Many fear increased ‘profiling’ – viewing it as both new and inherently bad. Done correctly, profiling simply restores to the retail experience what we always used to have – an attentive assistant who uses their eyes and ears to make sensible suggestions (to the customer and the store manager).
Lets take an example:-
Old World:- An assistant observes a man in his mid-40’s with big feet walking into his shoe shop. The man is wearing expensive, but worn, shoes and look like he’s in a hurry. The assistant can tell the man is a conservative dresser based on his attire. He asks whether he’d like the same style again and hazards a guess in size 10. The man nods and pays on account.
New World:- A man enters a store. He’s told his now 2 years old shoes are available in store if he’d like to replace them. He clicks yes and collects them from the counter (he’s already paid).
His app knows this based on:-
1) Reading past electronic receipts from his inbox – which tell the purchase date, model/style and shoe size (based on this, for how long will retailers continue to deliver plain text receipts?).
2) Observing the fact that past purchase behaviour has been tightly coupled to ‘conservative’ repeat purchases of very similar styles (e.g. 5 pairs of beige trousers and 10 blue shirts).
3) Observing past in-store behaviours. Low browse times, limited shopping around etc… + knowing the man has been walking quickly (and not in a relaxed manner, which correlated tightly with his more ‘adventurous’ shopping trips).
4) Knowing that the man is in the ‘formal’ shoe section of the store and not currently considering sandals / trainers etc… (which he also owns).
5) Leveraging stored payment details, or auto-provisioned store credit based on soft or hard credit profiling (NB: in the X.com view of the world attributes like shoes size etc… might also be stored just like preferred delivery address etc…)
In all of the above, very little data is revealed that could not be observed by a human shop assistant. From the pace of your gait as you enter a store, to the value of the clothes on your back – all these can be gauged currently – and bringing back the ability to do so, simply restores to the shopping experience what was lost through (a lack of) technology as we took people (and cost) out of the retail experience.
It should be noted that in the above scenario, data on shoes size, past purchases, gait etc… can all be withheld from the merchant and only used to ‘filter’ suggestions ‘broadcast’ by the merchant to the consumers client side app. For example, the store can say it has (UK) size 9,10 and 11 shoes available, and the store need only know anything about the shoppers shoe size if the app spots a match and the consumer decides to purchase. While there are many factors that will drive who’s app you let guide you around a store, it will be interesting to see to what degree data privacy concerns drive peoples’ choice of ‘shopping partner’.