Summary: Thanks to the IOT (internet of things) an internet-like experience of recommendations and awareness of your preferences is coming to the brick and mortar store near you.
You’ve probably noticed the huge difference in the tone of the conversation between data scientists and the general public over the issue of privacy and personalization. The professional community is largely quiet but for the public you’d think we were developing bionic eyeballs tracking their most minute and private habits.
In my house my wife is always complaining that I can’t remember how many sweeteners she takes in her tea; who her favorite actors are, or whether she liked that Indian restaurant we visited last year enough to want to go back. But if a web site shows her a picture of something she browsed yesterday, or if the recommended books and movies on Amazon are a little too on target she’s the first one to raise the hue and cry that her privacy is being violated. My failing to remember – bad. Their being helpful by remembering or recommending – also bad???
This is beginning to look like a real Catch 22. Behaviors we wish for at home are suddenly evil if a web site does an even better job than your spouse at remembering your likes and dislikes.
Personally I think site personalization is a real blessing. I don’t really want to see ads for rock climbing walls or baby diapers. I’m not in that market so not being exposed to a random untargeted bunch of ads (think your Sunday paper – what’s a Sunday paper you say?) is all for the good.
Well web sites are one thing but these days with the emerging IOT our brick and mortar stores are gearing up to behave more like a web site and less like a random walk up one aisle and down another. Here’s a brief update on who’s doing what in retail IOT. I’m sure there are many providers I’ve missed and can’t say if these folks are good or bad at what they do but my hat’s off to them for trying something new that might make my life better even if my wife would find it a little spooky.
In retail Heat Maps (which products get picked up more often than others) and Flow Charts (how customers navigated the store) are all the rage. Sensors also allow retailers to offer coupons over your smart phone that are tailored to your shopping pattern. And by moving desirable merchandise with long linger times to better locations, frequently to deeper in the store, they can achieve that same ‘stickiness’ we associate with web sites to make us stay a little longer. Where exactly are the customers going in the store, where do they pause and ponder, and how can the retailer use this information to revise the store layout, the merchandise displays, pricing, or anything else to squeeze out another dollar.
The specifics of sensors and strategies differ from one vendor to another and in this early stage of adoption it’s fair to say that we’re waiting for the market to tell us which are most successful. Some use your cell phone to triangulate your position, some use cameras, radio beacons, or even more exotic sensor types. This is a good thing since all this experimentation will tell us what’s worth the investment and what’s not. Any number of major retailers are running experiments. To name just a few:
Nordstrom – Euclid Analytics
Macy’s – Shopkick
Timberland and Kenneth Cole -Swirl Networks
Goldman’s Dept. Stores - RetailNext
The Future of Privacy Forum, a Washington, D.C., think tank, estimates that about 1,000 retailers are testing some sort of sensor strategy.
Swarm Solutions says 6,000 retailers have installed its door sensors to compare foot traffic with transactions.
Others working with Wi-Fi triangulation include Ekahau, Wifislam, and Prism Skylabs. Apple’s iBeacon technology probably belongs in this group as well.
Blinksight and Insiteo are working with radio beacons.
Bytelight, Aisle411, Everyfit, and PointInside are all working with other sensor types including embedded floor sensors and even LED lights.
These 15 innovators are probably only the tip of the iceberg. This is one of those ‘stay tuned for results’ stories. The results aren’t in but there are lots of horses in the race. Meanwhile, I’m still looking for the sensors I can install at home that will make my wife think I am a better husband.
Bill Vorhies, President & Chief Data Scientist – Data-Magnum - © 2014, all rights reserved.
About the author: Bill Vorhies is President & Chief Data Scientist of Data-Magnum and has practiced as a data scientist and commercial predictive modeler since 2001. He can be reached at:
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