Like it or not, banking traditionally operates within a copycat climate. Although banks struggle to differentiate, consumers still view banks and their products as commodities.

Introducing “theoretical banking,” based on the idea that by collecting and using new kinds of data, you can better calculate who your customers and prospects are, what they are doing now, and what they want in the future. Through theoretical banking, banks can provide true differentiation.

Following are some of the topics our marketing gurus are discussing right now as they seek to put theoretical banking into practice.

Watch how consumers behave

Shopping for a new bank. Opening an account. Anchoring and onboarding. Considering an additional product. Consumers behave and think differently at each stage of the banking lifecycle. 

Theoretically, you would take the time to understand the behaviors that are driving each stage. Using observed behavior, you would customize data and analytics for each stage of the consumer lifecycle. That way, you could choose channels, messaging and offers that reach the right consumers at the right time.

In reality, most banks are using their targeting data throughout the lifecycle of the accountholder. Therefore, they are missing opportunities by not hitting the behavioral triggers evident at various stages.

Banks that learn how to collect and apply behavioral data will be the innovators that transform the industry. For example, they will be the ones who can institute the idea of “lead scoring,” using behavioral data to assess potential needs at every stage of the lifecycle.

Theoretically, the CRM would expand exponentially beyond the traditional demographic and psychographic inputs. For example, new inputs should include credit bureau data that indicate where assets are located, as well as data about liabilities and how and when they are paid.  It would also include behavioral patterns related to buying, social media, mobile and web use, channel preferences, and personal geographic footprint. When analytics include behavioral inputs, you get a better picture of whom the customer is and what they are trying to accomplish.

Bank on the Bell Curve

When you graph the age of the U.S. population, you get a bell curve. Coincidentally, when banks graph the age of their accountholders, they also get a bell curve.

Theoretically, it would make sense to continually target and fill your acquisition pipeline with accountholders who look like the ones you already have, which represent age groups all along the bell curve. Each of these groups have the potential to bring you profit once you acquire them.

In reality, we see banks spending too many resources on Generation Y, their smallest age group at the tail of their bell curve. This has created a dynamic where banks are all competing for the same people, who, in fact, may not be very profitable since this group is comfortable having and managing multiple, mainly digital, banking relationships.

Location, location, location.

The probability of banking with a certain institution has a lot to do with where the consumer lives.

Theoretically, it would make sense to focus your marketing efforts on people within a scientifically derived range of your geographic footprint. There are a number of excellent methodologies available to identify this range and avoid marketing to people with a low or no propensity to respond.

In reality, we see banks going so far beyond their footprint that they have reached the point of diminishing returns. This negatively affects their response rates and thus their marketing ROI.

Since banking is a local behavior, banks would be wise to be early adopters of location-based marketing technologies. One of the promising benefits is that it will allow banks to interact with consumers when they are about to make major purchases. Wouldn’t it be nice to ping a person’s smartphone with an auto-loan offer when you know they are wandering around a car dealership?

Get more quantitative

Data collection and technologies are changing rapidly, and the strategies and approaches that worked last year do not work as well today.

Theoretically, banks would be making a conscious effort to become more quantitative in their marketing. Product development, pricing, delivery and promotional activities would be influenced by new, more robust data and analytics.  

In reality, this is starting to occur. Banks are hiring more “quant-minded” professionals. And although they sometimes get the reputation for being “data geeks”, they hold the key to helping us decode the sales potential in human behavior.

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