Radio Shack’s Empty Stores Are Risky Business For Other Retailers

Many retailers – from Sprint and GameStop to TruFoods – are hoping to find success in the shuttered RadioShack locations. While these vacant storefronts seem to be in prime locations, it does not mean they are ideal for any business.

I worry about those looking to profit from this vacant-RadioShack-land-rush. In the interviews I’ve read, the analysts and business owners are still evaluating locations “the old fashioned way” – with spreadsheets filled with outdated metrics and preconceived notions. Those notions represent a very limited view of the factors that might affect the success or failure of a particular brick-and-mortar location. For instance – how visible is the storefront to traffic? How accessible? What times of day does traffic peak? How much disposable income do nearby residents have?  While those few factors certainly do play a part in site selection, there are literally 100’s to 1000’s more that must be taken into account.

I ask myself – how do the analysts link these factors to actual success or failure of a business? This is where most analyses “hit the wall”. Lots of time and money is spent collecting piles of data, but then the analysts have no way to relate this input data to the success or failure of a particular business. Without scientific rigor and significant amounts of data, there is no simple way to determine the “factors of a successful location”. And these factors aren’t universal across all businesses. Some data is extremely predictive for one type of business, but useless for others. For example, the side of the street a business is located on probably doesn’t matter to a furniture store (people will drive very far to buy furniture and don’t care about the last left turn), but could matter quite a bit for a drive-through coffee hut. The annual spending by local households at steakhouses probably doesn’t matter to the drive-through coffee hut, but does matter to an Irish pub. So, even when armed with a huge pile of data, it is often unclear which factors matter and which don’t.

This is the point at which “analysis paralysis” can set in. The data will be sliced and diced a myriad of ways, charts and graphs will be made, and no clear patterns will emerge to the human eye. Or, even worse, someone will convince themselves that they’re on the right track, and a $1,000,000 or $10,000,000 decision will be made. Businesses end up with locations that can fall far short of expectations and fail altogether. For instance, one of our associates – an owner of a restaurant chain – paid over $30,000 for a “professional analysis” only to end up with a location that produced 22% of expected sales. How can something so unscientific and error-prone not be frightening?

There’s a low risk, far more accurate alternative. I am happy for my part in providing that alternative. IdealSpot is now analyzing over 15,000 inputs for each and every location we evaluate. Not only do we account for the “traditional” factors that most analysts rely upon (drive times, disposable incomes, and other standard demographic and customer preference surveys), we also utilize social media trends and internet search patterns (and we’re adding more all the time). And that’s where we’re just getting started.

Our algorithm has no agenda, bias or prejudice; it uses science to model success.

“If you torture the data long enough, it will confess.”~ Ronald Coase

IdealSpot has developed a scientifically-vetted algorithm that mines through our extremely large set of data and identifies key patterns of success or failure. The algorithm acts without human bias; it starts from scratch, creates models unique to each business, and finds the relationships between location and business outcomes. Whereas an old-fashioned analysis might focus on traffic patterns or demographics since that “seemed to work well” in the past, our machine learning algorithm assigns no special importance to one type of data or another. All that matters is how useful each pattern has proven itself to be in predicting success or failure of client & competitor locations.  Not only does this system make site selection more efficient, it makes the entire process accurate, repeatable, and successful.

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