What strikes me about the results shown in the chart above is that nothing stands out. That is, there is no one area where a large majority sees the utility of location data. Makes me wonder whether this is technology in search of an application. Remember — this is not new data. We’ve had the ability to geofence for quite some time.

Understanding the nuances of local demand is useful. And some retailers don’t have a clue, even about simple differences. Here’s looking at you, J.Crew, with your recent winter sweater display in Arizona. Come down and see how many people wear sweaters here, even in the depths of December and January!

I’m most surprised in these results at how low correlating traffic analysis with store performance ranked. Having seen so many deployments of analytics technology in retailers where this was of prime concern makes me wonder if the value of the insight was not as high as originally perceived.

I still believe the insight on what your customers are doing in the store and what products they gravitate to before making a purchase is invaluable. Perhaps what this really tells us is that retailers still don’t know how best to use this technology. Since nothing really stands out as the killer app in the chart, it may be that retailers are still viewing location analytics as a Swiss army knife of data insights, personalization enabler, etc. without a clear value winner.

There are two elements to consider with location data. One is the ability to trigger advertising based on location. The other is creating targetable segments based on how an individual user habitually navigates the real world. I have seen a lot of success with the later, providing accurate segment classifications.

Location data is one way to target people at relevant moments. It has great promise, especially for retailers but ultimately, it needs to be proven out via AB testing. I am optimistic such testing will support its value based on a series of experiments I have an indirect relationship with at the Mobile Marketing Association.

Location analytics is a big, “complicated” space. For retailers and others, it may be overwhelming to find out and understand all this new data — and data-driven answers always beget more questions. Could it be that it’s too massive to wrap your arms around? I believe there is a need to “productize” location analytics against key questions and objectives within the retail industry vs talk on a macro level. When asked are you interested in where best to add a store, how to improve merchandising or how to gauge store performance, all retailers would likely say yes. This solves real problems vs offering up data.

Every retailer across fashion, CPG, food, you name it, is looking for ways to be even more relevant for their customers. If you’re going to protect your market share and grow incremental sales you have to be able to understand who your customers are and what they like. From my years on the other side of the aisle as a retailer (full disclosure: today I am head of retail at Esri and we sponsored the RSR report noted above), the way you do that successfully is by being more precise in your assortments and more relevant in your offers. Location technology enables those specific capabilities. For example, no retailer for a moment would consider making a real estate decision without understanding the nuances and dynamics of the market are investing in. Increasingly, these same techniques of market optimization are being applied to assortment planning and target marketing. In my retail career I found that making local adjustments to facings in as simple a category as paper towels realized a 9% increase in sales. It came… Read more »