So much of what AI is capable of transforming happens behind the scenes. The complete opposite of that is retail’s wholehearted adoption of AI to increase the bottom line, keep customers happy in a sea of eCommerce sites, and reduce inefficiencies. Without further ado, here are ten sites and stores using AI, and doing it right.
Stores and Sites Using AI
Amazon—The AI Ecosystem to Rule Them All
Let’s get this out of the way. Have you ever gone back to watch a television show or movie known for breaking all the rules of its day and thought, “Well, that was kind of cliche?” It’s not that this show is cliche, however. It’s that it fundamentally changed everything that came after it, spawning copycats and improvements alike.
Amazon’s AI-driven ecosystem, including its groundbreaking recommendation engine, has changed things. It not only keeps customers clicking and searching, but it checks up on empty carts, alerts users to sales and new inventory, and acts like a personal assistant. This energy extends throughout the Amazon ecosystem. Users can search for products, get advice from Alexa, and then pick up a few things from Amazon Go minus the hassle of a checkout line. It’s the foundation for all AI integrations in retail.
If you want to hear more in-depth from the expertise of Amazon, Michael Jalkio (data engineer) will be leading a talk on managing data like a software engineer for ODSC East’s 2020 Kickstarter track, ” as well as Julian Simon (AI/ML Evangelist).
Morrison’s and H&M—Eliminating Stocking Missteps
Figuring out stock has long been an issue in retail. Before access to big data processing, retailers relied on painfully slow predictive methods. Excess and shorts can cost up to $1.1 trillion worldwide. Both Morrison’s and H&M are using AI to better predict stock needs through massive data processing of sales, location, weather, oncoming trends, and others.
1-800 Flowers—AI Chatbots
Eighty percent of companies are employing chatbots now, and one great example of this is Gwyn (Gifts When You Need) from 1-800 Flowers. Gwyn uses a common platform to communicate directly with customers, a messaging platform. Natural Langauge Processing allows Gwyn to respond to customer queries, find tailored offers and gifts, and guide customers through the shopping process. 80% of customers stated they’d use Gwyn again and the company increased sales 6.3% from the previous year.
Cosabella took out its entire ad agency and replaced it with AI with dramatic results. Their customer base increased by 30%, and ad spend return increased by 336%. AI deeply understands Cosabella’s customer base through the data, allowing the company to stop guessing and start seeing returns.
Stitchfix goes one step further with the recommendation engine to find clothing that fits both their customer’s measurement preferences and personal sense of style. Stitchfix uses a complex algorithm designed to predict customer preferences beyond simple style choices. The longer customers are with the company, the better AI gets at sending the right clothing, fitting just right, and at precisely the right time.
Rebecca Minkoff—Unified Commerce and the Store of the Future
The clothing designer has been offering the store experience of our dreams since 2015. AI powers touch screen mirrors, allowing people to browse inventory looking for inspiration or specific items. Interactive fitting rooms provide customized lighting, and RFID tech offers real-time inventory suggestions based on what customers are trying on. Customers enter phone numbers, interact with Rebecca through touchscreens, and check out sans cashier. Sales tripled during the first year.
FarFetch and Neiman Marcus—Visual Search
Language doesn’t cover the way we typically find things in real life. FarFetch and Neiman Marcus get around the limits of language using sophisticated visual search. Users can upload pictures they’ve taken of real-life clothing, and the site will find similar things in inventory based on the image alone. No more trying out 1000 combinations of keywords. This one gets straight to the point.
Visual search is the future of retail because it eases the process of search and cuts down on irrelevant results. The keywords “Red Dress” might return thousands of irrelevant results while something too specific returns none. Visual search uses a form of computer vision to cut out the keyword struggle, giving customers a reason to keep shopping at Neiman Marcus despite heavy competition in eCommerce.
ShopRunner – Classification and Computer Vision
Shop Runner uses deep learning models to make the classification of massive amounts of inventory from partner sites easier. Traditionally, eCommerce manually classifies this inventory, creating inconsistencies and taxing labor resources. ShopRunner has also been in the market to acquire an AI startup helping prevent fraud. All this provides users with accurate information while protecting the site and partners. ShopRunner’s Ali Vanderveld will be at ODSC East to discuss the ways ShopRunner uses deep learning for these retail tasks, “Using Computer Vision and NLP Together for Fashion Classification.”
Sephora—Making Choices Easier
Big retail stores struggle to keep up with the ease of online shopping, but Sephora is using AI to knock out two birds with one stone. Their Color IQ system scans a shopper’s skin and recommends makeup choices based on their unique color ID. It’s available only in-store, helping increase foot traffic and offering personalization without having to find an employee.
Walmart—Computer Vision for Robot Stock Assistance
H&M uses AI to predict stock, but Walmart is bringing those capabilities directly into stores. Walmart’s massive size means they spend a lot of human labor just keeping up with inventory. In Tampa Bay Walmarts, autonomous scanners are roving the aisles using a combination of computer vision and deep neural networks to scan, process, and record stock on shelves.
The job was formerly in the realm of managers and other team leaders and done by hand. Now, the robots could remove the burden of these tedious tasks, saving on operational costs, and putting human teams back on higher-order tasks.
ASOS reduced ad spend back in 2018 to invest heavily in new tech. The company then launched “boards,” a way for customers to automatically sort saved items into categorized boards using AI suggestions. AI uses this data to suggest other items on ASOS, send customers notifications about stock, and notify about price changes.
Combined with visual search and a powerful chatbot recommendation named Enki, ASOS can fully customize the user experience with little strain on human labor.
Stores Using AI —A Lesson in Efficiency
A big part of retail in the fourth industrial age is going to be delivering the personal experience customers want while reducing gross inefficiencies behind the scenes. Human teams are there to build relationships and problem solve in complex, higher-order tasks while AI provides support by analyzing data and finding patterns.
For now, companies investing heavily in AI are seeing gains across the board from the supply chain to the bottom line. AI is proving to be one of the best things to happen to retail in a generation.
Want to learn more about the use of AI in retail and other industries? Be sure to check out the Ai x Retail track as part of the Ai x Business Summit this April 14-17 during ODSC East 2020. You’ll learn about case studies, strategies, and insights on the best practices to integrate AI into your organization.