintaining a responsive email list is good hygiene.How long does it take for a lead to move through the drip campaign and convert? Understanding the average duration for a lead to progress through the drip campaign helps refine your strategy, and data-driven adjustments along the way can enhance the campaign’s overall effectiveness.If your drip campaign spans multiple channels (email, social media, etc.), you can measure engagement on each platform to identify the most effective channels.Calculate the return on investment by comparing the revenue generated from the campaign against the costs incurred. This provides a clear picture of campaign profitability.Conduct A/B tests on various elements of your drip campaign (subject lines, content, CTAs) and analyse which variations perform better. Apply successful elements to future campaigns.For post-purchase drip campaigns, measure the impact on customer retention rates. Assess whether the campaign contributes to long-term customer loyalty.Most important, remember that audience preferences and behaviours change over time. You’re going to need to regularly update your segments based on new data and feedback so your drip campaigns stay relevant.What’s the appeal of drip marketing vs. traditional email marketing?Traditional email campaigns are often used for announcements, promotions, or general communication. Broadcast emails, for example, are those one-time messages sent to a large audience. They are great for time-sensitive announcements, promotions, and newsletters.In contrast, drip marketing takes a more personalised, gradual approach, providing relevant content at specific intervals. It builds a coherent and engaging narrative, which inspires stronger relationships. Leads are more likely to convert to sales with this kind of structured, thoughtful plan.There’s also a distinction between drip marketing and paid advertising. Paid advertising takes a broader, outbound approach, using display ads, search engine ads, or social media ads for exposure. Drip marketing, with its targeted communication, reflects an organic, relationship-building approach.Unlike these mass messaging methods, drip marketing delivers a sequence of targeted, personalised messages to prospects or customers over time. With it, you build and maintain relationships, and adapt content based on individual responses.Combining drip marketing campaigns with social media efforts can amplify reach and engagement. Sharing drip campaign content on social platforms extends the message’s visibility and encourages audience interaction.The key advantage of drip marketing lies in its ability to deliver relevant information at specific stages of the customer journey.What does the future of drip marketing look like?New privacy concerns are making third-party data feel obsolete. Drip marketing campaigns, with their sequential and targeted nature, allows you to build a narrative for individual customers. This storytelling approach invites audiences to safely share their preferences – adding to a robust array of first-party data your company collects directly from your audience, whether customer, site visitors, or social media followers. Marketers using drip campaigns should communicate the value of first-party data sharing and give consumers control over their preferences.AI is revolutionising how you can predict customer behaviours, and drip marketing that adopts AI for personalisation gives you the best of both worlds. The same is true for taking advantage of AI’s capability for adapting content based on customer behaviour. It’s vital that marketers communicate to their customers how they’re harnessing AI in order to further gain their trust and address their concerns. As with any new technology, continuous monitoring will be key.Other future-forward drip marketing predictions for consideration:Drip marketing will incorporate more interactive content, such as interactive videos, to enhance engagement.Drip marketing will integrate omni channels, including social media, chatbots, and messaging apps. Adapting content for each channel’s attributes and user behaviours will be vital.Drip marketing will optimise content for voice search, considering the conversational tone and concise messaging.Consolidated interactions across the customer journey will help marketers tailor campaigns. For instance, in the prospecting phase, a unified profile helps differentiate between leads in the awareness and decision-making stages, ensuring prospect drips are finely tuned to specific preferences.As customers progress through the lifecycle, the unified profile guides the transition from onboarding to retention drips, with behavioural triggers dynamically adjusting content based on engagement.Drip marketing is uniquely valuable in that it’s an extremely effective tool for both gathering and acting upon first-party data. Being able to gather and incorporate that high-value data while still functioning as a strong conversion tool gives drip marketing an important role in this privacy-conscious age of digital marketing.

In the dynamic digital marketing landscape, a lead magnet is a strategic tool designed to attract potential customers by offering something valuable in exchange for their contact information. This article explores the essence of lead magnets, what distinguishes a good lead magnet, the various types available, and a step-by-step guide on creating an effective lead magnet that captivates and converts. What Makes a Good Lead Magnet? 1. Be Relevant to Your Audience The effectiveness of a lead magnet hinges on its relevance to the target audience. Understanding your audience’s pain points, interests, and preferences is crucial. Tailoring your lead magnet to address their specific needs ensures a higher likelihood of engagement. 2. Provide Value to Your Audience Value is the currency of the digital realm. A good lead magnet should offer tangible benefits, whether solving a problem, providing valuable information, or offering a practical resource. The perceived value of the lead magnet is directly proportional to its ability to address the audience’s concerns. 3. Be Trustworthy Trust is the foundation of any successful customer relationship. Ensure that your lead magnet reflects the authenticity and credibility of your brand. Transparent and honest communication establishes trust, making your audience more comfortable sharing their information. 4. Make Your Audience Want More A compelling lead magnet should act as a teaser, leaving your audience intrigued and eager for more. It serves as the initial point of contact in a journey leading to more profound engagement with your brand. Pique curiosity and provide a glimpse of the value your business can deliver. 5. Be Shareable Content Encourage the vitality of your lead magnet by making it shareable. Whether it’s an insightful infographic, an educational e-book, or an entertaining video, content that can be easily shared across social media platforms expands its reach and attracts a broader audience. Types of Lead Magnets 1. E-books and White papers In-depth guides or research papers that provide valuable insights into a specific topic or industry. 2. Webinars and Workshops Live or recorded online sessions that offer educational content, allowing you to showcase expertise and interact with your audience. 3. Checklists and Cheat Sheets Quick-reference guides that condense information into easy-to-follow lists, aiding in practical tasks. 4. Templates and Tools Providing ready-to-use templates or tools that simplify a process or task for your audience. 5. Free Trials and Demos Allowing potential customers to experience a taste of your product or service before committing. 6. Quizzes and Assessments Interactive content that engages your audience while collecting valuable data for personalised follow-up. How to Create a Lead Magnet 1. Figure Out Who You’re Targeting and What They Want Understanding your audience is the foundational step. Conduct market research, analyse customer feedback, and create buyer personas to identify your target audience’s specific needs and preferences. 2. Create, Design, and Name Your Lead Magnet Once you’ve identified your audience’s needs, create content that directly addresses those needs. Whether it’s a comprehensive guide, a webinar, or a tool, ensure it is well-designed, visually appealing, and has a compelling title that sparks interest. 3. Build Your Conversion Path Have a Dedicated Landing Page Create a dedicated landing page for your lead magnet to eliminate distractions and guide visitors toward the desired action. Write a Clear Call to Action Craft a compelling call to action (CTA) that communicates the value of your lead magnet and prompts visitors to provide their contact information. Consider Eye-Scanning Patterns Design your landing page with consideration for eye-scanning patterns to ensure crucial information is prominently displayed, increasing the chances of capturing attention. Add Social Proof Enhance the credibility of your lead magnet by incorporating social proof, such as testimonials or user reviews, instilling trust in your audience. 4. Set a Schedule to Update Regularly Regularly update your lead magnet for ongoing success to keep it relevant and valuable. Set a schedule to refresh content, add new insights, or introduce variations to maintain audience interest. In conclusion, a well-crafted lead magnet is a powerful tool in the arsenal of digital marketers. By understanding your audience, providing value, ensuring trust, creating curiosity, and embracing shareability, you can develop lead magnets that capture contact information and lay the foundation for lasting customer relationships. Follow the step-by-step guide to create and optimise your lead magnet, and embark on a journey of meaningful engagement with your target audience.

What is Conversion Rate Optimisation? Conversion Rate Optimisation (CRO) is a strategic approach aimed at enhancing a website’s performance by increasing the percentage of visitors who take a desired action. This action, often called a conversion, can range from making a purchase, filling out a form, or subscribing to a newsletter. In essence, CRO is about maximising the effectiveness of a website and turning more visitors into customers or leads. What is a Conversion Rate? A conversion rate is a key metric in CRO, representing the percentage of website visitors who complete a desired action. This ratio is calculated by dividing the number of conversions by the total number of visitors and multiplying by 100. A higher conversion rate indicates that more visitors are taking the intended action, showcasing the website’s effectiveness in achieving its goals. What is a Good Conversion Rate? Determining a good conversion rate depends on various factors such as industry, type of website, and the specific action being measured. While average conversion rates vary, achieving a rate that exceeds industry benchmarks is generally considered favourable. Regular benchmarking against competitors and industry standards helps assess the effectiveness of your CRO efforts. How to Calculate Conversion Rate Calculating the conversion rate involves a simple formula: divide the number of conversions by the total number of visitors, then multiply by 100 to express it as a percentage. This straightforward calculation provides a clear snapshot of your website’s performance in converting visitors into customers or leads. CRO and SEO The relationship between Conversion Rate Optimisation (CRO) and Search Engine Optimisation (SEO) is symbiotic. A well-optimised website attracts more visitors through SEO and converts them effectively through CRO. A harmonious integration of both strategies ensures a seamless user experience, positively impacting search rankings and conversion rates. Where to Implement a CRO Strategy Strategically implementing CRO involves focusing on specific pages where user actions are crucial. Key areas for CRO include: 1. Homepage Optimising the homepage is vital for creating a positive first impression and guiding visitors toward desired actions. 2. Pricing Page A well-structured pricing page can significantly influence purchasing decisions, making it a prime location for CRO efforts. 3. Blog Enhancing the conversion potential of blog pages involves incorporating effective calls-to-action (CTAs) and lead-generation elements. 4. Landing Pages Testing and refining landing pages are critical as they are often the entry point for potential customers. CRO Formulas CRO Calculation 1: Conversion Rate Leads Generated ÷ Website Traffic x 100 = Conversion Rate % CRO Calculation 2: Number of Net New Customers New Revenue Goal ÷ Average Sales Price = Number of New Customers CRO Calculation 3: Lead Goal Number of New Customers ÷ Lead-to-Customer Close Rate % = Lead Goal Conversion Rate Optimisation Strategies 1. Create text-based CTAs within blog posts. Add compelling and strategically placed call-to-action buttons to your blog content to prompt user engagement. 2. Add lead flows on your blog. Utilise lead flows—pop-ups or slide-ins—on your blog pages to capture visitor information and convert them into leads. 3. Run tests on your landing pages. Perform A/B testing on landing pages to identify the most effective elements and layouts that drive conversions. 4. Help leads become MQLs. Guide leads through the marketing funnel by providing valuable content and nurturing them into Marketing Qualified Leads (MQLs). 5. Build workflows to enable your team. Establish efficient workflows to streamline communication and collaboration among team members involved in the CRO process. 6. Add messages to high-converting web pages. Implement live chat or targeted messages on high-converting pages to provide instant assistance and enhance the user experience. 7. Optimise high-performing blog posts. Identify and optimise blog posts that generate high traffic, ensuring they are conversion-focused and aligned with business goals. 8. Leverage retargeting to re-engage website visitors. Use retargeting ads to re-engage visitors who have previously shown interest in your products or services, increasing the likelihood of conversion. Expert Tips: How to Improve Conversion Rate Optimisation Implementing a successful CRO requires a combination of strategic planning and continuous optimisation. Here are expert tips to enhance your CRO efforts: Understand User Behaviour: Analyse user behaviour through tools like heatmaps and session recordings to identify areas for improvement. Mobile Optimisation: Ensure your website is optimised for mobile users, as many visitors access websites through mobile devices. Clear and Compelling Copy: Craft clear and persuasive copy for your CTAs, landing pages, and product descriptions to communicate value effectively. Loading Speed Optimisation: Optimise website loading speed to prevent user frustration and abandonment. User Testing: Conduct regular user testing to gather valuable feedback and identify usability issues. Social Proof: Incorporate social proof, such as customer testimonials and reviews, to build trust and credibility. Continuous Testing: Implement a continuous testing and experimentation culture to refine strategies based on real-time data. What is the purpose of conversion rate optimisation? The primary purpose of CRO is to enhance the efficiency of a website by maximising the number of visitors who take desired actions, ultimately leading to increased sales, leads, or other valuable conversions. What is a CRO strategy? A CRO strategy involves a systematic approach to improve website performance and user experience. It includes analysing data, setting goals, implementing changes, and continuously testing and optimising elements to boost conversion rates. What are CRO tools? CRO tools are software solutions that help businesses optimise their websites for better conversion rates. These tools encompass a range of functionalities, including A/B testing, heatmaps, analytics, and user behaviour tracking, providing valuable insights for effective CRO strategies.

If you’re asking whether you need to implement generative artificial intelligence (GAI) tools to support your business, you’re not alone. This technology can boost employee productivity, but is it safe? While these tools can help from marketing to customer service to data insights, business leaders have posed concerns about AI’s potential impact and dangers on society and some are calling for generative AI regulations. What you need to know A global AI regulatory response has started to coalesce; in the U.S., lawmakers met with tech leaders in mid-September, and declared universal agreement on the need for AI regulation. The EU has already begun to audit AI algorithms and underlying data from the major platforms that meet certain criteria. As a business decision maker, you need to understand GAI — and how it impacts your work with other companies and consumers. “Most countries are just trying to ensure generative AI is subject to existing measures around privacy, transparency, copyright, and accountability,” said Danielle Gilliam-Moore, Director, Global Public Policy at LIKE.TG. What your company can do now Review generative AI products on the market and see what makes sense for your business. Ask: Do I need to build it internally or work with a third-party vendor, like LIKE.TG, to add its products to our tech stack? Be aware of any risks. The exec summary The climate around GAI is moving at breakneck speed and regulators are trying to understand how the technology may affect businesses and the public. Here are some recent headlines: Pope Francis calls for global treaty to regulate AI after a deepfake of him wearing a puffer coat goes viral. EU reaches landmark deal on world’s first comprehensive AI regulation. IBM and Meta launch 50-member AI Alliance with large corporations, start-ups, and universities around the world to push for responsible AI. US government’s new AI rules push companies to show how their tech is safe. The backstory on generative AI regulations Concerns around artificial intelligence (AI) date back years when discussions covered possible job loss, inequality, bias, security issues, and more. With the rapid growth of generative AI after the public launch of ChatGPT in November 2022, new flags include: Privacy issues and data mining: Companies need to have transparency around where they’re gathering data and how they’re using it. Copyright concerns: Because GAI tools pull from vast data sources, the chance of plagiarism increases. Misinformation: False information could spread more quickly with AI chatbots, which also have created entirely inaccurate stories called hallucinations. Identity verification: Is what you’re reading created by a human or chatbot? There is the need to verify articles, social media posts, art, and more. Child protection: There’s been a call to ensure children and teenagers are protected against alarming, AI-generated content on social media. This has all prompted regulators worldwide to investigate how GAI tools collect data and produce outputs and how companies train the AI they’re developing. In Europe, countries have been swift to apply the General Data Protection Regulation (GDPR), which impacts any company working within the EU. It’s one of the world’s strongest legal privacy frameworks; the U.S. does not have a similar overarching privacy law. That may change, with calls for more generative AI regulations.. “These are a lot of the same concerns we’ve seen previously wash up on the shores of the technology industry,” Gilliam-Moore said. “Right now, regulatory efforts, including investigations, seem to focus on privacy, content moderation, and copyright concerns. A lot of this is already addressed in statute, so regulators are trying to make sure that this is fit for purpose for generative AI.” Get articles selected just for you, in your inbox Sign up now What considerations do businesses need to make? Companies continue to wonder how these tools will impact their business. It’s not just what the technology is capable of, but also how regulation will play a role in how businesses use it. Where does the data come from? How is it being used? Are customers protected? Is there transparency? No matter where your company does business or who you interact with — whether developing the technology for other companies to use or interacting directly with consumers — ensure you speak with lawyers who are following generative AI regulations and can help guide you through your process. “Talking with your trusted advisers is always a good first step in all of this,” Gilliam-Moore said. “Innovation is happening at an incredible speed. So the conversations we’re having now could become stale in the next six months.” Regulators have been concerned about how companies collect data and how that information gets delivered to users. Having an acceptable use policy – an agreement between two or more people (like a business and its employees or a university and students) outlining proper use when accessing a corporate network or internet — can help safeguard compliance. In addition, it is important to show data provenance, a documented trail that can prove data’s origins and where it currently sits. “Without data, none of this works,” Gilliam-Moore said. How can small businesses stay compliant? Larger corporations can often invest in the research and development around the technology, especially to stay compliant. Smaller businesses may not have the resources to do their due diligence, so asking vendors and technology partners in their ecosystem the right questions becomes important. While LIKE.TG is taking steps to develop trusted generative AI for its customers, those customers also work with other vendors and processors. They need to stay aware of potential harms that may exist and not just trust blindly. Gilliam-Moore said smaller companies should ask questions including: Are you GDPR compliant? Are you HIPAA, or whichever law regulates your industry, compliant? Do you have an acceptable use policy? What are your certifications? What are your practices around data? Do you have policies that try to provide guardrails around the deployment of this technology? “If you’re a smaller company, you may need to rely upon the due diligence of your third-party service providers,” Gilliam-Moore said. “Look at the privacy protocols, the security procedures, what they identify as harms and safeguards. Pay close attention to that.”

Generative AI chatbots are helping change the business landscape. But they also have a problem: They frequently present inaccurate information as if it’s correct. Known as “AI hallucinations,” these mistakes occur up to 20% of the time. “We know [current generative AI] has a tendency to not always give accurate answers, but it gives the answers incredibly confidently,” said Kathy Baxter, principal architect in LIKE.TG’s ethical AI practice. “So it can be difficult for individuals to know if they can trust the answers generative AI is giving them.” You might hear those in the computer science community call these inaccuracies confabulations. Why? Because they believe the psychological phenomenon of accidentally replacing a gap in your memory with a false story is a more accurate metaphor for generative AI’s habit of making mistakes. Regardless of how you refer to these AI blunders, if you’re using AI at work, you need to be aware of them and have a mitigation plan in place. The big trend People have gotten excited (and maybe a little frightened, especially when used at work) about generative AI and large language models (LLMs). And with good reason. LLMs, usually in the form of a chatbot, can help you write better emails and marketing reports, prepare sales projections, and create quick customer service replies, among many other things. In these business contexts, AI hallucinations may lead to inaccurate analytics, negative biases, and trust-eroding errors sent directly to your employees or customers. “[This] is a trust problem,” said Claire Cheng, senior director, data science and engineering, at LIKE.TG. “We want AI to help businesses rather than make the wrong suggestions, recommendations, or actions to negatively impact businesses.” It’s complicated Some in the industry see hallucinations more positively. Sam Altman, CEO of ChatGPT creator OpenAI, told LIKE.TG CEO Marc Benioff that the ability to even produce hallucinations shows how AI can innovate. “The fact that these AI systems can come up with new ideas, can be creative, that’s a lot of the power,” Altman said. “You want them to be creative when you want, and factual when you want, but if you do the naive thing and say, ‘Never say anything you’re not 100% sure about’ — you can get a model to do that, but it won’t have the magic people like so much.” For now, it appears we can’t completely solve the problem of generative AI hallucinations without eradicating its “magic.” (In fact, some AI tech leaders predict hallucinations will never really go away.) So what’s a well-meaning business to do? If you’re adding LLMs into your daily work, here are four ways you can mitigate generative AI hallucinations. 1. Use a trusted LLM to help reduce generative AI hallucinations For starters, make every effort to ensure your generative AI platforms are built on a trusted LLM. In other words, your LLM needs to provide an environment for data that’s as free of bias and toxicity as possible. A generic LLM such as ChatGPT can be useful for less-sensitive tasks such as creating article ideas or drafting a generic email, but any information you put into these systems isn’t necessarily protected. “Many people are starting to look into domain-specific models instead of using generic large language models,” Cheng said. “You want to look at the trusted source of truth rather than trust the model to give you the response. Do not expect the LLM to be your source of truth because it’s not your knowledge base.” When you pull information from your own knowledge base, you’ll have relevant answers and information at your fingertips more efficiently. There will also be less risk the AI system will make guesses when it doesn’t know an answer. “Business leaders really need to think, ‘What are the sources of truth in my organisation?’” said Khoa Le, vice president of Service Cloud Einstein and bots at LIKE.TG. “They might be information about customers or products. They might be knowledge bases that live in LIKE.TG or elsewhere. Knowing where and having good hygiene around keeping these sources of truth up to date will be super critical.” Get articles selected just for you, in your inbox Sign up now 2. Write more-specific AI prompts Great generative AI outputs also start with great prompts. And you can learn to write better prompts by following some easy tips. Those include avoiding close-ended questions that produce yes or no answers, which limit the AI’s ability to provide more detailed information. Also, ask follow-up questions to prompt the LLM to get more specific or provide more detailed answers.You’ll also want to use as many details as possible to prompt your tool to give you the best response. As a guide, take a look at the below prompt, before and after adding specifics. Before: Write a marketing campaign for sneakers. After: Write a marketing campaign for a new online sneaker store called Shoe Dazzle selling to Midwestern women between the ages of 30 and 45. Specify that the shoes are comfortable and colorful. The shoes are priced between $75 and $95 and can be used for various activities such as power walking, working out in a gym, and training for a marathon. 3. Tell the LLM to be honest Another game-changing prompt tip is to literally direct the large language model to be honest. “If you’re asking a virtual agent a question, in your prompt you can say, ‘If you do not know the answer, just say you do not know,’” Cheng said. For example, say you want to create a report that compares sales data from five large pharmaceutical companies. This information likely will come from public annual reports, but it’s possible the LLM won’t be able to access the most current data. At the end of your prompt, add, “Do not answer if you can’t find the 2023 data” so the LLM knows not to make up something if that data isn’t available. You can also make the AI “show its work” or explain how it came to the answer that it did through techniques like chain of thought or tree of thought prompting. Research has shown that these techniques not only help with transparency and trust, but they also increase the AI’s ability to generate the correct response. 4. Lessen the impact on customers Le offers some things to consider to protect your customers’ data and business dealings. Be transparent. If you’re using a chatbot or virtual agent backed by generative AI, don’t pass the interface off as if customers are talking to a human. Instead, disclose the use of generative AI on your site. “It’s so important to be clear where this information comes from and what information you’re training it on,” Le said. ”Don’t try to trick the customer.” Follow local laws and regulations. Some municipalities require you to allow end users to opt in to this technology; even if yours doesn’t, you may want to offer an opt-in. Protect yourself from legal issues. Generative AI technology is new and changing rapidly. Work with your legal advisors to understand the latest issues and follow local regulations. Make sure safeguards are in place. When selecting a model provider, make sure they have safeguards in place such as toxicity and bias detection, sensitive data masking, and prompt injection attack defenses like LIKE.TG’s Einstein Trust Layer. Generative AI hallucinations are a concern, but not necessarily a deal breaker. Design and work with this new technology, but keep your eyes wide open about the potential for mistakes. When you’ve used your sources of truth and questioned the work, you can go into your business dealings with more confidence.

Prompt engineering is not Rocket Science: Learn at a Top Level how easy it can be to get what you want from AI. Have you always wanted a friendly, fast assistant who simply does what you say? In many cases, AI applications like ChatGPT can offer you exactly that. But it only works with your help. To get beneficial results out of AI, you need to master prompt engineering. Don’t panic; you don’t need an IT degree for this. Prompt Engineering is about asking the right questions and giving the right commands to generative AI’s like ChatGPT to get the information you need. In principle, this programming language is very close to our normal language. Before we get into it, we want to make sure you know about LIKE.TG AI Solutions – By using our Einstein GPT platform, AI insights are right at your fingertips. We have complete training modules to help you interact with our AI-based platform, with guidance on how to build prompts, and information on how our AI platform works for various sides of businesses such as Sales, Customer Service, Marketing and Commerce. What is Prompt Engineering? Prompt Engineering is the art of tailoring your input to create specific, desired responses from AI applications, whether through commands or questions. This empowers People and organisations to harness the full potential of AI technologies, such as LIKE.TG Einstein, by refining the way users interact with these systems to achieve greater productivity and superior results. Key Terms to Understand NLP (Natural Language Processing): A field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. Many AI applications use this as their base model, including ChatGPT. Intent Recognition: This is a fundamental NLP concept. It involves understanding the underlying purpose of a user’s message or query, a cornerstone for effective prompt engineering. Entities: In NLP, entities are specific pieces of information extracted from user input, like dates or product names. Dialogue Flow: Dialogue flow in AI/NLP is how a chatbot or AI system talks to you: understanding what you say, replying, and keeping the conversation smooth and on track. Training Data: High-quality data is used for training NLP models, which enhances your prompt engineering by improving intent recognition and response generation. Context: In prompt engineering, context refers to the conversation’s history and information, which is crucial for multi-turn interactions. A Beginner’s Look at Prompt Engineering In this post, we will focus on text prompts. But you can get almost anything from a prompt: including pictures! This work was created using the free version of Anime-style Clickdrop, an interface for various tools (Stable Diffusion XL by Stability AI, a deep learning text-to-image generator.) We tried this out with a straightforward prompt: “Show me a picture with blue water and mountains.” So, just think of a prompt like a short and precise briefing. If your generative AI tool receives a meaningful and relevant prompt, it can give you an appropriate answer. And what does Open AI, the creators of ChatGPT, say about prompt engineering? Examples of Prompt Engineering We’ve explained above what a prompt engineer does and how to get started. But what does that look like in practice? Our first example shows how well ChatGPT works when you put a little more thought into your prompt. Here is an example of ChatGPT helping us create Buyer Persona’s. Here is our prompt: “Create five dark chocolate buyer personas using whatever customer survey data you can find. Create a table of personas and give each a name, benefits, and use cases.” Note: If you had your own data to formulate, ChatGPT would use it and include it within its response. You can also ask ChatGPT to put it in a table for you: To master generative AI tools, enter the suitable prompts, and consider these three prompt engineering principles: 1) Provide context: Should AI develop personas as above? Then, specify for which product or service. 2) Be specific: It’s tempting, but don’t get caught up in chatting; it confuses GPT programs. Be brief and precise about what is expected. 3) Proceed Step by Step: Sometimes, slow is better. Break your prompt into multiple detailed questions. Chain-of-thought Prompts Explained Chain of Thought (CoT) is a way for AI to respond in conversations that make sense by considering what was said before, so it feels like a natural chat. At this point, anyone who is not an IT professional may have their head spinning, trying to understand the difference between AI and prompt engineering. Simply put, Prompt engineering is the main factor in the effective use of AI tools. The prompt is the interface between the users and large AI language models (LLM – Large Language Model) like ChatGPT. Nobody has to remember that. But you should know that these language models are based on natural language. LLMs are machine learning models. But they generate human-like texts, and that’s precisely why they work so well. The Real GPT Revolution: Prompts are a new form of code that is entered as text and delivers instant results. This makes us all coders— no specialist knowledge required. For users of Prompt Engineering (and we should all become familiar with it): We have to ensure that the texts we generate achieve the desired goals. So embrace the benefits of prompt engineering, but also be aware of the limitations of generative AI. If the prompt isn’t good, the results aren’t good either. Lastly, let’s look at another example of prompting. To do this successfully and give us good results, stick to the three principles described above. Here are a few prompt types to get you started – all created with the free version of ChatGPT. The Pareto Principle Prompt: “I want to learn about [insert topic (we said “daffodils”)]. List the top 20% of insights on this topic and share them with me, so I can understand 80%.” The Pareto Principle Prompt is a practical way to find out more about a topic. The Power Networking Prompt (Perfect for Cold Emails) Prompt: “Give me a cold calling email chain with parenthetical sections that can be customised to specific people or companies.” Perhaps a bit more content than necessary, but definitely useful as a starting point and can be used as inspiration. You can always click “Regenerate Response” to revise the output text, or Enter “shorten” as a prompt if the output is too long for you. Peak into the Mind of Leaders Tap into the collective wisdom of the top Fortune 500 CEOs with this prompt. We entered “green energy”. “Interview a team of CEOs from Fortune 500 companies on [TOPIC/QUESTION]. Create instructions and strategies to make [TOPIC/QUESTION] possible as if these CEOs answered them.” Advancing your Prompt Skills In these examples, we give ChatGPT a plethora of Real Estate data in point form and ask it when might be a good time to buy property in the area. Here is the output: It gives an unbiased answer as we posed a tricky question, “when is it good to buy in the region?”. But this type of prompt can give you a top-level understanding of what is happening in the region. Users of ChatGPT4 can take it a step further and get the AI to create charts to help them visualise the data, simply by asking it to do so. Harness your potential as a Prompt Engineer and explore the boundless opportunities that lie ahead. It’s important to remember that while generative AIs are incredibly capable, they generate responses from vast online data sources and are sometimes flawed. Protecting sensitive data should always be a top priority for you and your organisation. To enhance the value of your data and capitalise on its potential, consider the synergy between your data and ChatGPT. This partnership opens the door to creating fresh and captivating experiences for your customers, positioning you as a leader in the AI revolution unfolding.

Everyone wants generative AI applications and their groundbreaking capabilities, such as creating content, summarising text, answering questions, translating documents, and even reasoning on their own to complete tasks. But where do you start? How do you add large language models (LLMs) to your infrastructure to start powering these applications? Should you train your own LLM? Customise a pre-trained open-source model? Use existing models through APIs? Training your own LLM is a daunting and expensive task. The good news is that you don’t have to. Using existing LLMs through APIs allows you to unlock the power of generative AI today, and deliver game-changing AI innovation fast. How can a generic LLM generate relevant outputs for your company? By adding the right instructions and grounding data to the prompt, you can give an LLM the information it needs to learn “in context,” and generate personalised and relevant results, even if it wasn’t trained on your data. Your data belongs to you, and passing it to an API provider might raise concerns about compromising sensitive information. That’s where the Einstein Trust Layer comes in. (More on this later.) In this blog post, we’ll review the different strategies to work with LLMs, and take a deeper look at the easiest and most commonly used option: using existing LLMs through APIs. As LIKE.TG’s SVP of technical audience relations, I often work with my team to test things out around the company. I’m here to take you through each option so you can make an informed decision. 1. Train your own LLM (Hint: You don’t have to) Training your own model gives you full control over the model architecture, the training process, and the data your model learns from. For example, you could train your own LLM on data specific to your industry: This model would likely generate more accurate outputs for your domain-specific use cases than a general-purpose model. But training your own LLM from scratch has some drawbacks, as well: Time: It can take weeks or even months. Resources: You’ll need a significant amount of computational resources, including GPU, CPU, RAM, storage, and networking. Expertise: You’ll need a team of specialised Machine Learning (ML) and Natural Language Processing (NLP) engineers. Data security: LLMs learn from large amounts of data — the more, the better. Data security in your company, on the other hand, is often governed by the principle of least privilege: You give users access to only the data they need to do their specific job. In other words, the less data the better. Balancing these opposing principles may not always be possible. Get articles selected just for you, in your inbox Sign up now 2. Customise a pre-trained open-source model (Hint: You don’t have to) Open-source models are pre-trained on large datasets and can be fine-tuned on your specific use case. This approach can save you a lot of time and money compared to building your own model. But even though you don’t start from scratch, fine-tuning an open-source model has some of the characteristics of the train-your-own-model approach: It still takes time and resources, you still need a team of specialised ML and NLP engineers, and you may still experience the data security tension described above. 3. Use existing models through APIs The last option is to use existing models (from OpenAI, Anthropic, Cohere, Google, and others) through APIs. It’s by far the easiest and most commonly used approach to build LLM-powered applications. Why? You don’t need to spend time and resources to train your own LLM. You don’t need specialised ML and NLP engineers. Because the prompt is built dynamically into users’ flow of work, it includes only data they have access to. The downside of this approach? These models haven’t been trained on your contextual and private company data. So, in many cases, the output they produce is too generic to be really useful. A common technique called in-context learning can help you get around this. You can ground the model in your reality by adding relevant data to the prompt. For example, compare the two prompts below: Prompt #1 (not grounded with company data): Write an introduction email to the Acme CEO. Prompt #2 (grounded with company data): You are John Smith, Account Representative at Northern Trail Outfitters. Write an introduction email to Lisa Martinez, CEO of ACME. Acme has been a customer since 2021. It buys the following product lines: Edge, Peak, Elite, Adventure. Here is a list of Acme orders: Winter Collection 2024: $375,286 Summer Collection 2023: $402,255 Winter Collection 2023: $357,542 Summer Collection 2022: $324,573 Winter Collection 2022: $388,852 Summer Collection 2021: $312,899 Because the model doesn’t have relevant company data, the output generated by the first prompt will be too generic to be useful. Adding customer data to the second prompt gives the LLM the information it needs to learn “in context,” and generate personalised and relevant output, even though it was not trained on that data. The more grounding data you add to the prompt, the better the generated output will be. However, it wouldn’t be realistic to ask users to manually enter that amount of grounding data for each request. Luckily, LIKE.TG’s Prompt Builder can help you write these prompts grounded in your company data. This tool lets you create prompt templates in a graphical environment, and bind placeholder fields to dynamic data that’s available through the Record page, flows, Data Cloud, Apex calls, or API calls. But adding company data to the prompt raises another issue: You may be passing private and sensitive data to the API provider, where it could potentially be stored or used to further train the model. Use existing LLMs without compromising your data This is where the Einstein Trust Layer comes into play. Among other capabilities, the Einstein Trust Layer lets you use existing models through APIs in a trusted way, without compromising your company data. Here’s how it works: Secure gateway: Instead of making direct API calls, you use the Einstein Trust Layer’s secure gateway to access the model. The gateway supports different model providers and abstracts the differences between them. You can even plug in your own model if you used the train-your-own-model or customise approaches described above. Data masking and compliance: Before the request is sent to the model provider, it goes through a number of steps including data masking, which replaces personal identifiable information (PII) data with fake data to ensure data privacy and compliance. Zero retention: To further protect your data, LIKE.TG has zero retention agreements with model providers, which means providers will not persist or further train their models with data sent from LIKE.TG. Demasking, toxicity detection, and audit trail: When the output is received from the model, it goes through another series of steps, including demasking, toxicity detection, and audit trail logging. Demasking restores the real data that was replaced by fake data for privacy. Toxicity detection checks for any harmful or offensive content in the output. Audit trail logging records the entire process for auditing purposes. How the Einstein 1 Platform works The Einstein 1 Platform abstracts the complexity of large language models. It helps you get started with LLMs today and establish a solid foundation for the future. The Einstein 1 Platform powers the next generation of LIKE.TG CRM applications (Sales, Service, Marketing, and Commerce), and provides you with the tools you need to easily build your own LLM-powered applications. Although Einstein 1 is architected to support the different strategies mentioned earlier (train your own model, customise an open-source model, or use an existing model through APIs), it is configured by default to use the “use existing models through APIs” strategy, which lets you unlock the power of LLMs today and provides you with the fastest path to AI innovation. The Einstein 1 Platform combination of Prompt Builder and the Einstein Trust Layer lets you take advantage of LLMs without having to train your own model: Prompt Builder lets you ground prompts in your company data without training a model on that data. The Einstein Trust Layer enables you to make API calls to LLMs without compromising that company data.