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3 AI-Driven Strategies For Retailers In 2019
This article is part of our November 2018 series about the state of retail. Click here for more.
Artificial intelligence is reinventing the retail industry as we know it. From personalized customer experiences across digital touch points to improved product management, this powerful area of computing is helping retailers up their relevance, efficiency, and, ultimately, their bottom line.
“When we look across the retail industry, there is, surprisingly, quite a broad use of AI already,” said Vish Ganapathy, managing director and global retail technology lead at Accenture. “A lot of that has got to do with the fact that more and more technology vendors are injecting AI into everything that they do within their applications. For retail, there are very specific areas where AI can really make a big difference.”
Below we take a look at where retail stands today in its use of AI, with examples of companies that are reaping the benefits.
Personalization is table stakes for today’s retailers, who are increasingly competing to be relevant in the hearts and minds of shoppers.
According to Ganapathy, AI’s ability to absorb and sort through a lot of unstructured data and use that information to gain more relevance among customers is a big boon for retailers. Gartner predicts that smart personalization engines used to recognize customer intent will enable digital businesses to increase their profits by up to 15% by 2020.
“When I say personalization, I mean it to be a deeper level of relevance,” Ganapathy explained. “If you simply put my name on an email, that’s personalized, but if your email has an offer for wine and I’m a beer drinker, it’s not very relevant.”
One retailer that’s synonymous with personalization and relevance is Amazon. Its recommendation engine analyzes users’ past purchases, items already in their carts, products they have rated, and more to figure out the most relevant items to serve shoppers. It has been reported that Amazon drives 35% of its sales through its product recommendations engine.
British online fashion retailer Asos.com also is using AI to help shoppers choose the right sizes. The retailer analyzes which items customers keep and in which sizes versus the items and sizes that get returned most often. Using machine learning, Asos can then recommend appropriate sizes for individual customers. As a result, returns are minimized, the customer experience is better, and Asos reduces costs.
Nikki Baird, vice president of retail innovation at Aptos, said that the majority of investment in AI today by retailers revolves around personalization of product recommendations and the next offer to give. Personalized product creation is the next level of AI, she said, with some retailers already doing early tests in that space.
For example, Italian online fashion retailer Yoox used AI to design its first private-label collection. It analyzed fashion-related social media posts in its key markets and also data from products sold on the site, customer feedback, industry buying trends, and top trend searches to come up with the 8 by Yoox collection.
Jeff Barrett, CEO of Barrett Digital, predicts that in the next two years our phones will become the main delivery mechanism for relevant retail experiences. “Retailers will start putting retail pop-ups in the locations where they see there’s a lot more traffic,” Barrett said. “Experiences will become more fluid about what physical retail looks like, meaning that it will adapt to where people want to be.”
Optimizing Merchandising, Supply Chain, And Operations
Another area where AI is being used significantly is in merchandising and the supply chain.
“There are some interesting developments around how to onboard new products much more quickly,” Accenture’s Ganapathy said. “Image recognition, character recognition, etc., can very quickly predefine product attributes and allow a retailer to onboard new products into the business very quickly. This makes a big difference.”
Additionally, AI can also be used for better inventory management and product attribution, which can help merchants shave down costs.
For example, Walmart has a massive inventory with millions of products. The retailer uses AI to adjust inventory based on what’s happening in real time. For example, if rain is on the weather forecast for the next week, Walmart will shift its inventory to highlight the items that were most sought after the last time it rained for a while. At the same time, merchandise that is less likely to sell when it rains based on the company’s data is taken off the shelves.
AI also is going to be important on the operations side of retail to predict the maintenance of systems and in-store devices, according to Ganapathy.
“One anecdotal example I can give you is a large retailer that could spend anywhere from $40 million to $50 million a year on field tech: people going into stores and fixing printers or POS systems or even HVAC units,” Ganapathy said. “Imagine if you could use machine learning to get all of the signals from all these devices and then be able predict when one might need maintenance.”
For instance, in the future AI could tell you that your HVAC units need their filters cleaned before the units burn out and need to be totally replaced, he said.
Chatbots are the most common AI-powered customer service application today. To date, bots have predominantly been used to provide search and discovery and product recommendations.
Sephora, which has two Facebook chatbots, is ahead of the game. The first chatbot, Sephora Reservation Assistant, is an appointment-booking bot for makeovers. The bot has reportedly seen an 11% higher conversion rate versus any other channel for doing so.
The second bot, Color Match for Sephora Virtual Artist, is a shade-matching bot. For example, it can scan the face of a celebrity and provide a list of the closest-matching lipsticks.
Pepper, a customer service bot most often found in malls, is another example of chatbot innovation. Pepper is able to give directions to all the stores, restaurants, attractions, and services based on the user’s current location in the mall. She can also answer customer inquiries on deals, gifting recommendations, and holiday events in the mall.
“A well-designed bot provides the consumer a quick and easy solution,” said Amit Sharma, CEO of Narvar. “If their bot is as conversational and similar to real interaction as possible, retailers can essentially replace interactions with customer service reps and save shoppers from having to pick up their phones for answers – a big win for everyone.”