We argued in our article on commercial churn modelling that you want to predict not only the probability of a customer leaving you but even more importantly what you can do about it. We want to predict why the customer is churning or, more precisely, his likelihood to stay (given that he was likely to leave) after we extend an offer or perform an action from a list of activities for churn management, as well as his profitability after the save.
In the previous piece we did not consider the question of how you determine these reasons for churn, so let us turn to that briefly here.
You could try asking the customers who are leaving. This is unlikely to give you the answer you are looking for, but I still recommend that you do it, and that you do it regularly.
The idea here is to ask people who have just left you why and what (if anything) you could have done to keep them. Make sure that this is not a sales call. Try a script in the spirit of: “I know you have just left us and I am not trying to change your mind—I just want to ask you what made you leave and what we would have had to have done to keep you: if helps us to become better.” Do this for a sample of your leavers every week or month and track the answers you are getting.
If you are like every other company I have ever done this with, then by far the majority of people will answer “price”, which is why I said it would be unlikely to give you the answer you need for your commercial churn modelling.
But it is still a very useful exercise. First, understand the answers that were not about price, track them as they change over time when your competitive landscape changes, and devise saves activities that profitably address these issues.
Second, dig into those “price” answers. Is it really about the core price of your product offering, or something peripheral which you could change without incurring wholesale margin erosion and ideally change below-the-line only? Maybe you are in the insurance industry and people leave because a competitor has a good offer for bundling all policies with them, in which case you can address this specific price without touching individual policies. Maybe you are in the mobile telecommunications industry and a customer is leaving because he gets cheap on-net calls with a competitor: can you get his friends and family onto your network and save him that way?
Keep talking to your customers and keep trying to understand what are precisely the offers or activities that will retain them, rather than just reaching for across-the-board price cuts.
Customers think or say that price determines their actions even though that is not the case nearly as often as you might think.
Learn by doing
So if asking the (recently departed) customer is not going to give you the answers, what will? As usual in marketing, you need to test, measure, and learn.
You have some sort of saves activity going on for your customers. (You have, haven’t you?) Assign and extend a random retention offer to each of a random selection of your churning customers. That gives you the answer to what you really want to know: what offers work in retaining which customers. Very quickly you will begin to discern the clusters of customers that respond to the different offers. Ideally, you will have real-time predictive analytics installed in the front office to continually optimise the offer mix (and most solutions can do well on this type of problem) or any half-decent analytics team should be able to run the analysis daily or weekly and update the offer mix and rules.
The beauty is that as you keep running these trials you get more data with allows you to see deeper into your customer base and identify smaller behavioural segments. You can also test many more offers over time, helping you to manage your margins ever better and ever smarter than the competition.
(You will eventually have to consider the people you do not reach at the time they churn to understand their behaviours and feed that into your product and advertising plans, but let’s fix the ones we can save first.)
What are your best ideas for churn management? Let us know in the comments.