How Retailers are Harnessing Machine Learning and Predictive Analytics

Retailers are saying goodbye to intuitive guessing based on old-school data-gathering methods to convert customers. Welcome to the age of machine learning in retail, where online and brick-and-mortar business leaders can not only automate operational tasks and align product description with retail sales strategies, they now can accurately predict consumer behavior and personalize customer experience better than ever before.

If certain retailers have not considered implementing machine learning into operations, it’s high time that they do. A recent study from Infosys of 1,000 consumers and 50 retailers across the U.S. reveals that consumers now expect personalization, saying that 59% of shoppers who have experienced personalization believe it has a noticeable effect on their purchasing decisions.

Machine learning has surpassed the personalization legwork established by retail giants

Machine learning as applied to retail isn’t anything new, especially in larger establishments, Way back in 2014, Amazon developed predictive stocking, and Walmart has been using artificial intelligence to restock their stores’ inventory in the wake of hurricanes. More recently, Etsy started using machine learning to process nuanced product recommendations to predict customer behavior to an even higher degree than they already are.

For years, many companies have been using more “catch-all” personalization tactics like including a customer’s name in the subject line of an email, but, due to the implementation of machine learning, personalization tactics have grown exponentially from this level of “impersonal.”

As SDC contributor Mohammad Ali states: “Artificial intelligence is undoubtedly transforming the way ecommerce businesses work by offering tailored solutions, personalized customer experience, evolving the sales process smartly to convert leads into paying customers. It is making incredible changes to the way retailers and ecommerce brands deal with their customers, getting an easy access to big data and harnessing their marketing and sales team skills for business growth.”

How machine learning is actually humanizing the retail shopping experience

Machine learning is simply a method of data analysis that allows a computer to self-learn without a human programming its every move. And within the vast amounts of data processed, retailers are able to hone in on who their customers are and, more importantly, what their needs are in a precise moment.

With machine learning, “Retailers can now predict buying behavior with a greater degree of accuracy by understanding what products their shoppers are engaging with and how, whether they are trying on a product or simply picking it up,” writes Talitha Loftus of RetailNext.net. “Machine learning principles can identify human actions of both shoppers and employees, including crouching, bending, reaching overhead, and the like, all the way down to analyzing what aids are being used – carts, bags, brooms, mops, and more.”

These days, almost everyone is armed with some sort of smart device before and during shopping. You can order anything you want through a simple voice command through Amazon Echo. Your previous orders on any e-commerce website are tracked and analyzed. And imagine someday that you walk by a brick-and-mortar retail store you’ve never been in, and your smart phone alerts you to a sale that appeals to you that is going on right inside that store. Those days are not far off.

Where and how to start to implementing machine learning personalization?

It might seem daunting to blend machine learning into a human-driven retail operation to enhance personalization and to better predict buyer behavior. But companies like WorkFusion are out there to help. WorkFusion is an automation platform that offers a number of services, using smart process automation and robot process automation, where essentially an algorithm can understand relevant data points contextually (like a human!). These automations can enrich existing human workforces to improve the retail customer experience in many ways including:

• Meaningful website content
• Automated product categorization that aligns original product data with the ways the retailer himself would actually sell the product
• Improved data quality, speed and return on investment

According to the Harvard Business Review, the age of responsive retail is over — we have entered the era of predictive commerce: “It’s time for retailers to help people find products in their precise moment of need — and perhaps before they even perceive that need — whether or not they’re logged in or ready to click a ‘buy’ button on a screen. This shift will require designing experiences that merge an understanding of human behavior with large-scale automation and data integration.”

Retailers who are open to harnessing these tools effectively will be able to differentiate themselves by creating amazing personalized customer experiences. The rest, stuck in a bygone era, will literally be collecting dust.