Brands are actively trying to be customer-centric. However, many brands are mistaking personalization for empathy, and the result is an inability to meet customer expectations. In its 2019 State of the Connected Customer Report, Salesforce surveyed more than 8,000 consumers and found that while 73% expected the brands they engaged with to understand their needs, only 51% said those brands generally did so well. Similarly, 62% of those surveyed (and 67% of Millennials and Gen Zers) believed brands should adapt based on consumers’ behaviors and actions, yet only 47% believed this was currently happening.
What is CLV?
Let’s start with the academic definition. Customer lifetime value is a prediction of the expected net profit attributed to the expected lifetime of each customer, as a function of their current recency, frequency, and monetary value. In other words, increase the frequency and amount that you spend at a store, and your CLV goes up. Take six months off between visits and fail to purchase, and that score goes down.
Advances in machine learning have improved two key aspects of the metric: the ability to predict when the next purchase is expected, and what that purchase should look like. CLV is a vehicle for brands to move from being descriptive—understanding how customers acted and why—to the crucial step of being predictive, where they can anticipate what customers will do next and when. Instead of internal disputes over attribution, CLV is a single universal metric that can rally teams together and meet customer expectations.
CLV across the customer journey
While CLV is historically known as a metric for identifying retention and churn, it leads to more rich insights with applications across the customer journey. Only focusing on loyalty takes away from the full power of the model. Here are a few ways CLV can impact the entirety of the journey.
CLV can be used in segmentation and to develop look-alike modeling and referral programs.
Insights from CLV drive personalization, CLV-based offers, and exclusive experiences.
And, of course, CLV is instrumental in individual churn reduction, tiered loyalty programs, and the prioritization of improvements in retention strategies.
Creating a portfolio of customers
The myth has long been that demographic-based customer personas derived from thousands or millions of people were enough for marketing segmentation. But with a CLV model, you’re working with historical purchase and behavioral data at an individual level. Now, take that singular customer’s historical data and multiply it by ten thousand or one hundred thousand. What you get is a portfolio of customers. Those customers become an asset that you can track, chart, and manage over time.
Brands often get caught focusing on only high-CLV customers or trying to pinpoint their “average” customers. The issue with those strategies is customer portfolios rarely form a bell curve. The majority of customers for any brand are low value, where high-value customers are among the smallest groups. Too much revenue from the low-value customers and the cost to acquire drives down profitability. But high value comes with big expectations, and if even a few leave, it can damage future revenue gains. That’s why it’s important to track all CLV cohorts and monitor the risk for churn or opportunities to grow value.
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Gain more insights into why and how brands are shifting focus toward their customers by downloading a free copy of our customer-centricity guide.