What Makes “Best” in Next Best Offer?

What Makes “Best” in Next Best Offer?

We Need Better Marketing Banks today are looking to drive significant improvement in their marketing efforts by using real-time analytics. And among the latest efforts, there’s a lot of talk – and promise – about Next Best Offer. No one really agrees on what Next Best Offer means. It wouldn’t be the first time we all use terms we don’t really know what they mean (uh…big data, maybe?) But I want to clarify what Next Best Offer should mean. Or at least tell you what it shouldn’t mean. When I say Next Best Offer, I mean the offer that adds the most value to the customer – as measured by an increase in their overall expected customer lifetime value. Expected customer lifetime value is made up of two parts: The profitability if they accept the offer, multiplied by The probability they accept the offer. Most Next Best Offer engines only offer you the second part: the probability. They have models that tell you which kinds of customers should get which product. But they don’t speak to the individual profitability of the customer. Three Phony Next Best Offer Solutions I want to talk about three solutions that often get called Next Best Offer that aren’t. If you have one of these, they’re not bad; but they’re not Next Best Offer either. They can be improved. The Demographic Segment Solution There are many vendors who have taken marketing data – both demographic and psychographic – and created customer segments in retail banking. They can take a individual customer and map them to these segments. Once they’ve mapped a customer, they have chosen...
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...