Frequently Asked Questions

Is my financial institution too small to consider branch network optimization as a top priority?

No. Irrespective of size, retail institutions must have the ability to apply proper analytics engines to their data-mart. Predicting demand and customer behavior will continue to be a principle factor in an institution’s ability to remain stable and relevant.

How many branches are needed to serve a market?

If your strategy is to maximize your reach/coverage of all target households in a market, there are several methods for determining that metric. You will want at least a 6% branch share to improve your odds of getting more than your “fair share” of new business. If you are trying to aggressively increase your market penetration, then one branch per 12-15,000 households should be targeted. If you are trying to implement the “thin network” concept, you can target one branch per 20,000 households.

What is the typical payback period for new branches?

It depends on several factors, such as level of capital investment, market factors (e.g., lease costs, level of competition, etc.). A good rule of thumb is new branches should generate a positive cashflow with first three years and achieve full capital payback by years 5 or 6.

When I close a branch, how much incremental customer attrition should I expect?

Another “it depends” answer as many factors are involved, including distance to next nearest branch, capacity to absorb incremental customer volumes at nearest branches, mobile usage levels of impacted customers, and customer service levels.

What is adaptive redomiciling?

Adaptive redomiciling is the process of realigning your customers/members branch assignments based on their recent transaction and new account opening behaviors. Long-term customers may change their branch network usage patterns after relocating of changing jobs. Understanding you branch network’s performance relies on having up-to-date facts on each branch’s contribution to the network.

My branch trade areas overlap. Is that a problem?

Branch trade areas are unique, as they are based on each branch’s ability to attract new and existing customers. Some level of overlap is good, since it implies a level of branch network convenience. Our previous research indicates some level of overlap increases sale productivity from the overlapped geographies. But at higher levels of overlap, there is no incremental sales value, just incremental expense.

What are gravity models and how do they apply to bank branches?

Gravity models in retailing have been around for decades. The first Gravity Model for retail analysis was based on Reilly’s Economic Law of Retail Gravitation, which suggests that customers travel longer distances to larger retail centers that offer more choices (greater gravity or mass). At TerraStrat we utilize the Huff Gravity Model that allows “attractiveness” to be defined by multiple factors and not just size. Both models borrow from Newton’s law of gravity.