Artificial Intelligence and e-commerce: Who wins

Utter the words artificial intelligence, and the robots and creatures of science fiction movies are usually the first things that spring to mind. But more and more the workings of artificial intelligence and machine learning are infiltrating our everyday lives – even if we’re not always 100% aware. Take shopping online: software can now detect subtle patterns in online behaviour and use them to present shoppers and users with what they want, before they even know they want it. This begs the question: who wins more when it comes to AI? Retailers or consumers?

Speaking at Future Forum Taiwan 2015,  Yuki Watanabe, founder and CEO of Sensy, believes AI can first and foremost help consumers find what they really desire, faster. His app, Sensy, allows consumers to select objects they like and dislike, before providing personalized recommendation on products that could pique their interest and prompt a purchase, with the help of memory and learning capabilities. The benefits of this go without saying. For an online shopper buying shoes, for example, this means quicker shopping with more style options. Not just any style, styles a smart app has figured out they will, most probably, like a lot.

What about the retail side of things? Sensy works with 2000 businesses – proof in the pudding that getting a better read on consumers also has revenue implications and is seriously beneficial for businesses that live online. In a world where people are becoming increasingly mobile first, and in some cases using only a smartphone to go online, it is becoming more crucial than ever for retailers to get a grasp on the masses of data consumers are sending through their mobile phones day in, day out – and more importantly, to use it to generate actual sales and results for their business.

But wowing shoppers with (very) well-educated guesses about a shoe they might love is surely not enough to sustain a business in the long-term. Masaya Mori, Global Head of the Rakuten Institute of Technology, believes that all of this data, if used in the right way, can not only tell businesses all they need to know about their customers likes and dislikes in that moment – but can really come into its own when businesses are planning for the development of future products and services. AI can help businesses predict their next hit product, improve what’s on offer based on previous successes, and create designs that cater directly to consumer’s future needs.

But no discussion about Artificial Intelligence can be had with the age-old question: can a smart app really mimic the way our brains work? And what does that mean for the ‘brains’ who were responsible for understanding a business’ consumer before?

Watanabe urges caution when it comes to overstating the case, and reminds that there is still a limit to the communication capabilities of AI – it still can’t clearly distinguish dialogue and provide response, nor can it complete creative and challenging tasks.

However, AI is now able to go beyond data analysis to generate unique data where gaps exists in database sources.  That means, in the very near future, even if you don’t have enough data for analysis or prediction of consumer behavior and business operations, AI applications can generate the missing data (more precisely infer data), to fill these gaps.