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TECHNOLOGY

Tackling Customer Churn With Machine Learning and Predictive Analytics


The Challenge

A software company struggled to retain customers through subscription renewals because it could not predict possible customer churn in time. Sales managers and sales executives lacked a central repository of information on past transactions, accounts’ key decision-makers and product-related details and other issues that were necessary for sales expansion.

The company reached out to Cognizant, a long-standing partner, for help. It had decided to move all its transactional systems to the cloud and needed to enhance its predictive analysis and churn probability capabilities.


Our Approach

Using an agile delivery approach, Cognizant incorporated machine learning (ML) into the company’s analytics model to elevate its 360-degree view of customers. By applying ML, the client can now proactively take steps to retain customers who are about to discontinue their service and are unlikely to renew their contacts. It can also prioritize high-value customers.

Cognizant leveraged Cloudera to host transactional data and Hive to transform the data. By automating the extract, transform and load functions to Cloudera, the client collated data from multiple sources into a central location for easy access and analysis.

Preprocessed, customer-centric data provides a 360-degree view of each customer, and that data is fed into the QlikView dashboard. The Apache Spark in-memory processing engine determines customer churn probability and identifies potential cross-sell/upsell opportunities. The flexible solution can be used for approximately 20 product types and 150,000 customers.

Adopting ML to Predict Customer Churn

Cognizant's dedicated software company team partnered with the client to design and implement a solution that provides 360-degree views of its customers. The solution’s flexible dashboard provides customer performance details at all levels. The dashboard also provides details on churn probability, propensity to buy scores and customers’ lifetime value. With this information, sales managers are now able to provide discount offers and premium support to minimize customer churn.

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80% accuracy

in customer churn algorithm

75% to 80% reduction

in manual efforts

$12 million

in possible cross-sell and upsell opportunities


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