Repurchase prediction
To increase customer loyalty, the re-motorisation time is predicted using vehicle data.
The prototype combines the quality of heterogeneous demand signals from different markets
The prototype combines the quality of heterogeneous demand signals from different markets
Challenge
A premium car manufacturer wants to increase its repurchase rate (especially in the leasing sector).
The expected Re-motorisation date is entered manually by the dealerships in the CRM system. The entry is partly incomplete and incorrect.
As a result, customers are approached at the wrong time and marketing campaigns remain unsuccessful.
Solution
On the basis of diagnostic and vehicle data, we create a Forecast modelwhich significantly improves the accuracy of the repurchase prediction.
The comparison of warranty, diagnostic and CRM data shows patterns for the identification of implausible dealer entries.
Result
The car manufacturer can correct approx. 25% implausible entries and address these customers at the right time with the right product portfolio. Unreliable dealers can be identified and their processes can be improved by means of Best practice methods of the top dealers can be significantly improved.
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