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Enhancing Customer Engagement: How Coforge Empowered a Leading UK Bank with Data-Driven Customer Segmentation & Personalized Offers .

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Overview.

A major UK bank aimed to enhance customer intimacy and drive business growth through tailored product offerings. With a diverse portfolio of financial services and banking products, the bank sought to segment its B2B clients based on their data, income, and overall Lifetime Value (LTV). The goal was to guide the marketing team in identifying the most appropriate products for each customer segment, fostering deeper engagement and maximizing cross-selling opportunities.

Challenges.

  • Enhancing customer intimacy and driving business growth through tailored product offerings. 

  • Segmenting B2B clients based on data, income, and overall Lifetime Value (LTV). 

  • Guiding the marketing team in identifying the most appropriate products for each customer segment. 

Solution.

Coforge implemented a data-driven approach to segment the bank’s commercial and corporate customers and determine the next best offer: 

  • Utilized K-Means clustering algorithm to segment customers based on financial and demographic features. 
  • Merged internal data with external datasets from agencies regarding registrations and credit portfolios. 
  • Divided features into buckets and created cohorts based on clustering output patterns. 
  • Mapped existing products to each cohort and used collaborative filters to identify common products among customers. 
  • Developed a multi-class recommender algorithm to generate scalable next best offer models. 
  • Conducted AB tests to evaluate the performance of the machine learning models and analysed feedback to measure KPI changes. 

The Impact.

75%

Increase in Product Cross-Sell to Customers

15%

Uplift of Customers to New Categories

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