Why You Need to Predict Customer Lifetime Value
This article is part of our Predictive Frameworks series, which explores the most effective use cases for predictive analytics.
Two people begin a relationship with your business. One makes a single purchase and vanishes after a month, never to be seen again. The other makes purchases monthly and sticks around for years, enjoying your products and services for the long haul.
Wouldn’t it be nice to know, from the very first purchase, which one is which? The long-term customer who buys regularly and is devoted to your brand: That’s the customer you undoubtedly want to acquire, embrace, and retain.
Therefore, it’s imperative to identify those customers by predicting customer lifetime value (LTV) early in their journey with your business. LTV is sometimes known as CLTV or CLV and, in the mobile apps/games world, as pLTV, which stands for “predicted lifetime value.” Same concept!
It’s easy to get swept up in short-term considerations when there’s pressure to acquire as many customers as possible. But playing the long game by focusing on LTV is much more likely to drive better and more sustainable business growth. And with innovative predictive methods that make the most of your customer data, you can be better equipped than ever. You can predict the future of high-value customers and nurture them for long-term success.
Whether companies explicitly recognize it or measure it, they are already in relationships with their customers. It’s just a question of strength and value of knowing how important you are to your various customers. Who’s a best friend? An acquaintance? A fling? Who are the customers who snapped up that 75 percent-off daily deal, never to return? Who are the long-term, committed partners who haven’t been getting the attention from you that they should? There’s actually a way to answer these questions, with the precision that only math can deliver. The metric we use to understand customer relationships is known as customer lifetime value, or CLV. A CLV model predicts how much each of your relationships will be worth over its lifetime. It’s quickly becoming the indispensable measure for marketers trying to understand if they are creating sustainable value for their business or merely positioning themselves between transactions.
Chief Measurement Strategist at Google, in his book Converted: The Data-Driven Way to Win Customers’ Hearts
Watch our on-demand discussion featuring Neil for more details on this important concept!
Historical Analysis of LTV Offers Limited Value
A typical approach used to calculate customer lifetime value is retrospective. In other words, it looks at past data sets to see which existing customers were worth the most.
Typically, you’d total up an individual customer’s purchases and divide that sum by the count of their purchases. That would tell you the average purchase value for that customer. A higher average purchase value would mean a customer was more valuable to your business over time.
What would you do with this LTV information? First, you might create customer segments by LTV, then look for their demographic and purchasing behavior patterns. Next, you could do statistical analyses or create visualizations based on your gut feelings about which variables might set high-value customers apart. Finally, you could seek out more potential customers with those same profiles. But you’d assume (maybe incorrectly) that they’d become high-value customers.
As you might already notice, this approach has some serious limitations:
- What if a customer identified here as high-value just made their very last purchase from your business? You can’t tell from this approach whether that customer will continue a relationship. You might invest in nurturing customers who are already out the door, or spend too much on customers who would stay with you anyway. (Hey, we’d also love to help you predict customer churn!)
- What about new customers? With no purchase history to look back on, there’s nothing to calculate with this retrospective approach. You might do some “look-alike” segmentation to guess their trajectories, but that’s probably not much better than a guess. Instead, you could efficiently identify future high-value customers and quickly act to solidify your relationship with them.
- Is your analysis of historical data the best way to find future high-value customers? Humans can stare at data all day and never see complex patterns, even using BI tools. But predictive models powered by artificial intelligence can spot the patterns in seconds.
A historical method for using LTV has limitations — while a predictive approach to LTV can offer far more robust support for your performance goals. Let’s explore how.
How Better Understanding LTV Benefits Businesses
When marketing resources are limited, predicting and focusing on future high-value customers is critical to success. Building customer loyalty is everything in this time of rapid change, economic uncertainty, reduced brand allegiance, and unpredictable customer behavior. As a result, identifying and keeping customers happy — especially high-value customers — is more critical than ever.
Customer lifetime value prediction with machine learning can enhance and personalize customers’ experience. Data science methods are far better at parsing complex data and determining how variables interact to shape different outcomes.
In addition, when humans look for patterns in data, their search is biased by their preexisting assumptions. Machine learning models bring an impartial perspective to the data and reveal novel predictive insights.
Personalization is a force multiplier — and business necessity — one that more than 70 percent of consumers now consider a basic expectation. Organizations able to build and activate the capability at scale can put customer lifetime value on a new trajectory — driving double-digit revenue growth, superior retention, and richer, more nurturing long-term relationships.
Predicting LTV Benefits Customers, Too
When you identify your future high-value customers, you can dedicate marketing resources to campaigns designed to acquire and retain them. With that focused approach, you can increase your return on ad spend (ROAS) and reduce customer acquisition costs (CAC).
Identifying patterns in the data of your current high-value customer base is invaluable. This method allows you to understand which new customers will also likely evolve into high-value customers within a specific time period. That’s possible even early in your relationship with them.
Whatever industry you’re in, your current and future high-value customers are your greatest resource. Focusing on their needs and personalizing their experience can bring outstanding results.
In a follow-up blog post, we explored exactly how this process works and discuss some examples of putting predicted LTV to work. Check it out now — or, if you’re already ready to learn more, get in touch today!
Read more in the Predictive Frameworks series:
- Stop Customer Churn in its Tracks with Predictive Analytics
- Scaling Demand Forecasting with AI-Powered Predictive Analytics
- Predicting Success for Cross-Sell & Upsell Offers
- How to Implement High-Converting Cross-Sell and Upsell Strategies with Predictive Analytics
- Is Predicting LTV Really Worth It?