Retailers have always known the value of where. After all, the first question any retailer must answer correctly is where to open a storefront. Get it right and customers stroll in and spend money; get it wrong and they pass on by. Using location-based data, retailers are slowly edging their way to a deeper understanding of the value of where.
According to Deloitte, 80% of all information being collected today includes geographic data. ABI Research predicts by 2017, consumer-focused, location-based information will be a $9 billion market, with $5 billion of that just for indoor data, gathered while shoppers are already inside a store.
Smartphones, of course, generate and consume the location data that interests retailers most. Companies such as Macy’s and Harrods create mobile apps for shopping online, but more importantly to entice customers with in-store offers, helping to nudge shoppers across the boundary from considering something to buying it. For example, if a customer scans an item to get more details about it on his or her smartphone, the app connects to a backend analytics application that can offer discounts or pair the item with another compatible product for an even better deal.
Advocates for location-based data retailing even see it as a potential boon for stores that have been suffering from the boom in online shopping. Bookstores, for example, could use an app to offer limited-time, in-store only purchases for customers who walk in.
For me, though, one of the more exciting possibilities for retailers will be exploring the value of location-based data hidden in directions from mapping services. Researchers at Google have been looking into “hyper-local” queries from people needing to get from place A to place B. They work under the reasonable assumption that place B is “interesting,” at the very least to people who start in place A.
As you can imagine, Google and other online mapping services have enormous troves of data from users asking for directions to many interesting place Bs. By learning what makes the Bs of the world attractive to consumers (Is it a restaurant? A hotel? A sports stadium? Maybe even a bookstore?), retailers can match their compatible or contrasting storefronts to those locations.
In addition to understanding why B is an interesting place, retailers might want to analyze all the As that consumers start from. That is, perhaps there are enough people in the neighborhood around place A to warrant locating a place B closer to that place A. Savvy retail business analysts can have a field day with location-based data like this.
There are privacy issues entwined with location-based data. But retailers’ opt-in apps and anonymous mapping data from Google and other services mitigate those problems to a great extent. Ultimately, though, consumers will benefit with better service and product availability when retailers not only know more precisely what people will purchase and how much they will spend for it, but where they will buy it as well.
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