Is There Such a Thing as an Optimal Branch Network?
The search for an optimal network is a goal every bank and credit union should strive for in their strategic planning. However, there is no singular optimal branch network. Business models, target segments, customer bases, and capital budgets all vary between firms. So, the question isn’t “what is the optimal branch network?” but rather “what is MY optimal branch network?” TerraStrat Group can answer that question.
To Optimize or to Strategize?
The eternal search for the “optimal solution” can be a seemingly long and tedious journey. After all, who’s to say what’s optimal? Is there only one optimal or many optimal solutions?
I’ve written before about the benefits of having a larger branch network in my 6% solution stories, but not every bank of credit union can invest enough to build a branch network that size. If bigger is unattainable, even only in the short-term, are there alternative branch network configurations that are optimal for your budget? The answer is YES.
Optimal shouldn’t be thought of as a unique solution. The optimal solution for any bank or credit union, must consider my existing customer base, the markets I serve, any target segments I want to attract, and my capital and budget constraints.
To quote Don Norman, a researcher, professor, and author, best known for being the director of The Design Lab at University of California, San Diego. “In my opinion, no single design is apt to be optimal for everyone.”
Here are three different ways to think about the question:
How many branches would I need in market X to reach 50% of those Gen Z and Millenials I covet?
I can only afford five branches in market X and I want the broadest reach of all households. Where should I build those five branches?
My business model is targeting select business categories. Which one location would maximize my reach of those segments in market X?
From these examples, you can see that the optimal solution might be different for everyone. Optimal for a $1B community-based credit union targeting consumers will be different from a $10B community bank targeting commercial real estate developers.
One way to go about finding your optimal branch and ATM network involves studying each market you serve in detail, starting with:
What are the best retail locations within the market?
What singular neighborhood area provides the maximum reach into your target customer base?
What other local neighborhoods add the next most coverage?
Where do I see significant diminishing returns when adding one more branch?
All those questions can be answered nowadays using machine learning and the right data.
The Pathways of Perspectives
Let’s talk about approaching the problem from two perspectives of the use of gravity modeling. The first approach scores retail “locations” based on a set of factors, such as size/mass (total square footage, employment, sales, etc.), reach and coverage of consumers (or sub-segment) or small businesses, growth, or other factors at varying distances from the location.
[Quick definition for clarity: A retail “location” can be anything from an existing singular building to a large, complex shopping center. The general rule is that the larger the center the greater the attraction or draw. This concept led directly to the creation of large regional malls 50+ years ago.]
The second approach scores small geographies for the same characteristics, answering the question “which location(s) maximize revenue capture?” This second model also utilizes many factors that can be adjusted for your business model and target segments. Note that it is independent of an existing retail location.
The challenge in this second model is that standard geographies come in a wide variety of sizes. Census Block Groups can be a few 100 square meters or 10+ square miles. The way to mitigate that issue is creating your own geography, standardized by size.
At TerraStrat, we go small, using ¼ mile hexagons. Hexagons are the perfect shape for this type of analysis, as they fit together in repeatable patterns and are a close approximation of circles. Quarter-mile hexagons are about the size of a football stadium for a frame of reference. Put together, think honeycomb.
Combining these approaches, magic happens. You identify the best hexagon, next best, and so on, with each one reducing remaining demand/revenue. Each “optidot” can be measured for the revenue opportunity and graphed to show the point of diminishing returns. You can also measure how much coverage of any target segment or the market in whole.
Comparing these “optidots” to the nearest top-rated retail centers provides guidance on where to plan a new branch, thus answering the question, “which retail location best serves the best opportunity?”
The beauty of this approach is that it can be done in both a “greenfield” and brownfield” approach to define the “optimal branch network” for your firm.
[More definitions: Greenfield approach assumes you operate no physical distribution in a market, while brownfield diminishes demand to reflect your current penetration of the demand and your current sites.]
Closing Remarks
Let’s recap. There is no one “optimal” solution, but there is an optimal solution for your firm, your market, and your business model.
A search for that “optimal” solution should be approached from multiple perspectives to triangulate on the best practical solutions.
Using both a greenfield and brownfield approach can help identify opportunities to reposition current physical assets to better reach your targets.
At TerraStrat we can help you reach your “optimal” level.
