AI in Retail – Prediction, Automation, and Personalization

Today, you can see plenty of retailers successfully applying artificial intelligence (AI), machine learning, and robotics in most parts of their value chain. Digital savvy retailers and eCommerce companies are leading the way, using AI not only to eliminate layers of manual labor, but to predict trends, optimize warehousing and logistics, set prices, and personalized promotions. Some are even aiming at the full anticipation of orders and shipment of goods without waiting for any purchase confirmation from the customers.

Thankfully, AI ensures several benefits in retail. First and foremost, it helps retailers make smarter decisions with accurate and real-time forecasting that improves supply management, assortment, and pricing. Second, it enhances operational efficiency and productivity, reducing costs. Third, AI helps retailers to boost their revenue by encouraging customers to spend more by creating personal and convenient shopping experiences.

That’s not all. In the future, AI will allow retailers not only to forecast but automate decision making real-time. How? Experts say that it will be through identifying and learning from patterns in large volumes of data, spanning many disparate sources such as previous transactions, weather forecasts, shopping patterns, online viewing history, seasonal shopping patterns, facial expression analysis, social media trends, etc. By leveraging the power of data, AI will be able to help companies adopt and adjust to an increasingly dynamic market.


The impact of AI-powered forecasting in retail is obvious in the market. A European retailer, for instance, was able to improve its revenue by about 1 to 2 percent only by using an AI algorithm to anticipate its fruit and vegetable sales. Based on the sales forecast, the company could automatically order more products, minimizing waste and maximizing turnover. Similarly, a German eCommerce merchant was able to cut surplus stock by 20% and reduced the product returns by around two million items in a year. It used AI to analyze billions of transactions and predict what customers would buy before they even place an order. Apparently, the system is 90% accurate in the forecast.

It has become critical for retailers to optimize storage space and location as more sales migrate online, and non-digital store sales per square meter are declining. In the UK alone, retailers need to shave space by more than 20 percent to return to 2010 sales densities in real terms. Thanks to AI, retailers can now use machine learning methods to predict future store performance and understand profitability drivers, when expanding their physical footprints in new concept stores.


AI presents a rich set of optimization opportunities for warehousing and store operations. Automation of operations in supermarkets and non-digital retailers can make a huge difference since many supermarkets are now offering both online sales and home delivery to satisfy online grocers and to match the full cost of physical stores. Automation in large warehouses enables autonomous robots to work alongside people to increase productivity and reduce injuries. Notably, Swisslog reduced stocking time by 30% since it began using autonomous guided vehicles in its warehouses. UK’s online supermarket Ocado embedded AI at the core of its warehouse operations, allowing automation to steer thousands of products over a conveyor and deliver them to workers just in time to fill shopping bags. Meanwhile, other robots whisk bags to delivery vans whose drivers are guided by an AI application that selects the best route based on current traffic and weather conditions.


Today’s digitally-savvy and hyper-connected consumers expect nothing short of a seamless, personalized online shopping experience from online retailers. They are continuously redefining the value by comparing prices online and even when browsing in a non-digital store. On the other hand, they are equipped with smartphones, necessitating an omnichannel strategy for retailers to optimize and tailor a unique experience to each shopper in real-time. According to studies, insights-based selling and personalized promotions can increase sales by 1 to 5 percent, while personalization, combined with dynamic pricing, can lead to a 30% growth in sales. The internet retailers are already several steps ahead in personalization, while traditional retailers are beginning to start using AI to compete.

Franch global retailer Carrefour and the US-based Target, for instance, deployed electronic beacons in their stores to understand customer behaviors and purchasing patterns, and they use machine learning to analyze data and determine which personalized promotions need to be sent to their customers while they shop. Interestingly, Carrefour reported a 600% increase in app users after it deployed beacons in 28 stores.

With the development of natural language processing (NPL) and other technologies like virtual assistants and facial recognition, AI-powered personalization can go far beyond targeted promotions. Virtual assistants can easily identify repeat customers using facial recognition, analyze their shopping history to make suggestions in a conversational way, using NPL. This would allow retailers to give more of a human touch in the customers’ shopping experience.

In the future, virtual assistants will push the boundaries of convenience by alerting users that they are about to run out of a product and proposing to buy more. Recent developments in smart home assistants will pave the way for a major shopping disruption, where computer vision helps to identify desired goods by taking a picture, or the assistant identifies preferential patterns from images and videos that consumers like online.

Deep learning and computer vision technologies now help store owners compete with the online retailers’ one-click convenience by completely eliminating checkout. Amazon Go, an experimental Seattle grocery store, allows shoppers to take goods off shelves and leave without seeing a cashier or stopping at a kiosk for self-checkout. Computer vision identifies them as they enter the store and then connects them to products from the shelves. When shoppers leave, the system deducts from their Amazon accounts the cost of the items in their bag, and sends an email receipt.

Artificial intelligence technologies could also be used to deliver goods minutes after purchase at scale in the future. Most of today’s efforts — by players as big as Amazon and as small as Reno, Nevada, Flirtey startup — focus on aerial drones that are not piloted. In July 2016, Flirtey made its first delivery to a private residence, a box of local convenience store snacks. Starship Technologies, an Estonian startup, has taken a different path: six-wheeled delivery robots which putter 4 miles per hour along city pavements.

Let’s summarize. Keeping up with the current competition in retailing will be as hard as it is important. In this article, we have seen several ways AI will revolutionize the future of retail. 1. AI will enable retailers to anticipate orders and optimize their customer needs. 2. It will personalize promotions to match shoppers’ preferences and tastes and recommend complementary products, using ­shoppers’ lifestyle prole. 3. AI will allow non-stop checkout and automatic payment. 4. It will enable autonomous drones and robots to complete last-mile delivery.