Let’s say you’re walking past a store and your eye catches a fancy gadget that you decide you must have, but you’re in a tearing hurry. So what do you do? You take a picture of the doodad with your smartphone, and hey presto, the store’s technology enables you to get it delivered to your doorstep in a couple of days.
In that time, perhaps the e-store linked to that shop where the product caught your eye sent you information about a small selection of similar gadgets with various features, form factors, price tags, colours and so on. Perhaps it told you that several friends of yours had liked a similar gadget, that it has been reviewed highly by certain taste-makers you follow, and that the gadget features are in keeping with your known tastes. They made the choice simple for you, and you said yes to the one you wanted in the few minutes you were waiting for that important meeting you were on your way to.
If you wish, of course you could go further – recommend it to friends on your favourite social media site, and maybe even get a discount for the effort or earn points that could be redeemed against your next purchase at that store.
The scenario is startlingly close to reality, as a combination of big data analytics and cloud computing apps on our handsets and machine learning on the backend computing and storage infrastructure is enabling the delivery of real personalized choices to consumers.
Big data analytics has become so sophisticated that mere recommendations don’t cut it anymore. Instead, ecommerce businesses can learn about your genuine tastes, without impinging on your privacy, by harnessing an aggregate of hundreds of millions of ‘anonymised’ data points. They combine this with data on your known influences, your past behavior and your current context to deliver highly relevant choices. This kind of ‘guided choice’ is increasingly replacing search, which throws up lots of information, but does not necessarily solve your problem of sorting through the various choices on offer.