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How Banks Use Predictive Analytics to Remain Competitive

Nov 22, 2017

Sustaining a business in the service industry has always been challenging. The customers are more demanding, forcing companies to fight hard to sort out operations, improve products and services, and maintain profitability. Such a phenomenon holds true for the banking sector as well. In a quest to provide minimal interest rates and extended services, banks are fighting to remain profitable. To solve such issues, the banking and financial industry is turning towards predictive analytics to predict consumer behavior and maximize revenues from each customer. Analyzing factors such as customer loyalty, spending patterns, purchase frequency, and other buying behavior helps banks and financial institutions adjust their services and promotions to build their revenue base.

Cross-Selling and Up-Selling Opportunities

The competition in the credit card business has increased at such a phenomenal rate that banks have started providing credit at 0% interest rate, extended credit period, and offer higher bonus points on purchases made through cards. Amongst all these services, one would be perplexed as to how banks remain profitable. Well, that is because they use customer data to cross-sell and upsell their other products like housing loans, auto loans, locker services, or a platinum credit card. Analyzing behavioral data of the consumers can paint a picture for the banks as to whom should they offer a specific product to and at what rate. This, in turn, increases the bank’s share in the customer’s wallets and builds brand loyalty.

Speak with our analytics experts to know more about how banks and financial institutions use customer data to carry out predictive analytics for cross-selling, reducing attrition rate, and enhancing customer relationships.

Customer Retention

A generally agreed upon adage in the service industry states that acquiring new customers is ten times more expensive than retaining existing ones. As a result, banks are focusing their energy on retaining their customer base and lowering the attrition rate. Since banks deal with thousands of customers on a daily basis, it is almost impossible to identify dissatisfied customers. Additionally, they would not know if the customer they are about to lose is profitable or not. Adopting predictive analytics by analyzing customer’s historical data, spending patterns, and other behavioral data can help identify customers who are likely to churn. Predictive models can accurately identify such sets of customers, and automated systems can be built to send out lucrative promotions to retain such customers.

Enhanced Customer Screening

Banks and financial institutions have embraced advanced analytics solutions, which help them assess customers on various parameters such as creditworthiness and credit score. Banks can now generate every single detail about the customer including spending patterns, monthly billing, and spends across different shops. This way predictive models can be built to trace their spending patterns. Such screening can be helpful in multiple ways. For instance, if their card is stolen and misused by others to make a significant purchase, banks can verify the purchase by calling the customer. Additionally, predictive analytics can also help them identify a customer who might default from their payments so that timely measures could be put in place to increase collection.

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