Motivation for launching the project by the customer: the need to increase the involvement of bank clients in the use of internal products revealed limitations in the current solution: segmentation and profiling models were of low quality, the identified user segments were not interpretable, analysts could not use the modeling results.
Description of the initial situation:
Segmentation of the bank's client base assumes high conversion of internal product offerings to clients;
Segmentation requires deep understanding and analysis of bank customer behavior, such as user transaction data.
An increase in the quality of segmentation leads to an increase in response to the bank’s products offered and affects business performance: from customer loyalty to profit.
The segmentation models used were not interpretable and their quality was low;
For this reason, there were significant risks when using current models in product functionality.
Project goals: creating a model for profiling and segmenting the customer base by their transactional activity with a high level of quality and interpretation.
MIL Team solution: the use of the team’s existing solutions in the field of customer analytics and transactional data analysis made it possible to implement a procedure for training temporal models for segmenting the customer base.
To build the model we used:
Bank customer transactions;
Description of MCC codes and merchants;
Accompanying information about the user;
Results of sales of banking products.
Simulation results:
Behavior model of bank users;
Model for predicting the likelihood of additional purchase of a banking product;