Ask The Expert: Article

Q: How can we make smarter decisions about where to locate branches, retail operations and salesforce assets?
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The days of designing distribution based on anecdote, intuition, and imitation are over.  As companies become increasingly sophisticated in the design of their salesforce networks and physical footprints, data-driven approaches dominate.  From retailers plotting their storefronts, to banks considering de novo branch openings, to insurance companies designing their agent coverage models, leading firms today systematically leverage trade area insights, local market data, and prioritization algorithms to improve distribution outcomes.  However, most companies have not yet perfected the blend of art and science required to optimize distribution.  Based on our work with leading firms across financial services and retail, we have identified six common mistakes that limit the effectiveness of location analytics and suggest alternative approaches to unlock additional value from the discipline:

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Diving into location analytics without proper strategic alignment can severely limit the effort’s impact.  In particular, the design team must heed enterprise strategy regarding the target customer to be acquired and their channel preferences, the value proposition on offer, the degree of geographic diversification indicated by risk analysis, and the aspirational customer experience.  These factors should inform where to place distribution points – ideally, in those spots with large and underpenetrated populations of target segments and in risk-diversifying locales.  They should also inform what types of distribution points to invest in when it comes to retail format, size, hub and spoke configuration, and staffing.  A firm with a focus on high-net-worth clientele, little urgency around geographic diversification, and a proven model of dedicated relationship management should approach its location analytics very differently than a firm with a mass market offering, geographic concentration, and a price-based value proposition.

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Some companies rely heavily on analytics to determine new retail, branch, or salesforce locations, but turn a blind eye to legacy, existing locations.  Managers should think of trade area analysis as a network optimization strategy, simultaneously taking into consideration both existing assets and potential new locations. Existing locations can be assessed relative to the local market potential that is indicated by data-driven trade area analysis.  Is each existing location under-performing, over-performing, or meeting expectations based on the potential of the market in which it is operating?  Answering this question reveals whether an existing site ought to be shuttered to free up funding for higher-priority locations, rescaled or reformatted to align the investment with local market potential, or even grown to unlock additional local market opportunity.

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Trade area analysis often leaves out helpful data points that would yield better results.  Most companies check the box on relevant, point-in-time population and income factors, but many leave out highly predictive indicators like local brand awareness and consideration, local business ownership and employment trends (for commercial banking and insurance, for example, “where they work” is more important than “where they live”), competitive intensity, and real estate costs and talents costs.  Of course, the right set of factors will vary by industry and company circumstance.  As a result, the best approach may be to reverse-engineer prioritization factors by running regressions on historical data to identify which local market characteristics were most predictive of the success or failure of company distribution points.

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Many companies will conduct trade area analysis using off-the-shelf geographic units (e.g., comparing zip codes, CBSAs, or MSAs).  This leads to distorted comparisons because these units may vary greatly in shape and size and because population clusters that are adjacent and addressable but sit just outside the geography will be missed.  A better approach is to deploy radius analysis that creates standardized geographic units defined by a specified distance from the centroid of the zip code, CBSA, or MSA.  The size of the radius should be carefully considered in the context of industry dynamics and distribution point objectives and may need to vary between more urban and more rural markets.  In addition, the team will have to analyze any cases where prioritized radii overlap one another, using local market insights to decide whether to consolidate or duplicate distribution points.  Notwithstanding this additional complication, the radius approach will yield more meaningful results than the alternative.

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Location and trade area decisions cannot be made entirely from an ivory tower.  Executives must leverage the collective wisdom of sales and distribution decision makers, particularly those who understand local market dynamics, talent availability, historical market performance, and other factors that will heavily influence the success of distribution points but that may not lend themselves to systematic modeling.  Best practice is to use the output of the trade area analysis as a strawman or starting point for discussion with distribution leaders, who then layer on their expertise to ensure success.

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Just as the sales and distribution decision makers need to be involved, front-line personnel also need to play an active role. It is crucial to leverage enterprise tools to share location insights with field operations who drive day-to-day activities. Companies can use visualization software and provide systems access to equip front-line personnel with trade area insights.  Local access to these rich stores of data can enable field management to build out local market, financial, personnel and marketing plans that align with the recommendations of the analysis.

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