To Act or Not to Act: 2 Metrics Drive Banking Customer Retention

To Act or Not to Act: 2 Metrics Drive Banking Customer Retention

Recently my satellite company called me up. I thought it was a typical sales call, upselling me to some premium package. But I was surprised… They told me that data they are collecting on my system showed that we are having some signal loss and they would like to send someone out to fix it – on their dime. No service call charge, regardless of what they find. I was a little shocked, and I must admit, I was looking for the catch. There wasn’t one. But it was true that we were suffering silently with some signal issues. They sent out a tech. He reviewed the situation, found the installation wasn’t done right, and re-installed the entire satellite system. All without us asking. I asked what the motivation was for taking such a huge step. He said the company had identified that they were losing customers based on quality issues. Those quality issues were totally fixable, but they just didn’t know about them. So, they started collecting data. But what amazed me was the proactive way in which they intervened – even those they weren’t asked. It got me thinking about our banking customer retention experiences. Would you intervene financially with a customer if you thought – but did not know – the relationship was at risk? I’ve asked this question to many banks, and all too often the answer is no. At best, I get an “I don’t know.” Knowing When to Act The problem is that our banking customer retention efforts are build on being reactive, not proactive. We respond when a customer complains or closes...
3 Reasons for Price Optimization in Banking Besides Profit Improvement

3 Reasons for Price Optimization in Banking Besides Profit Improvement

Anyone who has been following bank price optimization technology since 2003 knows that the science was originally deployed to squeeze out additional profitability in portfolios that wouldn’t otherwise been seen. The idea was to use price sensitivity of customers to split them into sensitive and insensitive customers and trade off. Raise prices on the insensitive customers and gain profit. Lower prices on sensitive customers and gain volume. The loss of volume in the first group is offset by the gain in the second group. Profit increases overall. Seems like a great idea, right? Well, just using price sensitivity to arbitrarily increase profits hasn’t been a great motivator for the technology. Banks are worried about a lot of other things: regulatory pressure, customer engagement, share of wallet, etc. In fact, a recent survey of US community bankers by KPMG showed 57% of revenue-growth initiatives are around operational areas like cost reduction, divestitures, operational improvement, and M&A. Only 13% of initiatives are around business model changes, like price optimization. So, is price optimization dead? No. Think about what price optimization is. It’s using statistics to predict how your customers will behave when you change price – and then leveraging that information to be profitable. Basically, it’s what you should be doing anyways. But you still have to come up with a business case for the technology. I’d like to give you three reasons that correlate to three of Jim Marous’ top 10 retail banking trends. You can see the full list here. 1. Increased Competition (#3 on Jim’s Top 10 List) When competitors change their prices, the relevant question is: “now...
Analytics to Improve Customer Experience

Analytics to Improve Customer Experience

I just had a chance to speak at the Consumer Bankers Association 2014 Conference, CBA Live 2014: Red, White, and Banking in Washington DC. I talked about how banks need to use customer analytics to improve the customer experience. When they do this correctly, they truly improve both the long-term profit of the bank and create value for the customer. I outlined several great case studies from Earnix that did just that.   In the meantime, however, it reminded me of a post I wrote over a year back about channel optimization. I talked about how aligning customers to the right channel for each interaction, both the bank and the customer are happy. It’s an oldie, but a...
Nobel Prize Worthy Lending

Nobel Prize Worthy Lending

In the wake of Nobel prize winners Al Roth and Lloyd Shapley, it’s worth analyzing the matching algorithms to see their application to banking. I’ll walk through the basics of the matching algorithm here, but Alex Tabarrok provides a much better and more thorough primer here. The Basics The Nobel prize winning algorithm deals with matching. For example, men and women to get married or transplant recipients. Essentially, the algorithm allows one group to select its ideal match, allow the other group to reject or retain the offer, then rejected first group members make their offer to their second choice, which in turn rejects or retains the offer. This continues until there are no more offers. The reason the algorithm is good is because it “converges.” That means there is actually a solution in the vast majority of cases. But, it also has this great property that there are no pairs that would rather be together but are not. There may be disappointed first parties and disappointed second parties, but no mutually disappointed pairs. In my case, I want to discuss the algorithm for lenders and borrowers. The big problem we have in both consumer and commercial lending right now is a mismatch. Banks want to lend to those businesses that need the lending least of all. But of course, banks want to lend to someone, because if they don’t, they don’t make money. On the flip side, borrowers would like the best choice – and given the fear that banks may be left out – borrowers may benefit from bank offers that otherwise wouldn’t be made. Of course, banks don’t just make one loan. Fortunately, Roth has extended...
Non-Bank Business Lines of Credit Change the Risk Formulas

Non-Bank Business Lines of Credit Change the Risk Formulas

Google just recently announced the launch of a small business credit card that only allows for purchases of AdWords campaigns. Along the same lines, Amazon just recently announced it will be offering credit for its online sellers to invest in inventory sold via Amazon. In Google’s case, they’ve opted to use an issuer, Barclaycard, to process through the MasterCardnetwork. In Amazon’s case, they’re going it alone. In both cases, the question is: will this be a threat to traditional card issuers or other commercial lenders. So far, the conventional wisdom is “no.” I disagree. I think the fact that these lines of credit are issued by companies that directly benefit due to the growth will in fact change the landscape. When you look at traditional risk metrics for a line of credit, you are mostly interested in the net interest margin: what you charge minus what you lose minus what it costs you to find the account. (Yes, I’m oversimplifying.) But in the case of Amazon and Google, there’s another component of the profit equation: what you earn from the additional business. This additional profitability component could make all the difference in the world for small business owners. It could tip the scale from “too much risk” to “let’s take the risk.” I know there’s a similarity between retail cards used at, say, Sears or JCPenney’s, and that those retailers finally opted to go with issuers instead of self-funding in most cases. These credit lines drive growth in businesses where retail cards simply fund expenses. That’s why an “HP line of credit”, though it exists, isn’t game changing. HP doesn’t earn...