Reduce Churn – Predict Which Customers Will Leave Before They Do

Just as important as generating more customers is holding on to the customers you already have. In fact, the saying, “A penny saved is a penny earned,” translates pretty directly to, “A customer saved is a customer earned.” So let’s apply our customer data action items in reverse and see what happens.


›  Billing Data

›  Lifetime Value for Customers and Segments

›  Email Addresses

›  Targeted Ad Campaigns

›  Activators

›  An Efficient Web Review Process


At this point, if you implemented the tips in my other blog posts you know the demographics, interests, websites visited, web behaviors and predicted lifetime value of your customers. You also probably have a record of customers who have quit, left, abandoned the pipeline, etc. Let’s start there. Take the data for your customers who quit and start segmenting the interest and demographic data collected via activators. You may find that all of the customers who quit never received your awesome newsletter, or maybe they all hate football and received your football newsletter every month. Maybe they are all over 30 years old and you depress them with blogs about college life. Whatever the case, you will see commonalities leap out of the data.

With these characteristics in mind, you can query your Customer Data Platform for similar customers who have not cancelled and save them. Stop serving them content they hate. Send them a special or include them on a newsletter that will interest them. You don’t have to predict, you have their firsthand interest data at your fingertips. Engage these users in a unique and different way and you will see the return.

Finally, look at customers who came in from ad groups, campaigns and channels that have a low lifetime value (as identified by your Customer Data Platform). Use low LTV values to understand which customer segments are vulnerable to churn, and understand that you are already fighting an uphill battle with those users.