Calculation of churn probabilities

Predictive analytics can identify customers who are ready to switch at an early stage and implement suitable measures to retain profitable customers.

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360° customer view through the first-time integration of vehicle and customer data

 

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Identification of the 5 central churn drivers from over 50 hypotheses

Development of a new cluster approach for social media analytics

Challenge

In most industries, the principle is that it is cheaper to keep a customer than to win a new one. This is especially true for long-term, expensive products such as cars. Therefore, customers with switching intentions should be identified.

Solution

A generalised linear model (GLM) is used to determine the churn probabilities. To identify the drivers, various customer, vehicle and social media data are combined to form a holistic customer history.

Result

With big data analytics, exactly those customers with the highest risk of switching can now be prioritised and measures taken to retain them. Resource allocation in the company becomes more efficient, which in this case not only saves money but also leads to increased loyalty. The method has a 90% hit rate.

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