HDFC Bank

Customer Profiles AND Customer Segmentation

Identifying actual customer potential set against customer value exploited by the bank

HDFC Bank Limited faced challenges in accurately profiling its customer base due to limited income proof and transaction data, hindering effective customer acquisition and service strategies.

KENTRIX provided a solution by geo-coding residential addresses and segmenting customers based on lifestyle affinities, income, and product purchase behavior. This profiling identified approximately 30% of ‘undersold’ customers with higher potential for engagement.

Within three months, HDFC Bank achieved a remarkable 24.8% conversion rate by re-targeting these identified customers, showcasing the effectiveness of KENTRIX’s data-driven approach in enhancing customer value and driving growth for the bank.

The Challenge

Before working with KENTRIX, HDFC Bank classified its customer base mainly by the business its clients did with the bank. Thus, HDFC Bank could not securely identify customer potentials where income proof was not available to the bank. Further, insufficient transaction data from individual clients did not yield enough data for profiling and customer segmentation.

HDFC Bank is required for the next level of customer acquisition and customer service with reliable profiles of their individual customers. As per RBI (Reserve Bank of India), customer names and contact details cannot be shared outside the bank.

The KENTRIX Solution

KENTRIX profiled the total HDFC Bank customer base by geo-coding the residential addresses. The client IDs were then allocated to the KENTRIX micro-market cells. From the KENTRIX profiling data HDFC Bank acquired the consumer lifestyle affinity segmentation data, income, and a range of product purchase affinities.

Based on the customer profiling delivered by KENTRIX, the bank identified approx. 30% “undersold“ customers, i.e., clients having a significantly higher potential to bank with HDFC valued against their current customer value to the bank. Within three months of the project’s start in 2017, the bank realized an average 24.8% conversion rate on re-targeting these identified customers, a significantly higher conversion rate than it used to be before.

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