The Future of Retail: Data Science or Simpler Business Models?
This year, as part of my work with companies given grants from the National Science Foundation (NSF), I advised a startup in the Retail Industry. With online customer sales hitting a 10X growth rate compared to brick-and-mortar stores, one would think that there’s not much innovation going on to make the off-line shopping experience more profitable. That’s not the case.
Revenues for in-store shopping continue to be a majority of retail dollars spent by customers. As mentioned in a recent Forbes article, stores can’t just price-match with online competition to keep up with their growth. They need data-driven marketing insights to empower employees and improve the customer experience. They can treat shoppers as individuals with unique buying behaviors and preferences.
The challenge is to leverage the data of the shopping experience to drive revenues and positively impact customer loyalty. And that is what the NSF company, Celect, is attempting. Their technology, based on MIT research, brings data science to the retail environment through real-time insights to sales associates. This “Netflix algorithm” type of support during the customer’s shopping experience works through an iPad viewable solution that tracks in-store customer browsing, purchase behavior, and provides personalized customer recommendations. The data is hidden from the shopper, but the associate shares the findings. On the business operations side, the Celect solution offers intelligence for monitoring sales associates, improved product assortments, and optimized inventory decisions.
Some call this type of technology the “Amazon-ization” of the retail shopping experience. But, can it really work? Can a salesperson really upsell items listed on an iPad as they assist the buyer trying clothes on? Recently, a beta client experienced a customer conversion rate of 22%; a nearly 2X increase over the incumbent conversion rate. This upsell activity was driven by purchases of recommended items.
Consider these other approaches that compete with growth trends in online shopping.
• Automation comes to retail and takes away the need for salesperson interaction. Check out this video of the company Hointer, as they roll out their business model comprised of showroom, smartphone app, scanner, and i
tem retrieval system. Is retail ready to embrace the vending machine approach to shopping?
• One new shopping medium is popularized by “haulers”, or shoppers who “haul” or buy a load of items and then showcase them in a personalized video. There are over 500 million views of these shoppers on YouTube. They shop and advertise what they find in all kinds of stores. Viewers are influenced to buy those items.
• Then there’s Uptown Cheapskate, a thrift shop franchise concept targeting a $2.8 billion industry: apparel resale. No algorithms, no high-tech, just pre-owned clothing purchased for cash and resold to savvy shoppers.
These three examples are an attempt to make the off-line shopping experience more profitable. The last two have been very successful at finding hidden revenue using unconventional methods. They prove that simpler and more profitable does not need to include new technology. So, the future of retail may include real-time insights for sales associates or something different altogether.
“The future is not what it used to be.”
This mostly unattributed quotation shows that it’s tough to predict the future.
After World War II, our nation’s most influential scientist wrote about future technologies. His desire was to see applications for warfare transitioned into knowledge and power used for the benefit of mankind. Notable predictions included hypertext, personal computers, the World Wide Web, speech recognition, and online encyclopedias such as Wikipedia: "Wholly new forms of encyclopedias will appear, ready-made with a mesh of associative trails running through them, ready to be dropped into the memex and there amplified."
In his attempt to predict the Future of Retail, Vannevar Bush, spoke of processes we take for granted now. It’s almost painful to read about his vision of what would become a credit-card transaction.
Take the prosaic problem of the great department store. Every time a charge sale is made, there are a number of things to be done. The inventory needs to be revised, the salesman needs to be given credit for the sale, the general accounts need an entry, and, most important, the customer needs to be charged. A central records device has been developed in which much of this work is done conveniently. The salesman places on a stand the customer's identification card, his own card, and the card taken from the article sold—all punched cards. When he pulls a lever, contacts are made through the holes, machinery at a central point makes the necessary computations and entries, and the proper receipt is printed for the salesman to pass to the customer.
But there may be ten thousand charge customers doing business with the store... Now rapid selection can slide just the proper card into position in an instant or two, and return it afterward. Another difficulty occurs, however. Existing totals could then be read by photocell, and the new total entered by an electron beam.
The cards may be in miniature, so that they occupy little space. They must move quickly. They need not be transferred far, but merely into position so that the photocell and recorder can operate on them. Positional dots can enter the data. At the end of the month a machine can readily be made to read these and to print an ordinary bill.
There’s a lot that goes into the systems we use today for easy transaction commerce. The purpose is to make things simpler. It will be interesting to see what can increase retail revenue – more technology, new business models, or something we haven’t thought of yet. Until then, be on the lookout for iPad-wielding sales associates.