Customer Churn for Marketers
In the previous article, I outlined the common machine learning use cases that a marketer can consider for personalizing the experience to the consumer. In the first article, segmentation and targeting use case and it’s benefits was explained. In this article, I touch up on another common use case that that marketers can consider called customer churn.
Customer Churn
Big Idea: In the US eCommerce business, 40% of revenue comes from returning or repeat customers, who represent only 8% of all visitors[1]
Churn is defined differently for different industries. In the case of eCommerce business, Churn is defined as customers who have not purchased within a specific cutoff date. The choice of the cutoff date depends on each organization and can be decided based on past buying pattern (E.g. 180 days from last purchase date)
The key is to identify customers who are about to churn and give them enough incentives to come back. Along with segmentation and identifying the Life Time Value (LTV) of the customer, campaigns should be geared towards high valued customers who are about to churn rather than the customers with low LTV.
