Customer Loyalty Analytics in Retail Banking
Synopsis
We aimed to empower a leading regional bank with critical analytics and insights into customer loyalty, allowing them to understand customer behaviour more comprehensively and create targeted strategies for fostering lasting relationships. Given increasing competition, understanding customer loyalty was a pivotal strategic lever for our client. Thus our approach emphasized delivering actionable insights the bank could use to improve customer retention and engagement.
Our focus involved extensive data acquisition, cleansing, and transformation to derive key insights into customer profiles, lifetime value, consumption habits, and the evolution of their account holdings with the bank. The goal was to build personalised loyalty offers that resonated more deeply with their customer base.
Client Overview
🔸Medium sized retail bank in EMEA with +$45BN in assets under managementÂ
🔸Credit card usage down an average of 7% YOY for previous 2 years
🔸Inadequate customer segmentation
🔸Poor understanding of customer spend and card usage habitsÂ
Client Problem
“Faced with legacy challenges, including lackluster credit card loyalty, inadequate customer segmentation, and limited insight into customer spending, our company sought to uncover client usage patterns. By categorising clients based on their card habits, we aimed to create tailored loyalty offers that would drive customer engagement and foster long-term loyalty“.
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Analytics Delivery
4 weeks pre-engagement POC to gather and assess all critical data sets at the customer account level, on financial retail products, last 3 years of loyalty campaign offers and performance.
Our objective was to then build a modelling methdology that would provide answers to the following:
🔸What products and consumption habits does each customer have
🔸Which programs are best suited to their consumption habits
🔸Offers that would incentivise credit card usage from inactive and partially active customers
The Challenge
Our client had not previously implemented a comprehensive strategy for customer segmentation, resulting in imprecise customer groups and generic offers distributed across the entire customer base.
⦿ This approach led to limited customer engagement, and credit card usage decreased by over 15% in the last two years.
⦿ A recent decision to increase transaction fees in response to declining card usage may have worsened the situation without yielding improvements in revenue recovery.
⦿ To gain a better understanding of the situation, the client conducted customer forums and engagement initiatives, gathering preliminary insights into the decrease in card usage.
⦿ The top three reasons identified for reduced card usage and inactivity were:
   –  Lack of Card Usage Incentives
   –  Customer Awareness
   –  Insufficient Personalisation
Trusted Data Approach
Identifying subtle insights into purchasing habits would reveal lifestyle segments such as individuals that were fashion conscious or travel oriented. This would enable marketing teams to design tailored customer loyalty programmes with vertical partners, such as family orientated clients receiving rebates at supermarkets and gas stations.
⦿ Integration of internal data sources to augment existing customer profiles and offer more precise customer groups for offer personalisation.
⦿ In designing effective loyalty programmes, the bank needed to understand those features and services their clients considered most important.
⦿ Advanced clustering algorithms helped to solve this problem by uncovering meaningful patterns within the client’s profile and transactional data.
⦿ ML models were run to validate the robustness of these clusters, ensuring they stood up to scrutiny before customer segmentation and profiling.
⦿ These clusters then formed the segments of the loyalty based programmes to maximise their effectiveness.
OUR IMPACT
$MN incremental card spend
%
Retention Increase
%