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.

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Challenges with traditional solutions

Lack of data

Lack of data harmonization to identify trends

High time-to-market

High time-to-market, especially for new projects

Revenue-loss

Inability to predict churn cause and revenue loss

Manual and rule-based

A rule-based approach to identifying NBO

challenges

Features of Churn Prediction

Probability Survival Model

Probability Survival Model

Analyze customer demographics, policy details, history, spending, and LTV to predict the likelihood of churn and earn customer loyalty.
Hyper-Personalization

Hyper-Personalization

Create tailor-made offers to manage churn based on consumers’ preferences and behavioral patterns and drive loyalty.
The Next-Best Action

The Next-Best Action

Get to know your customer better and drive successful marketing efforts towards increasing Average Revenue Per Unit (ARPU).
End-to-end Integration

End-to-end Integration

Generate the right set of churn predictions using various variables which can help organizations assess the churn propensity of each subscriber.
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Benefits of Churn Prediction

Drive lasting Customer Lifetime Value
Improve CLV

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

Increase Customer Loyalty
Increase Customer Loyalty

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

Better Customer
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

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