In today's customer-centric market, it's essential to understand a customer's lifetime value (CLV). Customer Lifetime Value helps businesses concentrate their activities around their most "profitable" clients. The better a business understands CLV, the better its strategy at retaining its best customers.
Unfortunately, many enterprises continue to ignore this crucial information.
Any debate about CLV naturally converges on the Pareto Principle: 20% of your customers represent 80% of your sales. CLV is the discounted value of future profits generated by a customer.
To put it even more simply, it's a measure of the revenue you will make from a customer over their buying life cycle.
Calculating Customer Lifetime Value, however, is complex.
To determine the lifetime value, you need to perform CLV modeling. For that, you need three key inputs: Recency, Frequency, and Monetary Value, better known as RFM.
How many CLV (Customer Lifetime Value) models?
According to this post by Google, you can use CLV models to get answers to questions related to:
- The number of purchases a customer will make over their lifetime
- How long to wait before considering a customer gone for good
- Predicting how much revenue you'll generate from a customer in the future
Prospect new customers with the lowest churn and highest LTV? >>> Contact us
2 Modeling Methods for Customer Lifetime Value
There are two ways to look at CLV — historic CLV and predictive CLV. The landmark method analyzes past data to assess a customer's value. In this case, we don't try to predict the future value of that customer's purchases.
So the question is: what if you could classify which customers make up the 20% in the Pareto Principle, not just in the past, but in the future as well? That's where predictive CLV modeling comes in.
With Predictive Customer Lifetime Value, we can:
- Forecast the future value of existing customers with transaction history
- Predict the future value of first-time customers
Let's focus on predictive customer lifetime value. Our goal is to model customers' purchasing behavior to predict their future actions. But remember, not all businesses need this kind of model.
First, you should understand whether predictive CLV makes sense for your business.
If predictive CLV is right for your business, then use both. Otherwise, focus on historic CLV.
Modeling with Customer Lifetime Value
A key step in CLV Modeling is fitting a customer to a probabilistic model. This is done by taking the RFM values for all the customers, then dividing the data into segments that the model shows are likely to behave similarly.
The most common example is to group customers on a scale of 1 to 4 for each of their RFM values, and then consider all customers who fall into a particular group, such as people who are in Group 1 for all three values.
Every probabilistic model that uses RFM values includes a customer ID, an order date, and an order value.
Today, it's essential to understand a customer's lifetime value (CLV).
Want to know more about Express Analytics' Predictive CLV Modeling? Speak to Our Experts to get a lowdown on how CLV Modeling can help you.
We can use Artificial Intelligence to help with Customer Lifetime Value modeling.
Machine Learning (ML), a subset of AI, combines algorithms and statistics to perform a specific task without human intervention. The algorithm finds patterns in the data set. The algorithm uses these patterns to accomplish the task more effectively. ML is a key tool in predicting CLV.
In part 2 of this article, we'll look at the various ML models and how they work.
Stop guessing customer value. Start measuring and growing CLV with actionable insights >>> Schedule a free consultation with our experts to learn how we can help you implement CLV modeling solutions to enhance customer retention, improve profitability, and gain a competitive advantage.
