The fallout from the financial crisis that began in 2007 continues to impact retail banks. As banks have lost and struggled to regain customer trust, they’ve also labored to retain their most profitable customers.
Indeed, one-third of the new banking products that global customers have purchased over the past year have come from banks other than customers’ primary banks, according to a customer loyalty study by Bain & Company.
To retain and attract profitable customers, many retail banks are focused on providing face-to-face branch services for high-value clients.
In order to free up the resources needed to provide more complex services for high-value customers, bank leaders are also trying to entice the least-profitable customers to increase their use of lower-cost self-service channels such as self-service kiosks.
There are a number of ways that retail bankers can use big data analytics to evaluate customer behaviors and preferences and identify ways to entice and guide their least profitable customers to increase their use of self-service systems.
A good starting point is by analyzing the use of self-service channels such as online banking, kiosks, mobile self-service, and other tools to determine current usage rates and to gather insights about the causes for abandon rates to make it easier for customers to use self-service tools.
From there, decision makers can drill down on the root causes of why customers desert transactions and use those insights to improve the customer experience.
Big data analytics can also help inform retail bank executives as to the types of incentives that will persuade target customers to adopt certain types of self-service tools.
More than half of retail banking customers would be willing to switch to low-cost digital channels if they were offered positive fee-based incentives – such as increasing interest rates on deposits by 0.25% or decreasing interest rates on current loans by the same amount, according to Gallup.
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