This article is provided by BRC Associate Member Ciklum.


Artificial intelligence (AI) plays a crucial role in modern retail operations. By incorporating AI solutions into a retail or e-commerce organisation, storefronts of all shapes and sizes can deliver the right products to consumers with improved speed and accuracy. Not only can AI help boost a retail organisation’s bottom line, but customers can also enjoy highly personalised and frictionless shopping experiences that can lead to higher brand loyalty and improved customer satisfaction.

Here are five of today’s most essential applications of artificial intelligence in retail and e-commerce companies:

1.   Machine Learning-Based Recommendations

With machine learning, retail and e-commerce companies can provide finely tuned product recommendations based on a customer’s interests, purchase history, and other highly personalised characteristics.

Suggested and auto-completed search terms are one straightforward implementation of machine learning based-recommendations. Dynamically tailored to the likes and interests of each user, broader sets of search terms and synonyms are presented to the user to make it easier to find what they are looking for. By offering some similar and relevant options alongside what the user is already seeking, retail and e-commerce customers can conveniently find what they are looking for without having to scour an entire website.

Recommendations that adjust to customer’s exclusive needs are more likely to lead users to sales conversions

AI powers Amazon’s comprehensive product recommendation engine, which is said to generate 35% of the company’s revenue. Using data on previous purchases, browsing history, and items that are similarly purchased together, Amazon can pinpoint particular items that customers are likely to buy.
Throughout the e-commerce landscape, recommendation engines have become one of the must-have tools for brands seeking to stay competitive.

Sales Booster, an AI-powered e-commerce tool by Ciklum, makes it easy for online retailers to provide highly personalised product recommendations. Serving as an electronic personal shopper, the tool takes advantage of machine learning algorithms to deliver customers products based on their specific needs and interests, producing a high-quality user experience.

2.    Computer Vision in Retail and E-commerce

Computer vision solutions allow for in-depth analysis and pattern recognition of photo and video feeds, allowing retail and e-commerce companies to extract insights from cameras set up in warehouses and storefronts. By taking a more in-depth look into day-to-day operations, organisations taking advantage of computer vision can enhance productivity, boost sales, and reduce shrinkage.

One productivity-boosting implementation of computer vision is through shelf management, which uses camera feeds to identify when product shelves are empty. Store employees can automatically get notifications when products need to be replenished, creating a retail operation that’s more efficient at keeping items stocked throughout the day.

Facial recognition also makes it possible to reach specific customers in a retail space. In-store retail advertising technology uses computer vision to display customised digital signage to customers based on age, gender, or other characteristics. Rather than committing to a static in-store display, facial recognition allows brands to display different messages targeted to specific audiences.

Machine learning can also identify shopper behaviour, such as one’s gait or hand movements, and can even spot shoplifters placing items into bags. One test conducted in Japanese convenience stores found that using such software made it possible to reduce shoplifting by up to 77%.

3.   AI in Logistics and Delivery

Coordinating the distribution and delivery of goods is one of the most complex challenges retail and e-commerce companies can face. Artificial intelligence allows for highly efficient delivery and logistics practices, enabling companies to automatically send goods, assign drivers, and manage inventory based on historical trends and customer data.

Amazon incorporates AI into every product purchase, extending from the first mile to the middle mile to the last mile. By predicting when and where a customer is likely to order a product, Amazon’s fulfilment centres can better manage inventory and have products ready to go with the fastest possible shipping. Computer vision tracks where items are stored in a warehouse, and when it’s time to ship, machine learning routes packages for delivery the moment a label is applied to the box.

Microsoft’s approach to logistics provides AI-enhanced IoT solutions to oversee fleets of connected vehicles and freight. Through its Azure platform, transportation and logistics companies can create an intelligent supply chain that improves service quality, boosts safety, and reduces overall costs. In partnership with FedEx, Microsoft is combining FedEx’s in-house IoT technology with Azure’s machine learning and AI capabilities to boost inventory management and logistics performance, providing near-real-time tracking and intelligence on global shipping conditions.

4.   AI in Retail Payments

When combined with AI, intelligent payment systems make it possible for customers to purchase goods without ever interacting with a traditional checkout automatically. Machine learning also helps reduce the threat of retail fraud, ensuring that transactions are approved and legitimate before posting.

One of the most well-known AI applications in retail payments is Amazon Go, the company’s brick-and-mortar convenience stores that combine deep learning and computer vision to provide a check-out less shopping experience. Companies like MasterCard are also seeking to transform traditional retail payments. Unveiled in 2020, its Shop Anywhere touchless payment system allows shoppers to skip checkout lines by taking advantage of an autonomous checkout system.

AI and machine learning also provide enhanced levels of fraud detection, a critical application considering that fraud ends up costing retailers $3.13 on every dollar lost in a fraudulent transaction.
Using a variety of data sets — including user identity, behavioural patterns, locations, transactions, and personal device identifiers — machine learning models can quickly determine whether a purchase is likely to be legitimate or fraudulent.

5.   AI-Enhanced Advanced Customer Loyalty Programs

Through AI applications that extract main customer insights from shopping data, retail and e-commerce companies can ensure that shoppers see highly relevant selections of promotions and products that keep them coming back for more.

Through automated data collection, AI-enhanced loyalty programs can produce unique scores that suggest the best customers engage with. Machine learning can also help identify which promotions resonate most with specific customers, making it easy to tailor marketing campaigns on an individual basis. 

Broadly, throughout customer loyalty programs, AI allows retail and e-commerce companies to better segment and identify customers. By explicitly tailoring products, special promotions, and content towards individual users, brands can ensure that customers are exposed to a highly relevant stream of offers that can help build long-term loyalty.