Decision Support Systems for Chain Store Real Estate

During the past year, the Chain Store Advisors blog has covered a variety of topics under the broad rubric of real estate planning and site selection.  Many of these ideas have been combined in a white paper released last month called “A New Approach to Best Practices in Real Estate Planning and Site Selection.” Sounds like a typical boring white paper title, but don’t be fooled!  The “new approach to best practices” is really a rejection of the very idea of best practices in chain store real estate analytics.

There are best practices for simple tasks and information systems such as backing up your data or verifying the accuracy of your locations.  However, for situations and systems that are complex such as the retail marketplace, it is impossible to create standardized methods and practices because the “cause and effect” relationships between things are too hard to predict and they are constantly changing.  A phrase used by people in complex systems theory is “emergent practices,” which describes ways of approaching and solving problems based on recurring patterns in data and collaborative decision-making.  Some of these approaches are described in the white paper.

During the past year I have spoken with dozens of people in the chain store industry and related fields to broaden my perspective.  I listened to innovative ideas, groaning of disillusionment, excitement about new technology, and nostalgia for the “good old days.”  Here are the top five insights and guiding principles that have emerged from this process:

  1. There is no such thing as a set of “best practices” in chain store real estate analytics.  Every company is different in its goals, culture, business model, and decision-making processes.
  2. There IS such a thing as a “bad practice” in chain store real estate decision support systems.  Examples include poor data quality management, unrealistic expectations for mathematical models, and failure to use local market knowledge in evaluating sites.
  3. The culture and experience of the real estate development group and its relationship to the rest of the enterprise is the most important determinant of success in choosing good locations.  The best information system in the world is useless if the people and processes for making decisions are dysfunctional.
  4. Each company has its own point of diminishing returns (“PDR”) in the use of science and technology for real estate decision support.  The business concept, availability of data, available staff, and many other factors combine to determine the PDR, which should be used as a guide in defining requirements and budgets.
  5. Good decision support systems are built over time.  Every company should have access to mapping, demographic reports, and local market knowledge.  Investments in people and technology for analytics should be based on the company’s ability to interpret and apply the guidance, and blend the science with the “art” in the heads of the experienced real estate team members.

How can these insights and principles be applied today?

The first step is NOT to have a “dog and pony show” with software vendors to select or replace a system!

The starting point is an assessment of the culture of the company, and specifically the people in and around the real estate team.  One useful framework is described in the book Tribal Leadership which classifies working groups of 20-150 people as tribes in different stages of development.  Most tribes are in Stage 3, which is characterized by the statement “I’m great,” in contrast to Stage 2 (“my life sucks”) and Stage 4 (“We’re great.”)  It is common for the real estate group in a chain store company to operate in a “silo” that is isolated from other groups that are closely related such as marketing  and assortment planning.  The increasing importance of “omnichannel” retailing and integration with ecommerce further underscores the need for collaboration between the real estate team and other business units in a chain store company.

For a short video on this topic, visit

What difference does the culture of the “tribe” make in the design of real estate decision support systems?

There is a higher risk of disappointing results from analytics when a company’s culture is characterized by internal strife, competition, and distrust.  As companies become more aligned in their goals of working together to make customers happy, they are able to effectively implement analytic solutions that require acquisition and processing of large amounts of data from multiple internal sources.

For companies that are ready to implement state-of-the-art information systems and analytics, there are business partners ready to help.  Two of the companies that are leaders in this area are Trade Area Systems (Attleboro, MA) and Intalytics (Ann Arbor, MI).

Trade Area Systems offers TAS Unity, the only fully integrated market knowledge system for mapping and demographic reporting via desktop, online, and mobile applications.  Companies such as CVS, Family Dollar, and Ross Stores have trusted Trade Area Systems for their reliable, scalable data management, mapping and reporting systems.

Intalytics was founded by three of the former partners of Thompson Associates, the leading chain store analytics firm since the 1950’s.  Their clients include Home Depot, Advance Auto, Darden Restaurants, Chipotle, and Fifth Third Bank, to name a few.  They offer a wide variety of analytic services and software for market planning and site evaluation.

For further information on any of these topics, please contact Jim Stone at Chain Store Advisors:


1 thought on “Decision Support Systems for Chain Store Real Estate

  1. Pingback: Decision Support Systems for Chain Store Real Estate | re-lytics

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