When I first started working at my company, the business was still in “growth” mode. Our goal was to consistently grow our customer base, by bringing in more new customers each month. About 5 years ago, that growth started to slow. At the time, one of the leaders of the finance organization recommended that we should focus more on customer retention. We had a good amount of new customers coming in each month, but we had barely any strategy for keeping customer successful and satisfied throughout their tenure with us.
The company started to put more focus on customer retention shortly after that. One important part of this was getting an idea of what retention looked like historically. We knew that our average customer tenure was 34 months, but that was it. We didn’t know if there was a certain month of tenure, or a certain customer milestone where retention would drastically decline. To build a story of historical retention, I built a retention cohort analysis.
Each row included a cohort, which is the number of customers that were new to our business each month. Each column represented a different month of tenure for the customers in that specific cohort (first month, 2nd month, etc). If you look at the spreadsheet below, you can see that you can get a sense of how customers are staying over time. Data like this can reveal any glaring declines in retention after a certain tenure, or after a certain point in time.
After putting the analysis together, the next step was to find insights from the data. In our case, retention had been very consistent. We never saw any significant declines after a certain tenure or after a certain cohort month. Our retention rate would decline a few percentage points each month, and it was pretty consistent from cohort to cohort. The only times it dipped were certain cohorts where we offered a month end promotion for X number of months. After X months, we’d see a significantly larger decline than normal.
Now that we had our historical story, we wanted to use this to track the new cohorts coming in each month. For the most part, we made note of changes the company made, and would follow retention performance after that. For example, if we made a change to our pricing in October 2014, we would note that, and if performance changed, we would have a good hypothesis as to why. Keep in mind that we would always A/B test something like that, but this cohort analysis is a good compliment, and it’s useful for senior leadership who just want the high level company metrics, without going into too much detail. Knowing whether an A/B test is successful is very important, but it doesn’t show the impact on the overall business.
If your business model is a month-to-month subscription model, this analysis is pretty simple to build. As long as you can track when customers started paying for your business, and have records of monthly invoices/payments, then you can put an analysis like this together. If you’re subscription model requires payments in 6/12/etc month contracts, then this may be harder to build. It’s harder to pinpoint exactly where customers decided to stop paying. My suggestion here would be to figure out what customer engagement metrics are indicators of retention. Whether it’s customer logins, product engagement, etc, you could build a similar analysis with that data, and use that as a proxy.
DISCLAIMER: For confidentiality reasons, the actual metrics values included in this post have been adjusted. However, the story is still an accurate portrayel of the project, and what occured.