Next Time You Wonder What Your Customer Is Thinking, Ask Your Computer


When was the last time you asked your computer something? There’s Siri, Google, and Cortana of course, but these systems, clever as they may be, are the thin end of a newly emerging wedge of remarkable new approaches to computer learning and marketing.

If you need proof that we are entering a new era of machine learning and artificial intelligence (AI) you need to look no further than Google’s DeepMind project. Early this year DeepMind, Google’s AI computer, developed initially in London, challenged and beat South Korean Grandmaster Lee Sedol at the ancient game of Go.

Why this challenge is so important requires you to think back to the Deep Blue computer, which finally beat Gary Kasparov at chess in the 1990s. Deep Blue had it easy. Chess is a game that relies on probability to win. If you know the odds of winning based on each particular move then you can win by focusing on those moves that make you more likely to win.

Deep Blue considered every move possible and made the move that would, on the balance of probability, propel the computer towards victory. This required huge data sets and the system would go through them at high speed – it could process 200 million positions every second.

DeepMind is different. Go is not a game that relies on probabilities, but on strategy, so a computer that can play Go has to learn. The computer played itself thousands of times and learned how to win, developing its own approaches and strategies to achieve victory. Which it did, beating Lee Sedol 4-1.

We are only just considering possible real-world applications for DeepMind. A recent project at Moorfields Eye Hospital in East London used the computer to look at thousands of retina scans, in the hope it can learn to spot the early signs of eye diseases like those caused by diabetes or macular degeneration.

In marketing, there are highly complex barriers to overcome, such as language, relevance, and context. Data sets used by marketers to target consumers can be superb. At the moment we are let down by the executions and ways of using this data.

For example, everyone has shopped and bought something only to find that ads for that product appear everywhere. Not only is this annoying, but it’s a waste. It’s not the fault of the data, but of how the data is applied.

Imagine a system where consumer data could be used as a starting point to interact with consumers, discover more about them and help them. We’re practically there.

The DMA’s customer engagement research, published in June, found that half (48%) of respondents expressed an interest in artificial intelligence approaches. Examples given included chatbots, which Facebook is actively developing as the next step in engagement. The research suggests this approach could work well.

Perhaps unsurprisingly, younger consumers were more likely to be interested, up to 79% for the 16-24 age-group and 76% for those aged 25-34, and still as high as 62% of those aged 35-44.

These are technologies under development now.

Looking to tomorrow, more than half (54%) of the so-called ‘Millennial’ age groups (representing 28% of the total sample) were interested in a service that detected how they were feeling and sent them surprise offers/deals based on their mood.

That’s possible too.

If the whole idea of AI improving the way your brand deals with consumers baffles you a little, then imagine a clothing retailer. An AI assistant might know your previous purchases, and so make suggestions based on colour or style. But what if some of those were presents? You could tell the assistant – it learns about you as you interact with it. It could begin to know your customers brilliantly well, and the friends they buy for too, predict what they would like and what they wouldn’t.

But the best part is the interaction – the data set grows, as it would without the AI, but it also learns about your preferences, your likes, and dislikes.

This scenario would use the same data sets that brands currently use to set those ads that follow you around. AI approaches for marketing would not only be smarter, but there is a real consumer appetite for it. The next time you wonder what your customer is thinking, ask your computer.