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How a Malaysian bank used data science to increase its credit card spend

Client Profile: The bank is one of the largest financial institutions in Malaysia with retail branches in numerous Southeast Asia countries.

Challenge: A substantial segment of the bank’s credit card holders with high spending potential is not utilizing its credit card as their primary card.

Solution: Cardholders with spending potential were segmented based on various demographic attributes such as gender, product holdings and amount spend.

Collaborative filtering – a technique to predict user interest based on the behavior of other users – was applied to detect spending patterns. For example, the bank wanted to know how frequently merchant offerings appeared against customers with specific product holdings. And how often merchant offerings showed up against customers of a particular demographic.

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Customer segmentation and collaborative filtering to forecast consumer propensity scores

Each potential customer was scored on their spending propensity for every merchant category like dining, petrol, groceries and airlines.

The bank then matched specific merchant offers to individual customers based on their inclination to spend in that merchant group.

Customer data, card transactions and the effectiveness of previous merchant offers were used to identify potential cardholders to drive higher usage of the bank’s credit cards.

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Every customer is scored against every merchant category on their propensity to spend

Business Benefits: With personalized marketing campaigns derived from data analytics, the bank saw an increase in its credit card spend and utilization, resulting in a substantial increase in interchange and interest income.

Industry: Banking

Big Data Solutions Provider: Teradata Malaysia
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