Advantage

Significant increase in response rate

Data

Historical conversions or demographic information

Method

Plausibility analysis and bootstrapping


Customer analysis for VR Bank Mitte eG

Challenge

VR-Bank Mitte eG is striving to optimize its sales processes in the area of customer approach and support. Specifically, the goal is to assess and evaluate customers’ latent interest in a particular product in advance of a sales campaign.

Goal

Machine learning algorithms are used to calculate the affinity of a customer for the product to be marketed. A high affinity promises an increase in the response rate and a more efficient use of resources in marketing and sales.

Solution

To achieve a reliable score, eoda brings together 20 different data sources – historical conversions or demographic information. Numerous features are prepared or generated for later modeling. To determine the score, an ensemble of 1,000 classification trees with previous bootstrapping is formed. With a visual plausibility analysis, the predictions of the algorithm could be visually validated.

datenvisualisierung_Plausibilitätsanalyse

Result

After only a short campaign period, a significant increase in the response rate could be observed. Thanks to eoda’s customer affinity analysis, VR-Bank is able to talk to the right customers about the right topics even more frequently. Targeted and effective sales activities increase revenue potential, reduce costs and increase customer satisfaction.

Logo VR Bank Mitte


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