A Snarketing post by Ron Shevlin
A Snarketing post by Ron Shevlin, Director of Research at Cornerstone Advisors
During a presentation at strategic planning session for a large credit union, I challenged the management team’s notion that the financial institution was “competing on superior service.” My argument went as follows:
- The “superior service” market isn’t big enough. Only so many consumers choose a financial provider for its “superior service capabilities.” Many consumers choose a financial institution based on convenience — not service-related factors. Even there, “convenience” is not a singular or narrowly-defined concept. Does it mean nearby branches, or extended hours, or self-service technology-based tools that make managing finances easier?
- The “superior service” strategy isn’t measurable. Managers need to be able to gauge two things: 1) To what extent is their chosen strategy a smart strategy, and 2) How well are they executing on their chosen strategy. There’s no shortage of financial institutions (especially credit unions) who claim to provide superior customer care — with no ability to measure or prove that claim. Without adequate measurement, focus/alignment/discipline becomes impossible to achieve.
- The”superior service” strategy isn’t specific enough. The lack of specificity means that achieving any kind of focus, alignment or discipline will be extremely difficult. A financial institution that chooses to compete on “superior service” will still need to determine how it will deliver across other critical parameters — e.g., convenience, value and product quality. This isn’t impossible, but in practice, trying to build a brand position around “superior service” can lead managers to neglect other dimensions like value and product-quality. It’s vague.
( Read More: Your Service Is Not What Differentiates You )
One of the executive team members then looked at me and said, “You don’t get it. We do have superior service. It comes down to knowing our members better than any mega-bank could ever know them.”
I stared back at him as I blurted out, “Dude, you don’t know Jack!” (Okay, I didn’t really say it. But I was definitely thinking it in my head.)
I did say — out loud — that the credit union only really knows sliver of who its members are. Sure, when a member walks into a branch, credit union employees many know that member is Jack Jones — wife’s name is Jill, and that they have two wonderful kids, Jenny and Jimmy who attend Jefferson College. I explained, however, that the credit union doesn’t know Jack:
- How much money Jack has. You only know how much he has with you. And if he’s got any money, it’s a good bet he doesn’t have much of it, let alone all of it, with you. So you really don’t know his investment needs or risk tolerance.
- What Jack’s financial goals are. Oh sure, you have a PFM app that has a goal tracking capability. But PFM users account for what… 10% of your overall member base? What’s the chance that Jack is one of those members and uses the goal tracking feature?
- How Jack makes his money. Oh sure, you might know how much he makes because you can see that direct deposit coming in every month, but you don’t know if that stream of income is safe and stable, or if Jack works in an industry that is on the decline.
- How Jack (and his family, for that matter) spends his money. You’ve got a piddly percentage of your member base using your online bill pay platform, and it’s a good bet you didn’t issue all the credit cards he has, so you really don’t know where the money is going. And worse, you’re not even doing anything to analyze the debit card spend data you do have.
“You don’t know your members nearly as well as you think you do,” I told the credit union’s leadership with conviction. “And as I look around the industry, it’s megabanks and fintech startups — not community banks and credit unions — that are actually doing anything about it.”
Big Data Delusions Grandeur
In a CU Times article on marketing trends, a consultant was quoted as saying:
“Where big data holds out great promise for credit union marketing, i.e., the ability to enrich target marketing, forecast next best products for members, and generate more efficient marketing budgets; it also yields great strategic value. Big data — better said, your data — can create unrivaled value for your members. Your data produces more than the next best purchase or transaction; it initiates models for loyalty and lifetime value from your members.”
What are they talking about? What, exactly, is this “big data” that he’s referring to? Most financial institutions I talk to keep telling me that they don’t do enough with the “small data” they have.
The truth is, there’s a yin and yang to the concept of data in marketing:
- Yin = You have to have useful data.
- Yang = You have to be able to do something with that useful data.
It has become trendy to say that financial institutions have all this data they’re sitting on that they don’t make good use of, but few firms really know what data elements are good (or useful) for what marketing purposes. Consultants that spew big data nonsense and toss around terms like “predictive analysis” and “next best product models” are only talking about the Yang (data analytics capabilities) without any consideration for the Yin (data quality). The reality is that the job of data quality typically falls to the IT group inside the bank or credit union. It’s their job to get the data in one place, get it cleaned, and make it accessible. But that really doesn’t address the utility of the data — the Yang.
The term gentrification usually refers to the renovation and improvement of rundown neighborhoods. But the word could also be applied to the financial industry’s data. Most banks and credit unions have some ugly issues with their data — the assets, warehouses, and analytical capabilities are all rundown and desperately in need of gentrification.
In my contribution to the Top 10 Retail Banking Trends and Predictions for 2017 article published here on The Financial Brand was the following:
“Over the next few years, banking providers will embark on data gentrification efforts — not just cleaning up the data they have, but collecting and using better data.”
Hey, let’s admit it… data is boring. If you went to an executive team meeting and said the big strategic initiative for the next year should be gathering, consolidating and improving the quality of the institution’s data, it might be your last presentation for a while.
But you have to face the truth: Your data sucks. And it’s not just a nuisance, it’s a serious strategic problem.