Success Story - en

Retail customer segmentation

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;
  • Segments of the customer base.

Customer: Finance, Banking
Technology stack: TopicNet, BigARTM, gensim, Python
NLP Research Engineering
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