by Michael Klein, Adobe
Imagine walking into your favorite clothing store. Rather than combing the shelves or checking the tags on mannequins, you walk up to a kiosk that recognizes you and you input what you’re looking for — a new suit for a business trip to New York. Using the data from your previous purchases, inventory available at the store, weather forecasts and current fashion trends, an AI algorithm puts together several suit options. With minimal effort, you find a suit, shirt and shoes perfectly suited to your taste and needs.
Companies like Alibaba are already making scenarios like this one a reality. For Singles Day, Alibaba introduced a new app called FashionAI into 13 physical retail stores in Shanghai. The system scanned clothing taken into a dressing room by a shopper and tapped into the store’s inventory, as well as data collected from fashion experts and designers. Informed by that data, the retailer suggested complementary items. For Singles Day 2017, Alibaba drove a record $25 billion in sales for that one day.
And it’s not unique to Alibaba — Amazon Go’s cashierless store concept uses hundreds of cameras leveraging computer vision to “see” and determine the items in front of them. Other buying and selling platforms are experimenting with computer vision, so shoppers can take a picture of something they like and search for similar products for sale. Or, they can take a picture of their couch and find a lamp or table to match the style.
AI Boosts Sales
The amount of money companies have invested in AI technologies has tripled since 2013, and early adopters are seeing better profit margins than their competitors, according to a recent McKinsey study.
As people increasingly shop online, they miss the opportunity to see, feel and try on clothing before buying, and brands miss the opportunity to connect in-person with their customers. But AI applications like FashionAI can bring the two together by providing a better online experience and steering people to the physical store. AI can give brands the information needed to understand a shopper’s preferences and fashion sense, helping clothing stores better predict what pieces of clothing the buyer might like. In the case of FashionAI, it combines the ease of online shopping with the tangible in-store experience — delivering a buying experience that’s digitally immersive and personal.
When customers can’t physically go to a store, wouldn’t it be great if they could leverage a personal shopper? Some can, and it’s not only the affluent. Many brands are already participating in “conversational commerce,” where customers can use chatbots or their voice-enabled assistants to communicate with brands in a conversational format from their home — and it relies on AI technology to make it a viable experience. Brands like Louis Vuitton, Everlane, Burberry and Nike are using Facebook Messenger, which uses AI and the ability to take voice to text or use bots to give customers a guided shopping experience.
This guided shopping experience also blends into subscription retailers like Stitch Fix and Trunk Club, by combining machine learning algorithms with human personal stylists to curate personalized wardrobes. A Stitch Fix order is processed by five to 10 styling algorithms before it ever reaches a human stylist. Each algorithm has a distinct purpose in the styling process — from matching the stylist to the client based on style preferences to assigning which warehouse assembles the order. Stitch Fix currently employs more than 85 data scientists that oversee the entire process.
What about the store’s back end? Relying solely on manual methods of stocking and searching inventory leaves significant room for worker error and upset customers. AI is capable of rapidly searching massive inventories much more quickly, efficiently and thoroughly than a human can. When companies have a better way to analyze and understand customer data, they have a better idea of which products to stock where.
Those tags that you have to snip out of your clothing or have to be removed by someone in the store — they’re inventory and loss-prevention tracking devices that are now becoming even more powerful to try to understand and predict supply and demand. AI can help retailers gather and process data from these devices quickly in order to adjust inventory in real-time.
For example, AI could be used to track inventory and predict that a certain product will fly off the shelves in California, but have sluggish sales in New York. With AI and machine learning, the retailer could accurately forecast stocking more of the item in their West Coast distribution center and much less on the East Coast. This would improve sales, raise margins and prevent overburdening a store with merchandise that doesn’t move.
Predict Shoppers’ Needs
Going further outside the retail environment to product development, AI can help analyze consumers’ tastes and the most popular items in a company’s inventory and then convey that information to designers. Designers can create new products they know will appeal to buyers, particularly if AI can help them track trends that may still be in play, or ones that are being exhausted. For example, if a group of shoppers buys furniture consistently in a certain style, AI can indicate the pattern to product designers, who can create new accessories to match the furniture already purchased.
A group of Amazon researchers has already developed a program that determines whether a piece of clothing is stylish by analyzing images of the latest clothing lines. This feedback is available to designers as they create new clothing designs.
Another Amazon team developed a rudimentary AI fashion designer that generates new designs for pieces of clothing. Using a generative adversarial network (GAN), the program can analyze a wide variety of styles and apply what it learns to design a similar item.
Using AI To Improve Communication And Efficiency
AI also can drive productivity for employees in stores. One new technology, Theatro, allows employees at large retailers to access an AI-powered virtual assistant with their voice to gain information about the store almost instantaneously. Someone working on the floor at Home Depot in Canada has the ability to connect a Theatro device to an artificially intelligent virtual assistant that talks to her through an earpiece. The assistant can help her understand things like inventory and back-of-the-house operations, so that more employees can be on the floor working with customers instead of physically searching for answers in outdated systems in the back of the house.
With technology like this, employees can work faster and smarter, and can avoid wasting time on menial tasks that can be handled by a computer.
Early AI adopters also are investigating ways to communicate and work globally — like AI-powered translation services that allow a person to speak English into a microphone and have it emerge as Chinese from an earpiece across the world.
A Future of Enhanced Experiences
The magic of the best technology is when we don’t notice the technology. When we receive a great experience and can’t determine if it was created by man or machine, that’s a magical experience. It’s no different with AI.
The backbone of AI is machine learning with data, and customers provide an endless supply of data, from purchasing patterns to browsing behaviors. AI can combine the intuitiveness of human employees with a machine’s ability to analyze massive amounts of data in seconds. As AI is used in more commercial and consumer applications, the possibilities for integrating the technology into commerce and retail experiences will only grow bigger. The benefits of AI are in its ability to understand consumers, improve worker productivity and efficiency and boost sales.
About Author
Michael Klein is the Director of Industry Strategy for Retail and Travel & Hospitality at Adobe, where he leads a team of subject matter experts who work with Adobe’s global retail clients to help them develop best-in-class digital marketing strategies. His vast experience includes over 25 years as a Senior Merchant and Marketer for brands such as the Williams-Sonoma, Harry & David, Discovery Channel Stores, eLuxury.com (LVMH Group), and wine.com.
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