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 what?”
For deposits, as rates start to rise in the US or continue to be low in Europe, what do you do?
Do you match? Do you hold low and continue to collect margin?
What if staying low loses customers? What if matching loses margin? You can ask the same questions about loans.
Can you see this is more than bottom line improvement? This is leveraging customer behavior to make the right decision for your portfolio.
And it’s more than just your traditional competitors. Jim Bruene, founder of Finovate said recently:
The alt-lending sector will begin to be taken as a serious competitive threat to mainstream lenders with an outside chance that one or more mid-size or larger financial institutions will begin offering P2P lending services of their own.
By having a system that collects feedback from your customers in the form of reaction to price offers – whether individual or published, you will have the data at your fingertips you need to chart these previously uncharted waters. The only way to make decisions in this kind of uncertainty is to have a decisioning platform up and running in your bank that gives you constant feedback on your prices and how customers react to them.
Lesson learned: you need a software framework to run this.
2. Focus on Customer 3.0 (#5 on Jim’s Top 10 List)
The way Jim describes customer 3.0 is as follows:
Customer 3.0 is digitally connected, highly informed and demands a highly personalized approach in their communication, their products and the service they receive.
In other words, personalized banking. That’s the premise of my book, Seven Billion Banks. In it, I describe one of the major pillars of a personalized banking experience: next best action. Next best action is using optimization to not only recommend the right product at the right time, but at the right price.
Banks miss the boat when they use next best offer technology without price optimization. One bank in central Europe is able to individually tailor retention offers to their retail banking customers by loading them into the CRM before the discussion. That means, not only do their create a better customer experience – stopping the endless “I’ve got to check with my manager” routine, but they do it profitably. Focusing on long-term customer relationships that pay. All the while, they create an atmosphere of personalization; they know each customer well enough to make a personalized offer.
Here, price optimization plays only one role in an overall decisioning framework. That means being able to solve lots of problems, from retention to marketing – and yes, pricing.
Lesson learned: you need a decisioning framework that goes beyond pricing.
3. Differentiating Brands (#9 on Jim’s Top 10 List)
Consumers view most banking brands as undesirable and wholly undifferentiated. … No other industry in the [brand profile] study – not even the oil industry – suffers from such a profound lack of diversity.
Brand is a combination of service levels and customer segments served. Where is your sweet spot? Do you know?
If not, you need to collect data from your customers and find out, based on your current product offerings and risk profiles, which ones you can be profitable with and which ones you can’t. Simulate different brand propositions by placing constraints on the optimization; set portfolio-level goals about risk mix and customer types. See where the money is.
Once you know – I mean, really know – where you can be successful based on your bank’s cost structure and footprint, then go out and market to those segments. Increase your depth in those areas. And build a brand around it. But you need a tool you can use for simulation that gives you feedback based on your organization’s goals.
Lesson learned: you need a decisioning framework with a robust simulation tool and global constraints.
Bonus #4. Regulatory Compliance (This one isn’t in Jim’s Top 10 List – surprisingly)
I can’t think of a bank right now that doesn’t have a huge IT portfolio of projects around regulatory compliance. Be it fair lending, Basel II and III, stress testing, or consumer protections, every bank is trying to satisfy a regulator that the decisions it makes are consistent with fairness and industry stability.
That generally means regulators want to see how your bank will achieve their demands and what happens in stress scenarios like interest rate movements or macroeconomic trouble. Right now, most banks are handling this from a top-down approach. They build some high-level models that represent the total portfolio of the bank and they run it through its paces.
One of two scenarios often occur:
- The bank is unable to find a way to profitably meet the regulatory target, or
- The bank’s model for handling a particular stress scenario shows a result that is unacceptable to regulators.
What an optimization-based decisioning system can do is address both of these issues. Fundamentally, analyzing price sensitivity of customers creates a system that gives you a customer-by-customer set of directions to meet your corporate goals – both economic and regulatory.
In the case a regulatory target needs to be met, it may require product, mix, and profitability goal changes. These constraints can be relaxed while the regulatory target becomes the constraint. The decisioning system will then direct pricing and customer-level decisions in order to achieve the regulatory target. It will also indicate which existing business rules need to be relaxed in order to achieve them. By still maximizing profit, you will be able to reach the regulatory goal the most profitable way possible.
In the case a stress test shows that there are liquidity issues in the portfolio, a top-down approach rarely provides the insight needed to find the fix. It doesn’t provide enough guidance on customer-level decisions like pricing and segmentation to create a solid portfolio. An optimization-based decision system, however, pin points the issues directly at the customer-level, indicating which segments, risk bands, or product types that need to change. And not only do you see the problem, you know how to fix it.
Lesson learned: optimization can address your regulatory and compliance projects too.
Review of the Lessons Learned
We learned four things from these key initiatives.
- You need a software framework to do price optimization. Consultants and back-office analytic projects will not suffice.
- You need a decisioning framework that goes beyond pricing. Siloed point solutions that only do price optimization will not help.
- You need a decisioning framework with robust simulation capabilities and customizable objectives and constraints.
- Optimization can solve your regulatory and compliance challenges as well
I can’t discuss optimization frameworks without mentioning Earnix, the company I work for. Their platform meets all these criteria, and as far as I know, is the only one that meets all of them. Folks at Earnix would be happy to discuss it with you.
Of course, I started this off by saying there are reasons besides profit improvement. There still is profit improvement. In fact, between 10 -20 bps on loan portfolios and on deposit portfolios, between 3 – 5 bps in low interest rate environments (like the US and UK) and between 10 – 15 bps in high rate environments (like Turkey and India).
Typically, these numbers are more than enough to justify the investment, generating ROIs between 10x – 15x.
Look into optimization-based decisioning to see if it’s time for your bank to use this new and profitable analytic technique. I’ve given you at least four reasons to start.