Today, many banks, credit unions and lending institutions with brick and mortar distribution are faced with the bottom-line effects of a decline in overall sales performance as well as decreased market share and profitability in their existing markets. Some of the main contributors include same store cannibalization, competition, alternative distribution channels and underperforming legacy branch locations.
For example, a typical scenario might be to hypothetically close underperforming branches and re-optimize the market to determine opportunities for relocation. Another option might be to use a “greenfield” approach that assumes there are no existing sites and optimize the market to determine a branch placement strategy. In addition to using the market optimization tool to improve their branch distribution in existing markets this tool can also be applied to new markets that are under consideration.
A predictive sales model combined with market optimization functionality serves as an important input for key decision makers to plan their branch network strategies for optimal sales and profitability in existing and new markets. The example below demonstrates how combining client and EA data sources are used to achieve this result.
A leading provider of small personal loans to the low-to-middle income consumer with over 350 locations was seeking a way to optimize their existing and new markets for their brick and mortar distribution channel. They felt that in many markets they were over-stored and needed to reduce the number of branches without compromising on market share and total market sales. Their business need was to have the ability to interactively assess existing and prospective branch locations with measured impacts on cannibalization, competition and distance to their customers.
Environics Analytics’ location research team combined available customer transaction data, historical branch sales data, and their databases on census demographics, household segmentation, competition and personal loan demand estimates to develop a predictive sales model.
EA utilized a building blocks research approach (as illustrated below) where key insights and learnings drawn from each research phase were then applied as an input for predictive sales model development.
The site model was then implemented in EA’s online market optimization tool that enabled this client to quickly and interactively determine an optimal branch network strategy.
The following is a sample market optimization report and thematic map that were generated from the EA online market optimization tool following each scenario when applied to a single market.
Illustrated on the thematic map is the order of placement of prospective locations defined by total sales potential. The report that follows provides a location address for prospective locations sorted in descending order by sales potential.
The completed model and market optimization tool enabled this client to:
The example above is one of the many ways EA can help banks, credit unions, and lending institutions address challenges in the face of a changing consumer and competitive landscape, and significantly improve their branch distribution planning and resource allocation efforts.
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