Customer Segmentation With
Personalized Recommendations
On Churn Prevention

Challenges and Industry

A national pharmacy chain reached out to GFAIVE with the goal of decreasing customer churn and increasing the effectiveness of marketing campaigns. Since 2013 the pharmacy had a loyalty program with roughly 1.2 million transactions per month. Earlier, the client tried to segment these customers to identify those at risk of churn but was unsuccessful as his method was based solely on how recent the latest purchase occurred.

The goal for GFAIVE was to build a tool that would leverage the clients’ existing customer data to make highly accurate predictions about customer behavior, specifically customer churn. Simultaneously, the tool had to provide an opportunity to segment the customer base by likelihood of responding to each specific marketing campaign.



First, GFAIVE team took a random dataset of roughly 500'000 customers from the loyalty program and with the help of machine learning algorithms was able to perform customer segmentation.

The solution was then further customized to provide recommendations for each customer segment (optimal advertising channel, tips for churn prevention, recommended frequency of marketing emails).

By leveraging machine learning algorithms GFAIVE’s customer intelligence tool was able to identify customers at risk of churn and send notifications of recommended actions in real-time, without the help of an analyst.



After integrating GFAIVE’s customer intelligence tool, the pharmacy chain was able to further personalize its loyalty program based on the segmentation that was provided. Moreover, the insights and recommendation on customer behavior helped reduce customer churn by 7%.

The pharmacy also leveraged the technology to plan marketing campaigns for each customer segment, leading to a 21% marketing ROI increase. This new approach to customer management and personalization helped our client obtain a spot on a list of “Most customer centric pharmacies in the region”.

Technologies Used

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