Customer churn is like a leaking faucet that, if left unchecked, can erode the foundation of your business. So getting a handle on what's causing it and what can be done to fix it is critical, especially in highly competitive marketing environments.
Companies often rely on customer service agents to "save" customers who call to say they are taking their business elsewhere. But by this time, it is often too late to save the relationship. The customer may stay if you offer them a substantial discount, but without a good understanding of why they want to leave and what can make them stay, the discount is only delaying the inevitable.
So what can you do?
Predictive Models Can Improve Customer Churn
One action you might consider is developing a statistical model that can help you predict the likelihood and timing of a customer's propensity to churn. Armed with this information, you may be able to save those customers with preemptive marketing programs designed to retain them.
But before you go too far developing programs, you should first "look under the covers" of the model and evaluate the factors that appear to be driving churn. What behaviors are customers exhibiting (or not exhibiting) in the months leading up to a churn event? Are they engaging with your brand (how and how often)?
Evaluating the factors that are contributing to any changes in brand engagement can help you determine your best course of action -- which may range from any number of things such as improving your customer service, ongoing communications, recognizing customers for their loyalty, and/or product improvements etc.
When In the Customer Lifecycle Are Customers Most Likely to Churn?
Answers to this question could provide some insight into potential gaps that may exist in your customer lifecycle communications strategy.
As you continue your analysis, look for behaviors that appear to affect some customers more than others (the so-called interaction effect) and when they occur. A churn model can help you learn whether some sub-segments of your customer base are more susceptible to certain issues or problems than others. If this is the case, some proactive education (tailored to the sub-group) could help address the issue before the problem gets out of hand.
The point here is that a churn model can be more than just a predictive tool. It can also help point to the issues that are causing the churn and the types of customers most affected by those issues. Armed with that insight, marketers should be in a much better position to develop programs that are not just targeted, but also tailored with specific messages to the segments of customers who are most at risk.
About the author: Ken Howes is an analytical director at Peppers & Rogers Group. Contact him at firstname.lastname@example.org.