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The future of machine learning-powered retail

June 18, 2018

Much ink has been spilt recently over the waning of the retail industry. And the prognosis for retailers unwilling to unlock the advantages of big data does indeed seem bleak.

For retailers eager to incorporate machine learning into their organization, however, the human-machine alliance looks set to spark their industry’s renaissance.

Here, we’re looking at how machine learning will do this by redefining the role of consultancy and management and reshaping the scope for human innovation.

Machine learning and the (r)evolution of retail consulting

One inevitable consequence of machine learning-powered retail is that it will change what it means to be a consultant.

Coming up with insights relating to item potential or market trends, for example, will now be in the remit of machine learning algorithms rather than retail consultants. These insights, however, might not always be in harmony with an organization’s overall direction.

Let’s take an example. Drawing on machine learning insights, a fashion company identifies a clothing range that’s forecast to sell well. This is all well and good, but in choosing which range to stock, a consultant might decide that, this time, this clothing would be better replaced by one that’s more in line with the company’s image.

For retailers eager to incorporate machine learning into their organization, however, the human-machine alliance looks set to spark their industry’s renaissance.  Tweet This

Creating brand image is something the machine learning has not yet learned to quantify. Until it does, a consultant’s ability to identify such discontinuities between the algorithms’ insights and act on what their own gut feelings dictates will be more important than ever.

Merging machine learning and management

We’ve already published about how introducing machine learning to an organization demands a rethinking of its management structure. For companies that have enough data to sift through (and numbers to crunch) the quantitative groundwork the machine learning carries out frees up the people in an organization to do what they do best: understand those factors that haven’t yet been quantified, and innovate.

But if the machine learning isn’t being given the right set of factors to consider, its forecasts may well be misaligned with the company’s goals and brand. And so the manager’s role will be to determine which information to feed it to obtain the most effective insights.

Machine learning applications are meaningless without human understanding

Machine learning software works best when combined with human feedback. While the machine learning sifts through the data, it’s the executives in an organization that still drive innovation, come up with the strategies, and implement the ideas.

When humans and machines work in tandem, the organization stands to soar. But when machine analysis isn’t counterbalanced with human understanding, the results can be unfortunate.

Take the case of Target. Back in 2012, the retailer mailed out coupons relating to pregnancy items and addressed them to a teenage girl who had been secretly searching for them online.

Problematically, this girl had yet to disclose her pregnancy to her family. And Target, whose machine learning software had been trained analyse customer buying patterns, inadvertently beat her to the mark.

At the time, this story was taken up as a warning against the misuse of personal data. But apocryphal or not, this story is less about a failure of machine learning (which was actually doing its job perfectly) and more a case of human oversight taking its eye off the… well, off the target.

Had the idea of sensitive information been quantified, Target wouldn’t have ended up straying so wide of the mark.

The human-machine alliance marks the future of retail

As the coming years will show, the willingness to innovate with AI will be the determining factor of a company’s survival.

The tide is turning in the retail industry. And it’s only by investing in the incrementally more accurate forecasts and results that machine learning brings that retailers can be sure to stay afloat.

About the author

Alexander Meddings is Evo’s content expert on artificial intelligence, machine learning, and related topics.

He is an experienced journalist who covers branding, social media, marketing, and technology, with degrees from the University of Exeter and University of Oxford.

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