Personalization lies right at the heart of every good retail app. Any old retailer can create a basic mobile app (and they frequently do) but not every app is going to make shopping better, faster and cheaper. The apps that do it well are powered by big data platforms with the scale and reliability to collect millions of discrete data points about customers every day. When historic data like purchase history is combined with live contextual data like location, you can anticipate what people are most likely to buy and make relevant suggestions when they’re most likely to buy it.
You can do really cool stuff with real-time data
Location: If Tim’s near the Coffee Shack at 7:30am we can send him a breakfast meal offer, encouraging him to spend more than he would on his usual trim latte alone.
Weather: if it’s warm out we’re going to have better luck getting Tim to detour for icecream than pies. Based on transactional data we know Tim likes both, but because we’re also collecting up to date weather data and it’s a sweltering Sunday afternoon, we can send the more relevant offer.
Nearby events: we know Tim frequents local theater performances, so when he’s in the area on the next production’s opening night it makes sense to send an offer enticing him in for pre-show drinks and nibbles.
Beacon proximity: Tim’s spent a lot of time in store this week and most of that has been spent looking at fishing supplies, although he still hasn’t bought anything. Maybe he just needs a well-timed promotion to nudge him in the right direction next time he’s in the sporting aisle…
Social activity: Tim’s liked and retweeted a bunch of our promotional content so we know he’s engaged and will be receptive when we send him an offer. He’s also tagged his friends and shared a few posts with his network, so a ‘bring a friend’ promotion is going to be particularly appropriate.
Traffic: Tim’s bus is delayed due to an accident on the motorway so he obviously has some free time right now (plus he’s probably in need of a mood improver) so a coffee deal might go over quite well. Or maybe we could entice him in with something a little stronger…
Voucher redemptions: If Tim consistently redeems entertainment offers at the beach on holiday weekends and there’s one coming up, then it makes sense to send him an offer he can redeem where he is, rather than one that needs to be used back in the city.
In-app behavior: Tim adds offers that include coffee, ice cream and sports gear to his favorites. He usually redeems the coffee and ice cream offers(so we know he really likes his treats) but has yet to use the others. If we want to get him to finally buy that fishing rod we’re promoting, perhaps we can sweeten the deal with a free ice cream.
And that's just scratching the surface; with Big Brother watching and thousands of live data points to collect there's limitless potential for personalization. In short, incorporating real-time data makes your offers more useful – so they're more likely to get used.