Big Data, Culture and Community Banks

Who doesn’t love data? Banking has loved data long before we enshrined it as Big. Everybody knows data is powerful.

But so is culture. Whether it’s visible and explicit or just “how we do things around here,” culture dictates what data is discussed, trusted and valued – and whether data gets transformed into actions that drive revenue, or just sits in unconnected files. Even when it’s invisible, culture is powerful.

As community banks embrace Big Data, they are confronted by the need to reconcile their data aspirations and their culture. Otherwise, they face a couple of conundrums. The first is that they accidentally diminish the data advantage that they have always held over their big brethren, which is the intimacy of their customer relationships and the insights that flow from intimacy.

Data Tranches

Most customer data comes in one of three categories. There’s the transaction data, which is easy for you to collect from your customers’ banking activities (although not always that easy to link across product silos). There’s profile data, or the deeper customer insights collected in your interactions with them, including their needs, preferences and behaviors. And then there’s purchased data – those huge troves of external data that you couldn’t possibly compile on your own.

There’s no denying the rich potential of purchased data. It tells you what customers and non-customers in your market like to do and buy, how they like to vacation and shop, about their work, education, home values and so on. Moreover, purchased data lets you find patterns that you can then apply to other customers with similar profiles.

But there’s a stubborn fact about purchased data that inevitably limits its value: It’s not exclusive. If you could buy it, your competitors can as well. If you could afford a battery of data scientists like the big banks, then you could mine and massage it to your advantage. But for most community banks, purchased data is a playing field leveler, not a slam-dunk competitive advantage.

But you do have an exclusive on your profile data. Combined with your transaction data, this is what you have learned about your customers in deliberate ways and recorded for use in future interactions. It encapsulates that most valuable aspect of your customer – your relationship.

But what tends to happen to that carefully husbanded profile data? Gresham’s Law. Gresham was the British financier who noticed that when a government overvalues one type of money and undervalues another, the undervalued money disappears from circulation, and the country is flooded by the overvalued money, i.e., bad money drives out good.

When community banks buy data from today’s sophisticated providers, it arrives in impressive detail and slick formats: tidy rows and columns and categories, with customer segments clearly broken out. It’s easy to sort, easy to analyze and easy to draw conclusions from. It contrasts starkly with the bank’s own data, such as profiling forms filled out when customers signed up for a checking account, handwritten notes from loan officers’ visits to prospects and customers, plus more in the credit card system, some in the mortgage system and probably more over in the trust department but under a different name. In other words, compared to purchased data, the bank’s own data seems messy, spotty, incomplete, out of date, hard to find and occasionally inaccurate.

No surprise then, that culture takes over, and executive and marketing teams turn to the clean purchased data.  No longer do they talk about the value of profile data or how to use the front line to get better information. You wouldn’t say “bad” data has driven out the good, but certainly the less valuable data has driven out the more valuable. Instead of using the purchased data to strengthen their exclusive advantage, banks spend more to learn what everybody else knows. Their own hard-won deep insights about customers are set aside, never to be captured or analyzed as part of the bank’s institutional memory.

Vital Few

The Law of the Vital Few is the other conundrum. It says, “For many events, most of the effects come from a very few of the causes.” Some call it the “80/20 rule.” Applied to data, this posits that out of 100 data elements, the most insight will come from just 20. But Big Data has raised expectations about what can be known about any given customer. Now everybody wants a full file – all that rich data, much of which can be purchased. Increasingly, the idea takes hold that an incomplete file is an inaccurate file. “If we only knew….” becomes the refrain, rather than, “Here’s what I discern….”

Suppose, when they admit you to the emergency room, the hospital’s protocol calls for 30 questions to be asked. If they quickly learn your left arm hurts and you’re short of breath and clutching your chest, do they stubbornly take you through the other 27 questions, or do they urgently summon the cardio team?

Driving revenue likewise depends on a few indicators, not every tidbit of which can be gleaned from the data universe. Knowing which few indicators to access is the question that should dominate, not how to create a perfect/complete file.

A few practical steps can remedy these challenges for community banks:

  • Use your newfound data capabilities to cough up one more answer: What top five data elements do your most successful sales people use to sell mortgages? To sell small business deposit services? Loans? Remember, if five data elements answer the question, maybe 20 elements un-answer it, causing salespeople to target less-qualified prospects.
  • Know your culture as well as you know your database because what happens with the data will be dictated by the culture. Could this happen at your bank? Not long ago I was working with a bank that was successful in blending purchased data with profile data to come up with effective campaigns. I asked the top-selling branch manager, “If you were short on your goals and needed to reenergize your efforts, what data would you ask Marketing for?” He got up, closed his door, opened a lower desk drawer and pulled out his dog-eared Rolodex, saying: “Here are my notes on every customer I’ve ever called on, including what they want to do next. This is where I go when I need revenue.”
  • Demonstrate that this is a strategic matter worthy of regular, scheduled executive attention. Include an executive-level (even board-level) review of customer profile forms (beyond regulatory requirements) in your annual planning process. See if the bank has clearly identified the “vital few” and made sure they are eased into the culture with new forms, training and communication about capturing and codifying them.
  • Routinely and visibly measure the extent to which that customer information is being recorded, just as though it were your greatest competitive advantage – because it probably is.

Mr. Von Seggern is president of ­­­­­­­­­­­­­­­­­­­­Dallas-based kvCRM, which helps midsized and community banks succeed at CRM Culture Change. He can be reached at [email protected].