Customer churn is a tough and persistent problem, but even incremental improvement can be quite valuable for organizations. Those who take the customer churn problem seriously are using data to try to crack it, and some are using complex statistical analyses and teams of data scientists to solve it. Yet, many teams are surprised to find that, despite their familiarity with the problem and the sophistication of their data ops, there are a couple important gaps that are holding them back.
1. You Might Have a Data Problem
Many practitioners that concern themselves with churn focus on a handful of “usual suspect” reasons for churn: a customer progressed through on-boarding but failed to fully realize value from the product, the sponsor who purchased the product and evangelized it to the rest of the organization left, etc. Some savvy practitioners look a bit more broadly than that.
But there are myriad things that influence churn that aren’t being recognized, and remedied, by companies who maintain a narrow focus on the most obvious influences and the narrow data set that describes them. Did your company put out an ad campaign that angered customers? Perhaps your organization made the news… in a negative way.
It’s important to keep in mind that your customers’ interaction with your brand is frequently bigger and broader than what you can find in your CRM or ERP. To truly understand the reasons for churn, you must widen your lens to incorporate all of the data from across the enterprise. Data from different departments — HR, Ops, Finance, Marketing, etc. — even public data, may hold the keys to your churn problem.
2. Your Analysis Might Be Incomplete
Typically, analytics or data science initiatives aimed at solving churn use descriptive or predictive analysis to identify which customer have churned in the past and what caused the churn in order to determine which customers are likely to churn in the future. Days, weeks, months go by and, despite your new and hard-won insight, the red numbers on your reporting show that churn hasn’t decreased. This is because knowing who will churn is really only half the battle…and it is the easy half.
The hard, and essential, other half of the problem is knowing what to do about it. You need prescriptive analytics — considered the Final Frontier of Analytics — to tell you what specific, personalized action to take to retain each individual high-risk customer. One customer may respond best to a 30% off promotion, another may be happy with a replacement product, etc.
How are you using data to solve customer churn?