Everyone knows the value proposition for chain store real estate research:
“If you avoid closing one or two stores it pays for itself.”
So why are people so hesitant to invest in analysts, predictive analytics, and information systems to support better real estate planning and site selection?
I’ve been asking this question for nearly 20 years, and I think I’m finally beginning to see the answer.
It’s about measurement.
Here’s how the argument goes:
Summary (simplified): If we invest in better research, we can use a predictive model to estimate sales performance for proposed sites. If we know the population and income of the trade area, the distance to our competitors, and the quality of the site, we can use the weighting factors in the model to calculate the sales potential. We’ve heard that it’s possible to have a model where the estimate will be +/- 20% reliable at least 80% of the time. In the past we have only been +/- 20% correct 65% of the time.
Therefore, the avoidance of bad stores alone will more than cover the investment in people and tools to build and use the predictive model. Continue reading