Revenue Retention Analysis: Sample Output and Interpretation

This blog post is about analyzing and interpreting data in a billings/revenue retention analysis, and 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.

Once you have calculated the total spend over time, average customer bill over time, and monthly spend as a percentage of first month spend over time for each customer cohort, you can create charts that nicely visualize the trends. In this post, we will cover how to interpret the total billings via a quarterly cohort chart shown below:

This chart illustrates total billings by customer cohort by month, and shows us that, in general, billings by cohort increase over time, or that each cohort acts like a perpetuity trend with growth. Also, it seems that, for the most part, each subsequent cohort starts at a higher billing amount than the previous, meaning that the company is signing up more customers each quarter or that they are charging more or potentially both. The fact that total billings by cohort increase over time is very desirable. However, to verify that the distribution economics are strong, this analysis needs to be combined with the sales and marketing cost to acquire each cohort of customers. If the company recoups its sales and marketing spend in one year or less, then the billings growth by cohort over time is spectacular.

While billings for most of the cohorts are increasing over time, there are two anomalies — specifically the Q3 and Q4 2008 cohorts. The total billings for those two cohorts decreased dramatically in month 2 (75% decrease), and then started increasing gradually after that. There are many possible explanations for this phenomenon. Perhaps there were significant product issues at the time, or the company could have signed up customers who did not find the product useful, causing customers to cancel their subscriptions in month 2.

There is also the possibility that a few customers who signed up in Q3 and Q4 2008 were billed upfront for the first year, which would have resulted in a steep billings drop-off in month 2. To understand whether this is the case here, it would be helpful to create a chart that shows average customer billings by cohort (to see if the average bill declined proportionally in month 2, which would indicate that customers are not churning and that upfront billing is the probable cause of this anomaly). If this is indeed the cause of the decline in month 2, it would serve as a good illustration of why customers on different billings cycles need to be separated, and why separate revenue/billings retention analysis need to be created for each billings cycle.

We will investigate further next week. Stay tuned!