The Problem

Decision makers across many industries utilize data science to gain an edge. In some cases, enhanced decision making driven by data analysis lags significantly.  The multifaceted world of commercial real estate has been slow to apply the analytical concepts underpinning big data, particularly when it comes to site selection. Historically, decision makers within the space have relied on a combination of gut feel and macroeconomic indicators (eg. population growth, job growth, anecdotal traffic counts), leading to an “if you build it, they will come” mentality.  

The protagonists in Michael Lewis’ Moneyball recognized that biased and flawed perception often leads Major League Baseball teams to misjudge players, resulting in mismanagement of their teams. By refashioning player selection decisions to focus on statistical performance metrics, the team managers achieve a new paradigm of success:  fielding a championship level team for less money.

Why is the Moneyball analogy important? How is IdealSpot bringing real estate decision makers out of the Dark Ages?

Context

Moneyball tells the story of how big data and mathematics changed the game of professional baseball.  In the film, the strategic goal of the baseball manager should be to manufacture runs, rather than simply buying and investing in players. The book lays out the importance of Bill James’ approach to player analysis, whereby a player’s on-base percentage and slugging percentage had previously been undervalued and underutilized predictors of runs scored.  The proper application of data and statistical analysis resulted in a deeper understanding of observable patterns which enabled a team manager to field a championship team while spending less money, thereby improving a club’s overall ROI.

By corollary, IdealSpot uses data to find the observable patterns in location analysis in the real estate universe.  The upshot, in statistically observable terms, is that those making site selection (or tenancy) decisions achieve the best outcomes possible, resulting in strong customer traffic, lasting tenancy and reduced vacancy.

The data-based tools provided by IdealSpot enable decision makers within the commercial real estate industry to more effectively assess data and identify behavioral pattern recognition to establish identity. This helps the client stitch together a narrative that is unique to every business.

The Solution

At IdealSpot, we live and breathe data science. By looking across all locations/assets, we can tease out the correlated behavioral patterns that persist across all our data sets. This process establishes a success map. This can then be applied to any geographical region throughout the US to identify highly attractive opportunities for acquisition or expansion efforts. This process utilizes a combination of consultation from our in-house data team and a robust on-demand platform.

Process

  1. Conduct a correlation study of all locations to tease out the success archetype and key performance indicators.
  2. Upload the KPIs to our web-based platform to establish a watermark for comparing all locations and expansion regions.
  3. Utilize the IdealSpot platform to make incisive, data-backed decisions on commercial real estate assets. 
Marc Smookler

About Marc Smookler

Marc Smookler has founded 6 companies—2 of which have been acquired and 3 of which are market leaders in their respective spaces—the leading brick-and-mortar retail analytics company (IdealSpot.com), a leading online retailer (SakeSocial.com), and a cutting-edge marketing services platform (Written.com). Marc’s companies have generated over $300M in lifetime revenues and sold over 150,000 products worldwide.

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