Utilizing Machine Learning Algorithms to Decrease Churn and Engage Merchants on This Payment Platform Prescience Decision Solutions November 24, 2022

Utilizing Machine Learning Algorithms to Decrease Churn and Engage Merchants on This Payment Platform

The company has undergone rapid growth in recent years and is transforming raw data into meaningful insights to deliver superior business outcomes.


The client is India’s only payments company with multichannel transaction processing capabilities through web, mobile, in-store or at the time of delivery. The company has a payments platform solution to manage payments automation, consumer credit distribution and SME lending, which has become a benchmark in their markets. They also have an expanding portfolio of merchants that they work with, extensively.

The company sought to implement a solution that would help them derive in-depth insights into the behavior of merchants and determine the various reasons for the churn. In addition, the company also wanted to formulate a plan to proactively act on customers who were likely to get churned.


It is a known fact that retaining existing customers is a more cost-effective proposition than acquiring new customers. Hence, Prescience charted out the following methodology to enable the client to curb its extensive customer churn-out rate.

Once the methodology had been formulated, Prescience adopted a two-pronged solution using machine learning (ML). The team leveraged ML algorithms by using key factors such as number of transaction days, average sales, transaction time due to bank hops, varying sales figures, sales trends, support service levels and years of business association to identify the cause of churning. The results derived from this exercise were consumed by business and client teams who worked with stores and merchants to address challenges beyond the threshold.
The team also tapped the ML algorithms to predict the days it would take for a customer to churn. The customers who were going to churn in next seven days were targeted and their problems were addressed in-person by the sales team.

By implementing such a robust and advanced solution, the client is now able to take timely steps to address the issue of churn. Engagement with the merchants and stores have now became more meaningful with conversations being done around the specific factors identified for each merchant. In addition, the implementation of the model has also resulted in the reduction of churn through proactive measures, enabling the company to channel all its time, effort and resources to drive sales and business growth.

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