At least, don’t model churn as your only step.
A very common approach to churn reduction is to build predictive models which are intended to produce a score for the likelihood a customer will churn within a given time period. The approach is so common there are libraries of code available online for this purpose. ChatGPT can provide Python code for the task.
But is it the right thing to do?
We have been helping marketers reduce churn for over 20 years. In our experience, predicting churn is a good first step, but you cannot stop there.
You actually want to predict not just churn risk, but the probability you can save the relationship, given your history of communication and offers used for this purpose.
The highest churn risk scores may be pointing you to customers that are more than halfway out the door anyway. Not only will your communications and offers not dissuade them...it’s too late...it might actually make things worse. There is a “wake up” effect we have seen where at-risk customers realize their contract is up for renewal, for example, and it’s time to shop around, not jump at your first offer.
Think recovery, not just risk.
Next, think about the value of the relationship. What is the predicted lifetime value in the coming years, after your planned intervention? How long will they stay with you, what revenues would you predict over that period, and at what profit margins? Knowing this number gives you the starting point for a key question: what are you prepared to spend to save this relationship?
Extrapolate that question across all risk-save scores and you get a sense of the envelope of budget you need to reduce churn, as well as a sense of how much of a dent you can make in the churn rate.
Another way to think about churn reduction is as a problem in optimizing individual contacts....what offer, message, channel and timing. This is where a product like our 1:1 Optimization system can help direct traffic and make both planning and execution easier.
Finally, you probably don’t have a churn problem (what??). You probably have several. Customers don’t all leave for the same reason, and a tactic designed to keep them for one irritant may be completely irrelevant, even counterproductive for another.
Consider an example from wireless services. What if a customer is irritated about billing problems, not resolved with your customer service team, repeatedly? Consider another customer who is irritated about dropped calls. What can you do to counter these two problems? Would the same tactic work for both? Would a tactic designed to work well for one be as effective for the other?
You should think about a modelling solution that provides risk scores, save scores, and what we call Action Codes. Action codes combine an evaluation of why a customer is at risk with an action you choose to suppress that risk. All three scores can be funneled to the customer-facing systems you will use to deliver messages, whether that is an all purpose CRM system, a channel focused system such as for email, or other.
Put it all together and you have the answers you need to plan effective churn programs and translate strategies into individual action plans.
Best of luck and if we can help, don’t hesitate to contact us.
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