Retention metrics explained: Customer Future Value Pt. 1 [RS Labs]

Customer Future Value is the next metric in our ‘Retention Metrics Explained’ series and it builds upon what we described about customer churn in our previous post.

What is Customer Future Value?

In this post we build upon churn, to explain Customer Future Value (CFV). Customer future value is the amount of profit margin you can expect from a particular customer in the future. If we can predict whether or not a customer will purchase and if we can predict how much that person is likely to spend, then we can predict how much that person is expected to spend in the future. We call this Customer Future Value (CFV).

As a quick aside, the notion of Customer Lifetime Value (CLV, sometimes called Lifetime Value (LTV)) is a standard metric in SaaS and eCommerce businesses. However, at Retention Science we explicitly separate CLV into two parts: the deterministic order history (which is based on order histories) and the predictive, future value that customer will bring to the business, which is the CFV.

CLV = Previous order history + CFV

CFV is powerful prediction for a number of use cases. Fundamentally, it informs which customers will be worth more in the future, and therefore are worth nurturing. It also influences the types of discounts you may present to customers to keep them happy. A low CFV customer just might not be worth that 10% coupon, because even if it brings him or her back into the fold, that customer won’t spend enough to justify the offer. From a retention perspective, CFV gives a company the insights required to understand the value of their retained customers, and what impact that has on their revenue. For instance, it allows a company to quantify how their efforts to reduce churn impact their expected revenue from those actions.

As with churn, CFV can provide powerful insights to your business at both the individual customer level (e.g., send specific offers to high or low CFV customers) and at the holistic level of your business. For instance, consider a plot of CFV for your whole customer base (the CFV distribution). There are clearly whole segments that will contribute significant amounts to the revenue, and lots of customers that will not. At Retention Science we group users by CFV, using an approach based on statistical similarity, bucketing users into low, medium and high CFV groups. This allows marketers to target specific groups with campaigns (for instance, send VIP invitations to the high CFV segment to ensure their retention) and provides high-level insights, such as 20% of a company’s customers are expected to provide more than 80% of the revenue (see Figure 3.1 below). This highlights the necessity for retention where both keeping those top customers is crucial and where re-engaging the lower CFV customers can also dramatically increase revenues (for instance, by converting lower CFV customers into repeat purchasers).

Consider Figure 3.1 below, which plots that user’s contribution to the total CFV (x-axis is the percentage of users and y-axis is the percentage of total CFV). From the graph, 20% of the users are expected to contribute more than 80% to the future revenues, based on CFV. That’s an astounding finding for a business (though not a total surprise if you follow Pareto’s observations).

 

CFV_Pt1_3_1

Figure 3.1: Percent of users plotted with percent contribution to CFV

How is CFV predicted?

There are two components to the CFV, the likelihood that someone will purchase something and the expected revenue that will be generated by those purchases. For simplicity in our explanation, we will focus only on companies with subscription businesses. This is because if a company is a subscription company, then its customers don’t make purchase decisions in an ad-hoc manner (e.g., at a specific purchase time) but rather he or she makes a choice to start, stop or suspend the purchase cycle.

For subscription businesses, the likelihood that someone will make a purchase is tied directly to the churn for that person. Since a purchase is only made by subscribing, either a customer is purchasing (not churned) or not (churned). That is, the likelihood that someone will purchase (we call this the “purchase likelihood” (PL))  is essentially defined as how much we believe the person will not churn. So, if we define churn as the probability someone will stop being a customer, then we can define their purchase likelihood (PL) as 1 minus the churn.

PL = 1 – Churn Score (for subscription-only businesses)

Now that we know how likely it is for someone to make a purchase, we have half of our inputs for the CFV. Next, we need to figure out how much someone is likely to spend. For that, we introduce the Average Order Value (AOV). In the case of our subscription example, assume that a customer stays subscribed for 6-months, and that there is only one subscription option and it costs $10.00 each month. Then one simple model is to assume the AOV is simply $10.

AOV = Total order amount / Num recent purchases 

Predicting the average amount a user will spend is also a rich and interesting problem. For instance, you could assume that someone’s past purchase behavior is enough information to predict how much that person might spend in the future. Or you could assume that all of the customers are more or less the same, and use global information about your entire customer base to predict this value.

Now that we have the AOV, and the PL, we define CFV as:

CFV = (PL * AOV) * Time window for CFV

Time window for CFV is the time amount for the CFV expectation such as 3 months or 6 months. In our subscription model, this becomes:

CFV = ( (1 – Churn Prob) * AOV) * Num expected future purchases

Earlier we assumed one purchase every month, time window for CFV is identical to the number of the expected future purchases. At Retention Science CFV forms a fundamental unit for many of our advanced techniques for increasing customer revenue, but those are topics for another day.

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About the Author

Sang Su Lee is a data scientist at Retention Science. He is interested in solving less scientific problems in a scientific way. He received his M.S. and Ph.D. in Computer Science from the University of Southern California and B.S. in Electrical Engineering from Yonsei University.