AI sets a new battlefield for e-commerce players

  • “But AI is changing all of this, and adding tools which make the arsenal owned by e-commerce companies look archaic. Many malls in India and abroad are already adopting AI services which allow them to learn who the customer actually is.
  • The cameras installed in the malls are performing face recognition, and matching it back to the available database, where very rich and contextual information is stored about the customers.
  • Companies like Inkers are providing advanced retail analytics to offline retain chains which can tell retailers whether the customer walking in is a high-value customer or not, or whether he/she is walking in with the family, or the kind of stores visited prior to visiting their store, or even whether they came in a luxury car or simply walked in.
  • AI is bringing a new battlefield for the e-commerce players and AI analytics are helping offline retailers fight back.

8 cool technologies that are revolutionizing retail

Juniper Research predicts retailers will spend $7.3 billion on AI by 2022, compared with the approximately $2 billion spent in 2018.

Caper Introduces Smart Shopping Cart

Brooklyn, New York-based retail technology vendor Caper has developed a smart, self-checkout shopping cart that uses computer vision, sensor fusion, and three cameras to automatically ring up items placed in the cart.

Spoon Guru Uses AI to Help Shoppers With Food Allergies

Food search and discovery engine Spoon Guru offers a mobile app that uses AI to help allergy sufferers spot the products in a store that contain ingredients compatible with their needs

Ocado Uses Google Cloud ML to Handle Customer Complaints

UK-based online grocer Ocado is using machine learning (ML) powered by the Google Cloud Machine Learning Engine to increase the speed of analytics from shopping data and boost customer experience. When customers write to Ocado with complaints, Ocado can use a ML model to sort through and categorize incoming messages, Google Cloud’s Pillai said.

Heasy the Robot Points Customers in the Right Direction

Digital kiosks have existed in places like airports, shopping malls, and train stations for years, but now companies such as Hease Robotics are making them a bit more mobile. The company says mobile kiosks will bring 20 times more interactions than a stationary kiosk. Hease Robotics is producing 20 “Heasy” robots per month, according to Jade Le Maitre, co-founder and Chief Technology Officer (CTO) of Hease Robotics. The company has deployed Heasy the robot in retail locations in countries such as Denmark, France, and Germany.

Intel Powers Cashier-Free Stores

Amazon is a leading player in the growing trend of cashier-less retail stores and reportedly plans to open 3,000 new cashier-less grocery locations by 2021. Customers can grab the items for which they’re looking and leave stores without going to a checkout counter. In another innovative implementation, Cloud Pick and Intel are collaborating on cashier-less stores in China that incorporate automated door access, cameras, and computer vision to let customers check out without a cashier’s help.

AWM Smart Shelf Pushes Targeted Product Information

Smart shelves are another technology that could keep customers interested in visiting brick-and-mortar stores. One such product, the AWM Smart Shelf, features LED displays and targeted product information. Cameras gather data on shopper behavior and demographics to personalize the videos it displays. AWM can customize the videos according to age, gender, or ethnicity. The AI components keep track of shelf availability within a store.

Celect ML Helps Stores Predict Inventory Demands

Lucky Brand is among the retailers turning to ML and advanced analytics to optimize their allocation of merchandise in their stores. Celect’s Prediction & Optimization Platform makes this possible with its data modeling and prediction database. Powered by AI technology from the Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Laboratory, the Celect platform helps retailers such as Lucky Brand by pulling data from its customer relationship management (CRM) data and sales transactions.

Zone24x7 Aziro Robot Takes Inventory Counts in Stores

Large department stores are testing a robot called Aziro from

Zone24x7. It features an autonomous sensing system that uses radio frequency identification (RFID) to check shelf inventory. Zone24x7 says that RFID can help increase the accuracy of inventory counts and improve the ability to locate items within a store. In addition to a store showroom, the Aziro robot will be used in warehouses and distribution centers.

Protecting Retail Profits With Artificial Intelligence

“Retailers can use AI to reduce losses resulting from error, faulty processes and intentional fraud. According to the 2018 National Retail Security Survey from the National Retail Federation, more than 50% of retail shrink (the difference between actual on-hand inventory and the inventory level recorded in the computer system) was due to employee theft or paperwork errors. These types of losses leave a data trail that can be detected through data analytics. Typically, retailers use an exception reporting system to detect shrink, but I believe using a system with AI models built in could yield far better results.”

6 ways AI will revolutionize retail

IBM identified the following six ways the retail industry plans on utilizing AI, based on respondents’ feedback:

1 Supply chain planning (85%)

2 Demand forecasting (85%)

3 Customer intelligence (79%)

4 Marketing, advertising, and campaign management (75%)

5 Store operations (73%)

6 Pricing and promotion (73%)

Retail Breaks Out of the AI Black Box

“In 2019, we’ll see more organizations move to glass box AI, which exposes the connections that the technology makes between various data points. For instance, glass box AI not only tells you there is a new retail opportunity, it also uncovers how that opportunity was identified in the data. It also provides retailers with an opportunity to check their data – and any public or aggregate data they pull in – to ensure AI isn’t making bad assumptions under the adage ‘garbage in, garbage out.’”