How to Use AI to Uncover Customer Lifetime Value
Discover how to use customer lifetime value with AI and predictive analytics to drive long-term growth and loyalty.
Pooja Mathur, VP of analytics at Merkle, explains how businesses can leverage customer lifetime value (CLV) with AI and predictive analytics to enhance customer experiences, build long-term relationships, and drive sustainable growth.
Organizations are recognizing the importance of delivering exceptional customer experiences to exceed the demands of their audiences. Customer lifetime value (CLV) provides a holistic view of the customer journey, enabling businesses to identify pain points and opportunities for improvement.
While economic uncertainty underscores the importance of building long-term relationships, the ever-changing digital and AI landscape gives access to a wealth of data, allowing brands to change the way they interact with consumers, making CLV more important than ever.
Customer lifetime value is a metric that represents the total worth of a customer to a business over the entire duration of their relationship. It encompasses all past purchases and interactions and uses predictive analytics to make inferences on future value, too. It serves as a crucial indicator of a customer’s long-term profitability.
Sounds great, right? But you can’t just start calculating a metric this all-encompassing overnight. Let’s talk about how to make it happen.
Start Small
To get started, use a crawl, walk, run phased approach to incrementally unlock value and complexity.
In the initial crawl phase, establish a foundation grounded in rule-based methodologies. Begin by focusing on one of your flagship products or services, meticulously defining its inherent value. The value construct at this phase could be formula-driven, taking key components into account, such as:
- Current revenue (premium, interest income, transaction income)
- Current costs (such as claims, fixed and variable overhead, losses, etc.)
The duration of this value assessment can vary based on the product, as can the definition of various components of CLV. Alignment across teams on the value definition is critical for successful implementation. A simple start can help define the net present value (NPV) by discounting future cash flows to present value. Next, focus on using this as a building block to define the customer’s lifetime value.
Enhance the Definition
Once the baseline is established, enhance the value definition by bringing in individual-level differentiation for all components that define the NPV and include additional components to build the customer lifetime value. CLV accounts for future potential:
- Churn
- Up-sell/cross-sell opportunities
- Loyalty enhancement by recognizing value through word of mouth, positive reviews, etc., which are often overlooked in CLV programs.
AI can be used to predict these future events at the individual level by leveraging a combination of first- and third-party data. In addition to building the overall CLV, these individual components can, in turn, help drive retention, up-sell/cross-sell, next-best action, and offer efforts.
Ultimately, all these components can come together to inform the next best experience, which is what the customer is most concerned about.
Remember that insights enable informed decision-making regarding tailored offers for customers while also revealing hidden gems among your clientele—the most valuable individuals worthy of targeted attention and engagement. “Most valuable” here could mean the customers who are likely to upgrade or stay loyal for years to come over those who may go to a competitor.
Through advanced machine learning techniques, AI can continuously refine your CLV model based on historical data, enabling businesses to forecast customer lifetime value and predict conversion likelihoods. AI-powered predictive analytics offer personalized recommendations and targeted marketing strategies, maximizing conversion rates for valued prospects and enhancing customer satisfaction.
See More: Customer Satisfaction: The New Battleground for ERP
What Advanced CLV Could Look Like
Let’s say a leading insurance brand utilizes CLV to understand its customers’ current value and predict their future worth. Through data analysis, the brand forecasts that the average policyholder, in addition to their initial five-year tenure, is likely to renew their policy multiple times, leading to an extended lifetime value beyond the initial period.
By leveraging this predictive aspect of CLV, the brand can implement proactive retention strategies tailored to various stages of the customer lifecycle. For instance, they might offer personalized policy reviews or additional coverage options as customers approach the end of their initial policy term. Furthermore, by accurately forecasting the future value of each customer, they can better allocate resources more effectively, investing more heavily in long-term relationship-building initiatives that yield higher returns over time.
Recognizing the potential of CLV is a game-changer for brands navigating the complex “experience economy” that we find ourselves in. By delving into customer data and understanding the long-term worth of each individual customer, businesses can tailor their strategies to forge lasting relationships and drive sustainable growth. CLV helps brands to make more informed decisions, personalize interactions, and prioritize efforts where they matter most. As brands continue to refine their CLV strategies, they position themselves not only for short-term success but also for enduring relevance and profitability.
Brands can elevate CLV to the next level with segmentation, which integrates customer needs, desires, and motivations gleaned from research with demographics, lifestyle interests, and channel preferences obtained from third-party data sources to craft a comprehensive persona. By marrying segmentation with CLV, brands can unlock a wealth of insights to tailor personalized experiences, targeted marketing strategies, and product offerings that resonate deeply with each segment of their customer base to maximize engagement.
In conclusion, by measuring CLV, brands can better understand their audiences, anticipate needs, and tailor their strategies to build lasting relationships. With a phased approach, brands can take meaningful steps to leverage CLV – ultimately leading to a sophisticated strategy that combines AI and segmentation to craft personalized experiences that resonate deeply with each customer.
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