Using appropriate metrics for managing complex businesses is vital for success. In this article, we will discuss a metric used by a few publishers to keep their daily customer management decisions aligned with their long-term objectives. We call it Total Customer Lifetime Value, or Total CLV.

Customer lifetime value (CLV) is the present value of future operating margins from an individual customer. This metric is used in many industries, and it has many helpful qualities.

First, it addresses the difference between revenue and profit. Many high-revenue customers are not as profitable as low-revenue customers once the costs to serve them are measured carefully. Digital customers, for instance, often pay less than print customers, but their incremental costs to serve are very low. Second, this metric spans multiple time periods so future operating margin changes are captured in today’s decision making. We most often use three-, four-, or five-year time periods in our calculations.

Aggregating CLV across an entire subscriber base — adding up the lifetime value of all active customers — offers a view of how week-to-week decisions are affecting the overall customer relationships and future profitability for a publisher. This is the metric we call Total CLV.

During a particular week there will be new customers acquired, customers lost, renewal price increases, retention campaigns, customer service centre changes to customers’ accounts, and many other actions that affect subscribers’ revenues and costs. Many of these actions have offsetting effects on retention and revenue. Price increases raise revenue per week but lower retention; retention campaigns increase costs but increase retention.

Customer service actions usually involve changing a customer’s service and price together. CLV is a good framework for evaluating the success of these initiatives, and combining the net effect of all actions taken with subscribers in a week is an excellent metric for performance.

It is important the CLV calculation includes a dynamic forecast of future lifespan for a customer. Using historical retention curves for calculating CLV will fail to capture the effects of today’s actions on future retention. For instance, when a customer’s price increases, his expected retention will decrease, and this effect will vary significantly by customer. Adding services to a subscriber will increase retention, but it will also affect direct costs.

The graph below represents the effect of a price increase at point A on a customer’s probability of churning in the future. The additional revenue from the rate increase will be offset in part by the reduction in expected active life from that customer. The distance between points B and C, the likely retention with and without a price increase, can vary significantly by customer. The net effect of the changes in rate and volume will determine the success of the rate increase.

Customer lifecycle management involves many decisions affecting the profitability of customers over time and understanding the tradeoffs of these decisions is difficult. In addition, subscription management decisions have multi-year consequences that are hard to quantify. Many audience groups are judged on current-year performance.

Similarly, public companies must report yearly numbers to the market. There is a risk that actions taken to meet this year’s targets will have negative effects on future years’ performance, which may lead to more short-term decisions in the future.

Looking at the CLV of new customers and lost customers is interesting. New customer CLV can vary dramatically depending on the acquisition channel and offer. Using CLV to evaluate new acquisition is a much better approach than the number of starts due to the high churn poor offers will experience. Long-term offers, where a customer’s price is held constant over a term of his choosing in return for a commitment to remain active over that term, will have very high CLV. (I wrote about this topic in a prior blog post for INMA.)

In the chart below, we show average CLV for new customers and stopped customers by week. Early in this time period, through 2015, most of the new customers had lower CLV than stopped customers. This was due to the nature of the acquisition offers used by this publisher. Beginning in 2016, the average CLV of new customers begins to exceed the CLV of stopped subscribers as the publisher changed the terms of its new offers to increase retention and raise the average offer price.

The next chart shows the change in Total CLV by week for this publisher. All data has been re-scaled to protect this company’s information, but the trend remains accurate. The change in new customer acquisition strategy has increased Total CLV over 2016, 2017, and 2018. Other factors that have raised Total CLV are annual renewal price changes.

From this chart, it is interesting to note decisions made about customer acquisition in 2015 are still positively affecting 2018 performance. A benefit of the CLV approach to customer lifecycle management is that it quantifies the hard-to-observe effects of strategic decisions. It can also provide a breakeven threshold for retention campaigns to target.

For instance, if a publisher invests US$10 in a retention incentive, how many additional weeks of active life are needed for that investment to earn a return?

Total CLV is an effective performance metric for subscription businesses that is not commonly used by publishers. We hope this introduction to the topic is the first step for others to begin using it as a piece of their customer relationship management processes.

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