Cognitive technologies have exerted a major disruptive influence across virtually all industries in the 21st century. AI & Machine Learning have been pretty much in the forefront of the cognitive technologies that have brought about this widespread disruption across industries and domains. According to certain market projections, global AI revenues are projected to experience massive growth from a base of USD 643.7 million in 2016 to an excess of USD 36.8 billion in 2025.
One of the domains which has been harnessing AI for heightened efficiencies and business benefits is retail. In this article, we will document eight use cases pertaining to the use of AI & Machine Learning in retail.
Navigation & Location of Items in a Large Store:
Navigating a hardware store can be a rather difficult process. To make things easier, Lowes created the LoweBot to help customers find their way around the store and get the items they need. LoweBots keep going around the store and ask customers simple questions to find out what they may be looking for. They then provide directions and maps to products and share speciality knowledge with customers. These robots also monitor inventory and so, the store management knows what items need to be restocked.
Amazon Go Stores - AI Replaces Cashiers:
In Amazon Go stores, customers can simply walk into the store, pick up what they want to buy from the shelves and walk out without going through a cashier. Sensors and cameras throughout the store track what the customer has picked up, and his Amazon account is charged when he leaves. AI helps to create a quick and seamless shopping experience and does away with queues and waits near cash counters.
Digital Racks, Particularly for Apparel & Fashion Products:
With the help of AI, apparel brands can create virtual racks and trial rooms with gesture walls and touch-free monitors, to find the right style, without having to shuffle through a pile. Customers can instantly see how a dress looks on them and can browse through recommendations based on their various preferences. This not only helps to enhance the shopping experience but also enables the customer to choose from many thousands of options that can't be stocked, due to space constraints inside a physical store. With AI, a store can be converted into a bustling repository of design ideas that users can choose from. Also, stores will be able to gather deeper insights into consumer behaviour, which will help them to optimise their product portfolios, thus delivering a better retail experience to their customers in the future.
Using Chatbots to Understand Customer Preferences & Personalize the Buying Experience:
A floral and gourmet foods gift retail and distribution company and one of the first businesses to use the telephone and Internet for direct sales has been using a product of IBM's AI System Watson to help customers search for and place their orders online.
Using natural language, the product, known as GWYN, interprets customer questions about a product or service; 'she' can then follow up, if needed, with additional questions about the intended audience, occasion, and sentiment in order to suggest best-fit gifts for a particular customer. GWYN becomes smarter as 'she' interacts with more customers over time. 'Her' eventual goal is to offer a customised shopping experience based on a customer’s past buying behaviour. After implementation of the system, the retailer experienced a noticeable uptick in both sales and repeat buying.
With the help of AI, apparel brands can create virtual racks and trial rooms with gesture walls and touch-free monitors, to find the right style, without having to shuffle through a pile. Customers can instantly see how a dress looks on them and can browse through recommendations based on their various preferences. This not only helps to enhance the shopping experience but also enables the customer to choose from many thousands of options that can't be stocked, due to space constraints inside a physical store. With AI, a store can be converted into a bustling repository of design ideas that users can choose from. Also, stores will be able to gather deeper insights into consumer behaviour, which will help them to optimise their product portfolios, thus delivering a better retail experience to their customers in the future.
Using Chatbots to Understand Customer Preferences & Personalize the Buying Experience:
A floral and gourmet foods gift retail and distribution company and one of the first businesses to use the telephone and Internet for direct sales has been using a product of IBM's AI System Watson to help customers search for and place their orders online.
Using natural language, the product, known as GWYN, interprets customer questions about a product or service; 'she' can then follow up, if needed, with additional questions about the intended audience, occasion, and sentiment in order to suggest best-fit gifts for a particular customer. GWYN becomes smarter as 'she' interacts with more customers over time. 'Her' eventual goal is to offer a customised shopping experience based on a customer’s past buying behaviour. After implementation of the system, the retailer experienced a noticeable uptick in both sales and repeat buying.
Behavioural Analytics Based on AI-backed Surveillance:
Using state-of-the-art surveillance equipment powered by AI and computer vision technologies, retailers can capture and analyse customer behaviour inside stores. This will help them understand their engagement levels with current store layout and optimise operations for higher engagement during each visit. It will also bring about more repeat visits, leading to increased revenues.
Video-based monitoring and surveillance using analytics can also improve in-store security and reduce the incidences of theft. Footage obtained in real-time, run through analytics, can generate alerts to administrators, security personnel and store owners for prompt detection and preventive action.
Customised Product Recommendation for Shoppers:
Grocery chain Kroger is in the midst of implementing smart shelves for their stores. When a customer walks down an aisle and has his Kroger apps open, sensors identify the customer and highlight products that he or she might be interested in. For a customer who prefers gluten-free products, the app could highlight products which are gluten-free. Similarly, for young parents, it could point out the kid-friendly snacks available. The app also provides personal pricing and alerts customers if an item on their shopping list is on sale on some given days.
Using AI to Track the Spread of Flu in Different Locations:
Flu can be uncomfortable, inconvenient and even deadly. Knowing the neighbourhoods or locations where flu is widespread currently, helps people take necessary action to keep themselves and their families healthy. A large chain of pharmacies uses data from the number of anti-viral prescriptions it fills at more than 8,000 locations, to identify the areas where flu is widespread. The online, interactive map not only helps customers know how bad the flu is in certain areas, including their own neighbourhood, but also helps the pharmacy chain stock more inventory of flu-related products in infected regions. AI is thus both informing and empowering customers and enhancing revenues for the pharmacy chain.
Reading the Shopper's Mind Using AI:
Apparel chain Uniqlo has blended science and AI to create a unique in-store experience. Select stores have AI-powered UMood kiosks that show customers a variety of products and measure their reactions to the colour and style through neurotransmitters. Based on a shopper's reactions, the kiosk then recommends products. Customers don't even have to push a button since the UMood kiosks are equipped to pick up brain signals and process these using AI apps to figure out what the shopper may be feeling about each item shown.
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