Customer churn analysis is one of those things that you have to live with in any service based industry, SaaS included. By now, you’re undoubtedly familiar with churn, the gradual entropy of customer loyalty and the resultant loss of established customers. It’s one of the bigger negatives to have to contend with in this kind of business.
We’ve talked before about customer churn analysis as being important in finding the hurdles to growth, and overcoming them. We’ve also talked about this when it comes to customer metrics and some mathematical application of those.
Well, you came here to get some insight into this kind of analysis, to get some tricks and tips for doing it well. Unfortunately, there’s not a lot to say about this in a technical sense, because it’s a pretty simple process for gathering churn data.
All you really have to do is compare paying existing customers from the previous period to the ones present in the new period once all the base metrics are taken. This will directly show the churn as a defined phenomenon.
It can be displayed as a bell curve or any number of other overly complicated graphs and visualizations, but in the long run, it’s just a simple ratio of customers lost in a period.
Now, above this, people often use multiple churn measurements over several periods to create a bigger curve of churn rates over a significant period of time. As for analyzing the data this gives you?
Well, it’s more of an indicator than a statistic, and it shows, potentially, the presence of a number of problems such as sustained need being lacking, or customer dissatisfaction for one reason or another.
So, the real trick is going to be to first find out why customers are leaving. And, finding that out is often an indirect exercise, using BI software to snoop. Programming it to listen in on the internet, through social networks and other channels, and surmising from this why people are not sticking around. People are pretty blunt online, so in enough time, you will determine the most likely causes of churn from here.
I know you expect something far deeper and complex here, like some math formulae or something, to give you greater insight into churn and how to analyze it, but mercifully, that’s just not the case here.
In the past, some of these metrics and the analysis and use of the data from them involved painfully complex, overblown use of arcane mathematics, which made explaining them a Texas-sized headache. This one’s pretty direct and straightforward.
Customer churn analysis is simple about spotting differences in established constants, and inferring the meaning of the phenomenon. From there, you just use wise data capture strategies to find out what’s so wrong that it’s causing churn of any significant level. Then, you address it as best you can. I suppose something could be said for knowing what makes for concern or simple unavoidable outcome. If your percentage is higher than ten, then you have a very serious problem, and one you must devote resources and planning in order to remedy.