UK Financial Institution Improves Campaign Conversions by 28% with Coforge’s AI-Driven Segmentation and NBO Engine
Overview
A top UK-based financial institution serving over 14 million customers turned to Coforge to transform its customer segmentation and product recommendation strategy. The bank struggled with siloed data, static segmentation models, and manual campaign execution—limiting its ability to deliver relevant offerings and drive conversion.
Coforge implemented a machine learning-powered segmentation and Next Best Offer (NBO) engine that unified internal and external data to create dynamic customer cohorts and generate personalized product recommendations. The solution enabled data-driven targeting and automated campaign execution, driving higher conversion and lowering acquisition costs across commercial and corporate banking segments.
A top UK-based financial institution, with over 14 million customers and extensive commercial and corporate banking operations, faced key challenges in effectively segmenting customers and recommending relevant products.
Key issues included:
Inability to segment business customers dynamically based on real-time data
Siloed financial and demographic information from multiple internal and external sources
Limited capability to recommend personalized offerings aligned to customer profiles
Manual campaign execution leading to low conversion and high acquisition costs
Solution
Coforge developed a data-driven segmentation and Next Best Offer (NBO) engine, powered by machine learning and collaborative filtering.
The solution included:
K-Means clustering on merged internal customer data and external bureau data
Creation of customer cohorts using financial behaviour, product holdings, credit activity, and engagement signals
Identification of product gaps for each segment using collaborative filters
Development of an AI-based recommendation engine for next best products
Seamless integration into marketing systems (e.g., Salesforce) for campaign execution
A/B testing framework and KPI feedback loop to refine recommendation accuracy
Key Highlights
Created granular customer cohorts
Enabled precise product mapping and tailored offer creation
Supported marketing teams with cohort-specific targeting logic
Built a scalable framework for future multi-product cross-sell and upsell models
The Impact
28% Improvement in campaign conversion rates
25% Reduction in cost per acquisition (CPA) via targeted engagement
