Advantage
Significant increase in response rate
Data
Historical data from the customer journey and demographic information
Methods
Plausibility analysis and bootstrapping
Challenge
VR-Bank Mitte eG aims to optimize its sales processes in the area of customer contact and support. Specifically, the aim 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 conversations or demographic information. Numerous features are prepared or generated for subsequent modeling. To determine the score, an ensemble of 1,000 classification trees with previous bootstrapping is formed. A visual plausibility analysis was used to clearly validate the algorithm's predictions.
Solution
A significant increase in the response rate was observed after just a short campaign period. 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 earnings potential, reduce costs and increase customer satisfaction.
Result
A significant increase in the response rate was observed after just a short campaign period. 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 earnings potential, reduce costs and increase customer satisfaction.
Get started now:
We look forward to exchanging ideas with you.

Your expert on Data-Science-Projects:
Lutz Mastmayer
projects@eoda.de
Tel. +49 561 87948-370