BID® Daily Newsletter
Oct 28, 2010

BID® Daily Newsletter

Oct 28, 2010

GEOLOCATION STRATEGY (PART II)


Yesterday, after seeing vendors selling T-shirts and baubles on the street corner for the World Series, we made the analogy that from a market perspective, where a bank places its locations can have a great impact on earnings. We stepped through the importance of setting objectives, so today we look at additional steps banks need to go through to optimize both the opening and closing of locations (such as branches, ATMs, kiosks or loan production offices).
Once objectives are set and weighted as to importance, a list of potential areas can then be mapped and information can be collected. Here, common mistakes are not looking at a granular enough level and not weighting forward looking data. Items like upcoming property tax rates, building/housing absorption, permits, zoning changes, development plans, major employment trends and others should be compiled in addition to the more common demographic, geographic and economic factors. Once collected, this data can be compiled so you can prioritize objectives. Usually, employment trends receive the top rating for a full branch, as this statistic has the highest correlation to credit quality, which has the largest impact on profitability. Other data points can be ranked (see picture above) depending on whether the objective is to generate loans, deposits, fees, gain marketshare or retain customers. The good news is that most of this data and/or models can be purchased (from the econometric models that drive retail or municipal service locations), so it is not that difficult to do.
Next, banks can select areas that have the highest weighting of the data streams and creating a short list of available locations. Once a short list is established, location-specific data such as site traffic, security, visibility, costs and proximity to other locations can be determined and ranked again in order of importance. Here, often projected P&L statements can be produced in order to put the model into economic terms based more on profitability.
Once a through understanding of each site on the short list is obtained, a strategy can be employed. This is where objectives need to be crystal clear, as oftentimes deciding between customer convenience and shareholder value are at odds with each other. For example, if the competition is located near the boundary of a desired area, choosing a location that puts your new branch in the closest location for a majority of customers may result in the largest profitability, but it won't necessarily result in the shortest travel times for your existing customers. If customer satisfaction was your primary importance, spacing locations so that you and your competitors are located equal distance to the desired population centers would then be an optimized strategy.
In similar manner, if the goal is to capture business customers below $5mm in annual revenue that have fewer banking relationships, the best strategy may be to locate your branch away from competition. However, if you are going after larger business customers that already have multiple existing relationships, oftentimes "clustering" despite a close proximity to the competition may make sense. Banks also need to anticipate changes from the competition. Nothing is static, so bank failures and branch closings can make many unprofitable areas marginally profitable again.
It should be noted that the same analysis of deciding where to open a branch or place an ATM, is the just the reverse of what to do when analyzing which location to close with one exception. While banks want to get rid of their least desirable location, the real key to the decision is determining how the highest numbers of profitable customers can be retained through other locations.
We may expand on this topic in future editions, but the important part to understand is that making location decisions is not about optimizing branch traffic, but rather more about optimizing opportunity for profit that meets your strategic objectives. By taking a quantitative approach and leveraging game theory, banks may be able to boost profitability by a factor of 2x to 7x compared to the often utilized "gut feel" guess.
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