How Machine Learning Can Drive Retail Sales

In this post, ELEKS’ head of omnichannel solutions Pavlo Khliust explores how retail is using machine learning, artificial intelligence and other developments to drive sales.

As the shopping experience becomes more and more integrated, retailers tend to adopt an omnichannel sales approach. This means that a customer may seamlessly switch across the multitude of sales channels, shopping online from a desktop or mobile device, by telephone or in a bricks and mortar stores.

Sales Ex Machina

Not only does this allow customers to get the best out of their shopping, but it also provides retailers with an enormous amount of data generated by customers. This digital trail left by customer’s interactions with the retailer, both online and offline, provides marketers exabytes of data. Bluetooth beacons in-stores drove $4 billion in sales in 2015 alone.
Certainly, lots of precious insights can be found in this plethora of information. But crunching that is a tough row to hoe. And here’s where machine learning (ML) comes in.

This term is often used interchangeably with artificial intelligence (AI), yet they are not exactly the same. AI is, basically speaking, a machine capable of intelligent behaviour. ML is, as Stanford Dictionary puts it, “the science of getting computers to act without being explicitly programmed.” ML uses algorithms that learn from data to build predictive models that choose where to look for insights. This technology opens a great deal of opportunity for businesses.

Applications of ML are almost limitless when it comes to retail. Product pricing optimisation, sales and customer service forecasting, precise ad targeting, website content customisation, prospect segmentation — these are the most obvious examples of how ML can boost your sales and save your marketing budget.

As usual, numbers speak best for the success of ML in retail. Fifty-five percent of Amazon’s sales come from personal recommendations made by machine learning algorithms. Target Corporation achieved 15 to 30% growth in revenue with the help of machine learning predictive models. At least 40% companies surveyed by Accenture Institute for High Performance already use machine learning to improve their sales and marketing performance. And, frankly, I’m not too optimistic about the future of the remaining 60%.

Mine Your Own Data

Obviously, data-driven decisions have been defining the success of retailers long before AI and ML were even invented. Choosing the right mix of products based on customer demand, setting prices and offering discounts based on competitor policies — those things have always been about careful data analysis.

But the crucial factor to thrive in our “be quick or be dead” time is the speed at which you make decisions, as well as their quality. Businesses should not only look back, analysing the data obtained in the past. Cutting edge data processing happens in real-time and changes are being made on the fly.

Adaptive analytics, for instance, prevent customers from abandoning your website by sensing the first signs they might drop off and causing live chat assistance windows to pop-up. They are also good at upselling, showing customers the most relevant products based on their behaviour at that moment. As in many other cases, it’s not the size of your data that matters, but the way you use it — ”small data” improves your marketing ROI greatly.

The Power of Personalisation

Last year was hard on a number of American clothing retailers. True Religion was listed among those facing considerable risk of failure in 2016. This denim brand, however, didn’t go quietly into the night. In order to boost sales, the company’s marketers decided to harness the power of artificial intelligence. True Religion is now seeking a new, highly personalised approach to its customers with the help of Einstein, an AI tool from Salesforce. One hundred and forty-five brands that used Einstein-powered tools in the beta-period saw 7–16% revenue growth per visitor.

This story is as old as the world. Its moral is quite Darwinian: One has to evolve in order to survive. And the latest evolutionary adaptation of retailers is the ultimate personalisation.

So, how exactly does AI help retailers improve their services in this regard? A good example of the AI-powered personalised approach to customers can be found once you get in the Jackets and Vests section of the North Face website. Just click on “Shop with IBM Watson” and enjoy an experience that’s almost akin to a human sales associate helping you choose which jacket you need. The AI will ask where, when, and for what kind of activities you are going to use that jacket. It will then specify what styles, materials, and colours you prefer, and after a string of clarifying questions, you’ll get a jacket that perfectly matches both your aesthetic perceptions and practical needs.

Not only does this approach empower retailers with easily scalable personalisation tools, but it also helps the customer overcome the curse of our times: being overwhelmed by too many choices and information. Yes, more goods lead to more possibilities. But this, in turn, may lead to more confusion when a customer faces an endless grid of products on the website to choose from. Or — the other way around — to more satisfaction when a customer gets a friendly assistance that reduces his or her cognitive load by an order of magnitude.

Use the Momentum

Needless to say that all the above-mentioned techniques should be used wisely. Machine learning recommendations, iteratively feeding on data produced by the ML algorithm itself, could go too far from what a customer really wants and create an effect that might resemble Facebook’s echo chamber. Therefore we need to carefully supervise and constantly tweak these intelligent machines instead of being awestruck by their superhuman power.

The technology, after all, is not a monument but a momentum, and we need to use this momentum to our advantage. Combining artificial intelligence, machine learning, virtual reality, apps like coupon aggregators and deal finders with real human expertise will benefit both retailer and customer, making the world a better place to buy and sell.

Did you like this article? Let me know what you think and what applications of AI, ML, and adaptive analytics you use or plan to use in the future.

This story was originally published at http://www.insider-trends.com/