Advanced analytics slashes churn by over 90%

Q. How did this trade-led retailer redefine the way they targeted emails and slash email churn by 90% as a result?

A. By using Planning-inc’s Category Affinity Model to predict the content that will appeal to their customers most.


The client’s CRM team send out a huge number of campaigns each and every week, driving massive revenue. They of course knew that the more relevant their targeting was, the better the responses. But a new focus on maintaining the size of the marketing consenting base meant relevancy was more important than ever. The wider business meanwhile wanted volume for key campaigns, believing the more emails you send, the more revenue you’ll make. How do you find the balance?


We implemented the our Category Affinity Model - a machine-learning algorithm that automates the analysis of vast volumes of web browse, on- and offline transactional and email engagement data. 

The model was then able to accurately predict each customer’s likelihood to buy from both a category they’d previously bought in as well as how likely they were to buy in a new category altogether.


The Category Affinity Model has helped this retailer totally rethink its email program. The CRM team can now forecast volumes and revenue to manage trading teams’ requests, reducing email volume by up to 50% while still hitting category sales targets. The retailer has also reduced email churn by up to 90% by using the model to directly target specific customers and improve relevancy.