Subscribe to DSC Newsletter

Personal Banking and the Data-Driven Approach

AmericanBanker.com reported recently on a phenomenon they characterized as “The Downside of the Data Driven Decision.” The LA Times first uncovered Wells Fargo’s quota of eight consumer products per account. The story described questionable tactics used by bank employees to meet that goal. American Banker quickly picked up on the story, characterizing the policy as “data-driven.”

Compelled by the bank’s aggressive consumer sales policy, LA Times alleges that bank employees were pushing individuals to buy more products without regard to customer benefit. These quotas have allegedly led some employees to mislead customers into additional products, and even forging signatures and signing customers up for credit cards. What’s most notable in the coverage of these stories is how little thought is given to the costs of these blanket selling strategies. While the public relations problems and potential legal actions the bank will deal with are an obvious result of the short-sighted strategy, the reality is that the method is far from the most profitable approach to personal banking.

It should be instructive that the largest bank in America (Wall Street Journal, 2013) has yet to implement an analytics solution that will present better, more mutually beneficial product offerings to their customers. While we have only scratched the surface of banking data, the pioneers of “big” financial data have already shown that there is much to be learned from digging deeper. Instead of attempting to maximize the profit of the average customer, modern data technologies could allow for a much more dynamic, more flexible approach. In the implementation of this policy, Wells Fargo has tipped their hand regarding just how little they have been able to make of the data they store.

The promise of Big Data is not just more profitable banking, but banking that both benefits bankers and delivers the best products to customers. A better strategy begins with an in-depth analysis to reveal levels of microsegmentation, identifying the most profitable customers by the combinations of products and how they use them. The result is a better understanding of the banking relationship. Banks learn what makes a better customer, and customers are directed towards the products that will make them happiest.

See where you stand with Big Data – Take the State of Analytics in Finance Survey now.

While better segmentation will be a great leap forward from selling as many products as possible to each account, advanced data techniques hold much more promise. The next step in consumer banking will be a customer-centric approach. Banks will not only need advanced microsegmentation analytics, but automated systems that identify the attributes of a customer’s account, compare them with similar but more profitable customers, and automatically suggest additional products or services.

Real data-driven banking will use advanced analytics solutions to match customer’s with a “best-fit” selection of products and services, rather than once size (in this case, eight) fits all approach. Deep and real-time analysis of existing customer data will create a better view of the factors that drive long-term profitability and better bank/customer relationships. Customers who will see real benefit from a CD will be offered a selection of CDs, while customers more likely to be buying a home will be directed to home loan information. Regardless of if these offers are delivered electronically or through the Customer Service Rep, banks will be cultivate a better relationship with their customers and those customers will remain loyal, delivering long-term value back to the bank.

The future of banking will result in happier and more profitable customers, but banks won’t get there without first putting their data to work.

See where you stand with Big Data – Take the State of Analytics in Finance Survey now.

Views: 595

Comment

You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge

On Data Science Central

© 2019   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service