Imagine wanting a cup of coffee and suddenly finding it before you, freshly prepared to your exacting standards.
This isn’t a fantasy from the mind of JK Rowling. In the not-so-distant future, this will be reality for Muggles, too.
In fact, thanks to a surge in consumer data, brands and marketers can already make better inferences about consumer wants and needs, but as AI and machine learning are more deftly integrated, insights will only get better, as will the ability to anticipate consumer needs – and to even make decisions on behalf of consumers without any input from them whatsoever. Like, say, ordering a cup of coffee.
As it stands, digital enables brands to customize offers for specific users rather than provide generic solutions. For example, a hotel can use data to greet a guest by name and have a room ready with either a soft or a firm pillow, depending on that guest’s preference, said Aaron Shapiro, CEO of digital agency Huge.
“Technology exists today…to identify small ways that companies can do a better job of servicing users and start to implement and test and evolve from there,” Shapiro said. “At the end of the day, companies use technology to help users solve problems. Users will love it and have a better relationship and drive more business performance, but now we have amazing tools in the form of cloud computing, machine learning and AI to do a better job to meet user needs.”
But we haven’t quite worked out all the kinks yet.
Case in point: There’s the oft-cited example of Target, which mines data in part to assign pregnancy prediction scores to its customers, and the poor Minneapolis-area man who reportedly learned this the hard way, complaining the retailer was sending his teenage daughter coupons for baby clothes and cribs only to learn later she was, in fact, pregnant.
The Target-knew-his-daughter-was-pregnant-before-he-did example illustrates how quickly acting on consumer insights can shift from useful to creepy and, as it happens, “creepy” is a word we hear often in conversations about data in marketing.
However, as RP Kumar, executive vice president and director of international research, insights and planning at marketing agency Ketchum, observed, younger generations seem less concerned by privacy as they grew up in a digital era, well aware Google and the like are keeping tabs on them, so this will perhaps be less of an issue moving forward. Time will tell.
In the meantime, with just a bit of analysis, marketers can make useful inferences like, say, consumer behavior changes at different times of day, said John Stewart, senior vice president of strategy and analysis at marketing and technology agency DigitasLBi. In other words, a consumer is likely doing different things at 10:00 AM on a Tuesday than 7:00 PM on a Saturday night.
The trigger before the trigger
And that means brands looking for clever ways to use data and to connect at the right moment — preferably without crossing any lines — should look for what Stewart called ‘the trigger before the trigger’. For instance, a brand that sells appliances might think, “Consumers who are moving are likely to be in the market for new appliances.” And that may very well be true. But targeting them when they are moving may be too late. Instead, Stewart said to look at what might precipitate a move, like a career change, which would be reflected in LinkedIn data.
“Forty per cent of career changers are in the market for a new appliance or insurance or cable, so we’re moving now to a market of data,” he said. “Instead of what was a trigger beforehand, now it’s the trigger before and the trigger before, so you can hone that and give the right message.”
But it doesn’t just have to be these anticipatory triggers. It can also include a little good old-fashioned deduction based on the data a brand has on hand.