HyperSense
Churn Prediction

Churn Prediction solution uses behavioral, lifestyle, and demographic variables, which helps the organization develop a comprehensive view of each customer to identify the churn propensity and take necessary actions for retention.

Challenges with traditional solutions

Lack of data harmonization to identify trends

High time-to-market, especially for new projects

Inability to predict churn cause and revenue loss

A rule-based approach to identifying NBO

challenges

Benefits of Churn Prediction

Improve CLV

Develop personalized strategies to predict and manage churn and make quick business decisions leveraging real-time data-driven insights.

Increase Customer Loyalty

Influence customer loyalty and increase retention by improving service quality, dynamically adjusting pricing, and delivering personalized offers.

Better Customer Experience

Deliver the best customer experience by accessing the complete customer history during customer service calls to identify and resolve issues quickly.

Your Questions Answered
We’ve put together some FAQ's to give you more information about what we offer.

It helps organizations develop a comprehensive view of each customer to identify the churn propensity and take necessary actions for retention. With real-time insights on factors that trigger churn, organizations can take corrective actions to retain customers, such as offering attractive packages, better pricing, and special discounts tailor-made to customers’ preferences. It uses customer’s historical, behavioural, lifestyle, and demographic data to analyze and predict the likelihood of customer churn.

Churn Prediction helps businesses gain a better understanding of future expected revenue. It identifies and improves areas where customer service is lacking and helps understand what preventive steps are necessary to minimize lost revenue

The essential factors that help predict customer churn are tariff plans, subscriber contract, duration (length) of the contract, number of services, income, revenue, features used, etc.

Various analytical techniques for Churn Prediction are Customer Segmentation, Segment movement analysis, Historical segment churn analysis, Churn prediction modelling, Logistic regression, Decision trees, and Segment churn attribution modelling.

Churn prediction is typically treated as a classification problem, classifying a customer as yes/no for churning. Logistic Regression is an easy starting point because it is easy to explain and implement.

Boost customer retention and engagement with Churn Prediction

Top