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March of the Retail Robots Smart machines will upgrade the in-store experience, as well as the business model

April 7, 2016

by: Martin Vilaboy

[Excerpt from Inside Outdoor Spring 2016]

 

 

The Space Genius also provides retailers with an interactive 3D map of their store, precisely depicting each product exactly as it is displayed on the shelf. This realistic, virtual store can either be displayed on the company’s Web site for consumer use and shopping, or toured remotely by retail executives at headquarters.

 

“With one click, customers can virtually navigate through any scanned store anywhere in the world and view products on the shelf exactly as they are,” said the company. “As shoppers tour the aisles, they can pull products off the shelf, spin them around to read more product information and add them to a shopping cart to be shipped or delivered by local courier.”

Included with the Space Genius is a 3D planogram application. “Unlike traditional methods of creating planograms, which are based primarily on static, theoretical inputs, the 4D Space Genius intelligently generates dynamic ‘realograms’ based on actual, scanned data,” says 4D.

Then there’s Pepper, a humanoid robot that Pizza Hut expects to have in its restaurants in Singapore by the end of this year. A joint effort with MasterCard and created by Softbank Robotic, Pepper not only will take orders, provide product information and facilitate payment, it will also be able to access customer and sales information in order to make personalized recommendations and offers. And at the Carnegie Mellon University store, visitors will find AndyVision, an autonomous robot that fuses image-processing, machine-learning algorithms, a database of images of the store’s products, a basic map of the store’s layout and navigation sensors to take thorough inventories and tell staff when an item is running low in stock or merchandise is out of place.

“The idea for AndyVision was born out of me being a shopper. I go to a lot of stores and I find it very difficult to find the items I want, and sometimes I leave when I don’t find what I want,” said Priya Narasimhanm, head of the Intel Science and Technology Center for Embedded Computing at CMU.

Best Buy, meanwhile, has begun using Chloe, a robot that retrieves products that customers request from a kiosk, and Target recently began a trial of Tally, a robot that travels through aisles and takes inventory.

 

RELATED: FloorSpace: An Upside to Markdowns

Some pretty smart machines are being deployed in the outdoor market, as well. Although created largely to enhance e-commerce customer service, The North Face has been working with one of the most intelligent artificial machines, partnering in the development of the first mobile app experience to put Watson, the powerful artificial intelligence computer owned by IBM, to use in a retail environment. The application was designed to help online shoppers pick the perfect jacket for their respective wants and needs without having to bungle through product pages on the small mobile screen. Watson asks questions such as where, when and during which activities the jacket will be used, and then based on the feedback, crunches data and makes a recommendation. During initial trials, says The North Face, users who provided feedback rated the experience a 2.5 out of 3, and 75 percent said they’d use it again.

Osprey Packs likewise recently introduced its Packfinder digital tool which “allows customers to take a step-by-step journey through the decision-making process of finding the perfect pack for the intended activity,” announced the company. Customers answer several simple questions such as the primary activity the pack will be used for, trip length, desired features and price range. Packfinder then determines the best Osprey product solution and creates a report card explaining the selection.

Again, both Packfinder and the TNFWatson tool were created for online sales, but it’s naïve not to notice how the qualification questions and recommendation processes sound a lot like something most outdoor specialty sales staff hear on their first day of training.

It’s also no secret how retailers need to reduce the cost of running physical locations in order to counter a declining percentage of overall sales. It’s pretty safe to assume, after all, that dollars will continue to shift to online, mobile and social. It’s even possible that online sales are just now hitting a critical mass, recently reaching 10 percent of total sales.

It’s not something providers of such technologies are anxious to talk about – most smart technology initially is being marketed as “assisting employees” – but AI-ML-NLP technologies will be deployed to reduce the labor costs involved in keeping physical stores open, if they’re not already having some impact on hiring. (Wal-Mart, for instance, recently said it was six to nine months from beginning to use drones to check warehouse inventories in the United States.)

While it’s certainly true that in many cases AI-ML-NLP technologies empower retail workers and enable stores to deliver an omni-channel experience, in other cases, they specifically handle tasks traditionally executed by retail sales staffs, cashiers and warehouse workers. When the machines are smart enough and the artificial intelligent enough, those tasks are done much more effectively.

That is no slight on the value of retail employees or the customer service they provide. Rather it is an acknowledgement of the rapid advancements taking place across the spectrum of AI-MLNLP technologies, driven most recently by “deep-learning,” whereby large neural networks modeled after the human brain are fed enough data to be trained to do all kinds of things. Such “artificial brains” are the power behind Google’s search, Apple’s Siri, Amazon’s recommendations and Tesla’s self-driving cars. As those advancements increasingly make their way onto the retail sales floor, it becomes almost unfair to make comparisons between human and “artificial” or machine-based capabilities. After all, machines don’t need breaks or vacation days; they’re never late for work, never steal merchandise and can work 14 straight hours, seven days a week without overtime pay or Labor Department disputes.

Sure, even the smartest machines have their limits. There will be maintenance and upgrade costs and break-fix inconveniences, but non-human workforce solutions also have no need for health insurance, worker’s compensation and employment tax, and human employees simply can’t compete in terms of automatically gathering, storing and retrieving on-demand gobs of customer data the way machines increasingly can. Smart machines and robots also can speak multiple languages and be updated constantly with real-time inventory and customer data.

 

RELATED: FloorSpace: The Future of the Sales Floor

On the other hand, there will always be lots of consumers who prefer the face-to-face of human interaction and real-person problem solving. But there is also most certainly a decent percentage of shoppers who are indifferent or even prefer interaction with non-humans. A recent study by Mintel suggest as much.

Within the relatively high-touch category of cosmetics and beauty products, Mintel found that 45 percent of beauty consumers prefer to search for product information in-store on their mobile devices rather than ask for assistance from a sales associate. What’s more, two in five (39 percent) of those consumers are interested in using, or have used, a store-provided tablet to research beauty products available.

When former McDonald’s USA CEO Ed Rensi recently stated how it would be cheaper to buy a $35,000 robotic arm than hire a $15 an hour employee to cook and bag French fries, it’s was largely said as a claim in the highly charged minimum wage debate. But placing politics aside and looking at the matter purely mathematically, smart machines at those prices can be justified by eliminating the cost of just one full-time employee, especially when factoring in the dollars for training, insurance, sick days, employment tax and so on.

We’re also already seeing “robot-as-a-service” models being discussed, under which the cost to purchase, maintain and upgrade smart and learning machines is lumped into a recurring monthly cost – much like labor. And whereas current retail technology investments in online, mobile, local and social generally need to be justified by a boost in revenue or customer retention, capital for AI-ML-NLP investments may already exist in budgets, shifted over from the labor line item.

Some may say we sound like doomsayers, or at least are inflating the type of hype this publication is usually careful to deflate. Even so, events and advancements that truly disrupt longstanding business models don’t appear very often. When they do, it’s always better to know about then too early rather than too late.

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