Predicting customer churn is critical for industries like telecom, retail and banking. The ability to detect such customer move requires establishing relationship across multiple variables of growing data sets that might signal preferences and finally intent. This kind of predictive analytics application need to be conducted at regular intervals to ensure continued customer satisfaction and prevent revenue loss. Once organisations are aware of customers whom they might lose to competition, they can implement a targeted and cost-effective retention campaign in a timely fashion.

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Executes churn model on large, disparate and scattered data sets

Predicts with high accuracy customers who are going to leave.

Identifies and scores all possible paths leading to customer churn

Generates results faster; deploys model faster in production environment

Provides scale with growth in customer base; retains huge revenue source

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