Interview: Steve Rymers on Market Analytics for the Financial Industry
Market analytics is playing a larger role in branching strategy, from helping select locations in the branch network to supporting more nuanced sales strategies. When many financial institutions are relying solely on data visualization tools and intuition, those using advanced market analytics have a competitive edge.
We asked Steve Rymers, Director of Applied Analytics for Financial Services at Pitney Bowes, to weigh in on the subject and show us how financial institutions can use this insight.
Momentum: What kind of insight do your clients gain from market analytics?
Steve: Our financial institution clients gain a complete overview of their branch network. They’re going to understand which branches are likely to experience growth in deposits and loans in the future, which branches they might want to consider consolidating and the financial implication of consolidation, where other opportunities within their market exist, and the overall opportunity from those locations in terms of deposit and loan forecasts.
The goal is for the client to gain a good understanding of their market from a descriptive data perspective, as well as a demographics demand and competitive perspective. And then from the analytical perspective, we’re helping them make better decisions by using our data, our modeling, and our expertise.
We’re not saying “open a branch at this intersection” because intuitively it feels like the right thing to do, we’re backing it up with hard data and modeling to answer the question “what does it mean to put a branch at this particular intersection in terms of deposit, loan, and revenue forecasts?”
The client’s intuition is important, it play a role in this process, but the role that we play is one of doing the homework on the market and helping them achieve an understanding of the potential of the markets.
Where I’ve seen this process work the best over the years is when you take that homework piece of it, the piece that we do, and combine it with the client’s local knowledge and intuition about their market, that’s where you tend to have the best results.
Can you walk us through the process of developing a market study?
We break the process into two phases. The first phase provides a descriptive overview through a series of thematic maps and data summary tables that give the client a really good understanding of the demographics, the financial services demand, and the competition within their marketplace.
That will give them potentially the first opportunity to see their members’ locations on a map, or member market share within a market, or certain demographic characteristics within the market that they operate.
I think that’s been important in these engagements in that it’s helping to build the knowledge base and understanding of the market dynamics or the markets that some of these institutions are operating in.
This initial phase starts to bring out discussions around “okay, here’s our market today, and here’s’ the demographics and competitors, and here’s where we’re at today with our branch locations and our members. What does the future look like for our organization? What do we want to test within the analytics?”
This provides an opportunity for the client’s management team to sit down and review some of the market dynamics and bring to light any discussions about strategy and tactics that they want to think about for the future.
From here we move to a second phase, where we use our WinSITE network planning model and predictive analytics. We’ll use the modeling to run a series of exercises that give us a deep understanding of the client branch network, how they should be looking at their location, which locations they should be looking to consolidate, where there are growth opportunities within their branch network, and where the next best locations for a branch are.
We can also input specific intersections and locations into the modeling to simulate locations that the client is already considering.
How is your predictive analytics approach different from data visualization applications?
What we do differently is that we use modeling and predictive analytics to help clients make decisions.
If you’re looking at tools that simply summarize data, a lot of the information at that point is left up to the interpretation of the user. Not to say that our approach isn’t, but what you’re going to get with our analytics and our tools is a modeled approach in terms of “how likely are you to achieve success at a particular location?”
When we’re identifying a new branch site or giving a forecast for a new branch site, we build the probability of success of that location into the analytics. What does success look like at that location? It could be $30M in new deposits, and those new deposits could be comprised of checking accounts and savings accounts, it could mean $14M in loans comprised of home equity loans, mortgages, etc.
Simply put, it becomes a matter of complexity. You can display data, but we’re taking it to the next level by not only presenting data but using analytics to help the client make a better decision. You get forecasts where other applications may only show historical data.
In a simple display model, for example, you could have ten competitors within a two-mile radius. Is two miles the right number based on the market density and that particular location? And the ten competitors, there could be three credit unions, two national banks, and five community and regional banks. How do you handle those as far as evaluating their competitiveness to your offerings?
What we’re able to do is differentiate those competitors, not only from their locations on the map but also the name on the door. Who are they? We’re going to go into a much greater level of granularity when we’re evaluating a marketplace and evaluating a competitor.
I understand that predictive analytics has even more powerful applications beyond location selection. Could you tell us more about that?
Organizations can leverage analytics from a branch and channel planning perspective, but they can also leverage it from a sales goal setting perspective, a market opportunity perspective, and they can even use it at a customer level to predict their next best product.
A lot of organizations rely on historical data to set sales goals, but the thing you don’t get when you use historical data is understanding what opportunity you really have in the market. “How many checking accounts should I be getting out of the market, how many mortgages should I be selling within this market?”
One of the pieces we used to build our Perform 360 platform is a score for each customer household as to what their next best sell is. We take the analytics down to the household or customer level and we can provide the client with a list of households and what their likely next best sell is based on the clients’ product offerings.
The tool helps you get an understanding of where untapped opportunity exists within the network. Much like WinSITE, we use it to analyze market based information like demographics, demand data, and competition. Perform also looks at the facility characteristics to level set the branches and understand that, depending on the branch type and facility characteristics, all branches don’t have an equal opportunity to sell through their location.
In some cases we’re validating people’s intuition, and in other cases we’re potentially contradicting their intuition. But what we’re really able to provide is a fact-based, objective view of the marketplace not only in terms of data, but also any branching decision that they could make. It’s hard to weigh everything in your head and gain an understanding of what’s important and what’s not, but the process that we go through helps our clients come up with an objective, fact based plan or strategy.
This isn’t a static platform, but rather something that is being constantly refined, correct?
This is something we do every day, and it’s something we put a lot of thought and a lot of resources into. We keep refining or process as the market dynamics change and branch transformation continues, and we continue to use our data and experience to find ways to help people make better decisions. At the end of the day that’s really what it’s all about.