Low interest rates, higher capital requirements, and moribund customer spending continue to put the squeeze on bank profitability.
One effective way for bankers to respond to these challenges and improve profitability is by using customer data and predictive analytics to identify customers and prospects who are most likely to generate profitable relationships for the bank.
A study by Aberdeen Group demonstrates that banks and other financial services companies that use predictive models have increased their new customer prospects by 10 percent and realized a 167 percent gain in cross-sell and upsell revenue compared to companies without predictive analytics.
One way to boost margins is by identifying opportunities to sell products to customer segments that offer the highest profitability potential.
Relationship managers and loan officers can analyze customer data – including data regarding the products owned by current customers, demographic data such as household income, and other data types – to help determine those products that targeted customers are most likely to buy as well as the profit potential they carry.
From there, bank managers can prioritize which customers to target and extend offers to those customers that are most likely to convert based on comparable offers made to customers with similar characteristics (income level, lifecycle status, number of products owned).
Marketers can also use predictive analytics to identify the best prospects to extend offers to. Roughly one-third of the retail banking products purchased by customers are from a bank other than the consumer’s primary bank, according to Bain & Company.
Bank executives can also use predictive analytics to determine the common characteristics between leading prospects and new customers that have purchased specific products (e.g., home mortgages, IRAs). That information can be used to identify the best offers to extend to potential candidates along with the messaging and channels to be used that are most likely to drive conversion and boost profitability.
Predictive analytics can also be used by bank executives to determine the optimal price level to set for certain products with individual customers. An analysis of the factors impacting bank profitability by PricewaterhouseCoopers finds that a 1 percent change in the price of a product or service can increase profit margins by up to four times more than a similar increase in sales volume.
Predictive analytics can be applied to customer data to determine the price points that previous customers have been willing to accept as well as the product pricing that target customers and prospects are most likely to accept.
