What Determines the Success of a New Location? All About Data-Driven Site Selection
For multi-unit franchise organizations, deciding just where to put their next location for a store, restaurant or service center can be a daunting task. This assertion should especially ring true for organizations that have 20, 50, 100 or more operational locations under their belt and yet they still do not know why some locations turn out to be superstars while others are dogs. How we can better differentiate between the superstars and the dogs at the site selection stage?
There are many ways to approach site selection for new store locations. Every company who makes these decisions in-house has its own, home-grown methodology that makes logical sense and that is certainly founded in rational thought. However, in practice the models are sometimes highly predictive of store success, while other times they fall short.
At MindEcology, our experience in helping clients in multiple industries to select ideal locations for their ongoing expansion efforts has given us a deep understanding of what makes some locations more successful than others.
In our experience, we find that in-house site selection committees are typically biased in the following 3 ways when it comes to ideal site selection:
* they rely too heavily on the quality and condition of the physical building and its immediate surroundings, while largely ignoring the composition of the population of the surrounding neighborhood
* they allow their personal preferences and anecdotal histories about a particular area influence their decisions
* they focus too heavily on a single data variable (e.g., absolute population count, median age, proximity to a major thoroughfare, etc.) as a determinant for projected store success
By contrast, at MindEcology our data-driven approach to site selection helps us avoid these common biases. Our approach is unique in that we:
1. Base our models on historical store performance data (when available)
2. Leverage advanced segmentation methodologies that transcend the use of more traditional, simple demographic factors such as age, income, and ethnicity factors
3. Pre-test 20, 30 or more individual variables for possible correlation to store success before culling our variable list to a more manageable number
4. Combine the most highly correlative (to store success) variables in unique ways using advanced predictive modeling techniques
5. Our models are internally self-validating, greatly reducing your risk of making an investment in an undesirable location
6. Provide a full set of recommendations for ideal store locations within a give market
In our experience, about 75-80% of store’s future success is relative to “who lives there” (in the surrounding neighborhood). Thus, our modeling process focuses heavily on those factors.
Around here, we often repeat the mantra “Trust the data.” Data-driven site selection techniques will trump the bounded rationality and inherent judgment bias of even the smartest of us humans any day. Trust the data and you can be confident that you have chosen well.