Time to Overhaul Your Analytics Engine?
A friend reports on a recent and rare visit to his bank branch: “I felt like I was in a time warp. There were signs all around pushing home equity loans. Are those tactics working well these days?”
He has a good point. Banks are still in the banking business; they can’t start selling handbags and tattoos just because loans and fees are out of favor. But it does seem that a branch touting home equity to all comers might be stuck in pre-recession mode, since today’s borrowers have less home equity and appetite for debt. But more to our point, what are the odds that a person in the branch for his own reasons would be tempted by a sign to make such a significant purchase?
That is why you have customer analytics data: to enable you to make a judgment about what product a customer might be in the market for and their preferred channels for interacting with you. But even analytics can get stuck in a time warp. If your analytics engine still depends on assumptions formed in a different economic environment, you’re likely getting bad answers.
Today’s recessionary environment has affected how people view money. After six decades of steadily increasing their debt levels, Americans lowered their ratio of household debt-to-income by 11% last year. Many customers now scrutinize their statements for fees – old ones that they used to tolerate as well as new fees. They are newly anxious about their low savings. They have slashed spending.
It follows that these people are no longer susceptible to sales ploys that would have worked before. Reaching them will take new ideas, new strategies, and fresh, subtle execution. But is your bank’s analytics engine up to that job? We have identified four common ways today’s analytics often fail the up-to-date test:
Missing new signs of the likely-to-leave customer. The traditional sign of a decamping customer was a sizeable drop in deposits while the deposit account itself stayed put. Many customers who closed other accounts would leave the checking account in place for a few months longer, out of inertia or indifference. So banks watched for sudden drops in balances in order to initiate retention measures.
But today, what are the signs of a likely-to-leave customer? It might be multiple calls objecting to new fees. Does your database capture incoming calls and their nature? It might be the cessation of one bimonthly direct deposit into an account, even though another direct deposit continues – perhaps one half of a couple choosing a new bank, putting the other half at risk, too. Would that raise a flag at your bank, and would that information make its way into your analytics?
Another tell-tale sign of potential attrition is a change of address. A relocating customer used to mean losing the customer to a more convenient branch. Now it can be an attractive opportunity to convert the customer to online and mobile channels. Do your analytics highlight that information, and with enough lead-time for you to act?
Failing to satisfy newfound desire for savings. For the past decade or so, saving was not on the radar but today it is. Developing a savings cushion is a high priority for many account holders uncertain of their employment prospects. Are you able to recognize a customer’s ability and propensity to save?
Can your analytics engine identify, for example, customers writing checks to external investment accounts and tell you if your savings offerings might work better for those customers? What if the checks are going to payday lenders – can you make them a better offer of credit? What about customers who keep checking balances well over the required amounts? Can you make sure they don’t get nuisance offers of credit but instead suggestions for making their money work harder for them?
Holding outdated notions about the inclinations of various demographics. It’s commonly assumed that young people are tech-savvy but older people aren’t. That statement isn’t false, but it overshadows a legion of exceptions, closing off rich pockets of outlier segments. Retirees Skyping from their RVs and glued to their iPhones are ripe targets for mobile banking. Just don’t hope to capture them with the same edgy video outreach that works for twenty-somethings.
Millennials might be happy to transact ordinary banking business almost exclusively online or on mobile devices, but when they have a problem or an important banking transaction, they have high expectations for friendly, personal service and for a relationship with someone at the bank who can help or guide them.
Today many fifty-somethings no longer fall into a “nearing retirement” segment. Some still do, but many others, looking at severely depleted savings portfolios, will be working for another two decades. They are a new segment with different needs.
A finely tuned analytics engine, fueled by information about how these customers currently conduct their business, is crucial for peeling back predictable layers and finding special opportunities.
Not recognizing the changing value of relationships. Dramatic changes in household status over the past few years have drastically changed the current and potential value of customer relationships. Many that were ripe for growth before are no longer. A small business owner might still appear prosperous, but if he also has a suddenly unemployed spouse or an underwater mortgage, his potential changes. Conversely, some accounts that were candidates for maintenance-only may now have richer prospects.
Constant vigilance and constant search for more external information can avoid wasting marketing dollars and associates’ time on some relationships while passing up sales opportunities as they arise in other relationships. And don’t be stymied if absolute customer value is elusive. Relative value can still be used to drive decision-making.
Here’s the good news. Virtually all the information your analytics engine requires is available – perhaps not at your fingertips and probably not well organized, but it is there. In fact you probably have more data than you know. As you make the effort to refresh your analytics just be sure to bring two firm attitudes to the effort: One, be well aware that many old assumptions are probably wrong. Two, be endlessly curious; query the data from all sides and viewpoints and be willing to go down surprising paths.