Rising regulatory pressures. A sluggish economy. Heightened competition from new market entrants. These are just a few of the challenges companies in the financial services industry are facing today.

In response to these pressures, there are tremendous opportunities for banks, brokerages, and other players in financial services to use customer data and analytics to gain deep insights about customers’ needs, preferences, credit worthiness, product ownership, risk appetite, investing habits, lifecycle status, and other characteristics that they can then use to provide customers with targeted product offers and personalized experiences.

Financial services companies that use predictive analytics outpace non-adopters in several key areas of business performance, according to the Aberdeen Group report, “Predictive Analytics In Financial Services: See The Future, Make It Brighter.”

For instance, predictive analytics practitioners saw a 10 percent increase in new customer opportunities identified, an 11 percent gain in customers and an 8 percent lift in cross-sell/upsell revenue compared to increases of just 7 percent, 8 percent, and 3 percent for non-users, respectively.

The surge in customers demonstrates that financial services companies using predictive analytics not only convert a higher percentage of prospects, they also retain a higher number of existing customers.

A big part of the success achieved by financial services companies that use predictive analytics is the ability to better anticipate the needs of their customers.

For instance, a male customer in his 50s applies for a new credit card with his primary bank. An analysis of the customer’s transaction history and product ownership by a regional manager for the bank reveals that the customer is also a prime candidate for retirement investment vehicles such as a Roth IRA.

As part of the analysis – which could involve using data outside the bank’s system, e.g., social media – the manager discovers that the customer doesn’t own any of the bank’s investment products and his youngest child is close to finishing college, which should free up the customer’s disposable income.

In discussing the customer’s credit card application and terms, the regional manager can use these insights to suggest retirement products that the client is most likely to respond to based on a quantitative analysis of previous prospects with similar characteristics who converted.

Next Steps:

  • Download the Aberdeen report: “Predictive Analytics In Financial Services.”
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