Revenue Retention Analysis: What to Look For (Cont’d)

July 1, 2010

This blog post is about how to interpret the data and results of a billings/revenue retention analysis, and focuses on trends and patterns that you should look for. It is part of a series of posts that serve as a step-by-step guide on conducting the analysis from start to finish. Beyond the insights you will gain, conducting the analysis will be helpful for most expansion stage companies hoping to raise expansion capital. Many venture capital firms will perform this analysis at some point during the due diligence process. Presenting this data upfront will save them time and likely impress their management teams with your “metrics-driven approach” to management.

In the last post, I covered some trends, patterns, and anomalies you should look for in a total billings by cohort chart. While a total billings by cohort chart can give you many insights into a business, the average billings by cohort chart — which illustrates how the average customer bill in a specific cohort changes over time — is very useful as well. A lot of information can be gleaned from it, and it is worth spending some time to analyze.

An increase in the average customer bill indicates that either customers are buying more over time, smaller customers have a higher churn rate than larger customers, or potentially both. While an increase in average bill over time seems good, it could also point to a problem. For example, if a company had a few large customers and many small customers that contributed most of the revenue, high churn among the smaller customers would increase the average bill over time.

Also, if you see from the average billings chart that the average bill changes dramatically over time, it may make sense to analyze churn by customer size and by customer bill amount. You may find that there is significant variability in customer retention in different customer size buckets, and that can present the company with opportunities to focus on selling to a customer size that has the highest revenue retention, or address issues that cause customers of a certain size to have high churn. For example, if customers that spend between $1000 and $2000 per month churn at a much lower rate than customers that spend between $250 and $500, the company should contemplate shifting their focus to selling to that type/size of customer.


Vlad is a CEO at <a href="">Scandent</a>, which develops radio frequency identification (RFID) systems that prevent theft, loss, and wandering/elopement in hospitals and nursing facilities. Previously, he was an Associate at OpenView.