Machine Learning in Retail: The Basics and 10 Key Applications

In recent years, between shutdowns, curfews, supply chain disruptions and energy shortages, retailers must have felt like dinosaurs trying to dodge an asteroid shower and avoid extinction.

But unlike those giant prehistoric reptiles, the retail trade could count on an array of technological innovations to better meet the challenges of these difficult times.

One of the most impactful tools in this arsenal has certainly proven to be artificial intelligence, including its powerful sub-branch known as machine learning (ML). Let’s briefly define the nature of this technology and explore the main use cases of machine learning in retail.

The Role of Machine Learning in Retail

Machine learning in retail relies on self-improving computer algorithms created to process data, spot recurring patterns and anomalies among variables, and autonomously learn how these relationships affect or determine trends, phenomena and industry business scenarios.

The self-learning and contextual understanding potential of machine learning systems can be harnessed in retail to:

  1. Identify the underlying dynamics driving the retail sector.For example, ML-based data analytics systems are widely adopted in marketing to personalize the shopping experience with recommendation engines and targeted advertising based on customer data, but also to forecast product demand. or other market trends and thus optimize inventory management, logistics and pricing. strategies.
  2. Powering AI-related cognitive technologies such as computer vision and natural language processing (NLP), which recognize and learn visual and linguistic patterns respectively to mimic human sight and communication. These tools are typically used by retailers to collect data from textual and visual sources, power interactive solutions like chatbots and in-context shopping, or for video surveillance.

10 machine learning use cases that are redefining retail

How can retailers benefit from the aforementioned capabilities of ML algorithms in the field? Here are some of the most relevant machine learning use cases in a typical retail scenario.

1. Targeted Ads

Although primarily used in e-commerce, target marketing is a powerful tool for directing potential customers to both online platforms and brick-and-mortar stores. It involves segmenting users based on a set of behavioral, psychographic, demographic and geographic parameters (such as their purchase and browsing history, age, gender, interests, region, etc.) and target them with fully personalized advertisements and promotions.

2. Contextual purchases

A different, more interactive solution for grabbing users’ attention and directing them to your e-commerce platform is contextual shopping. This marketing tool leverages machine learning and computer vision to identify and flag merchandise featured in videos and photos on social media while providing a “shortcut” to access the related product page in an online store. .

3. Recommendation Engines

Once users arrive on an online platform, they may feel lost among a huge selection of merchandise. Recommendation engines are powerful tools designed to direct customers to the products they might actually need.

To provide personalized suggestions, these systems may adopt a content-based filtering approach, namely recommending items with similar characteristics to those purchased in the past, or opt for collaborative filtering, which consists of suggesting products ordered by other customers with similar buying habits, personal characteristics, and interests.

4. Dynamic pricing

Product recommendations and ads aren’t the only things dynamically changing thanks to machine learning. Nowadays, most online stores and e-commerce platforms constantly adjust prices based on fluctuations in product demand and supply, competitors’ promotions and pricing strategies, sales trends and more. wide, etc.

5. Chatbots

Chatbots and virtual assistants are highly interactive tools powered by machine learning and NLP and capable of providing customers with 24/7 user support (including information on available products and service options). shipping) while sending reminders, coupons and personalized suggestions to increase your sales.

6. Supply chain management

Product replenishment and other inventory management operations should never be left to chance. To better match product supply and demand, optimize the use of space in warehouses and avoid food spoilage, it is necessary to rely on the analysis and forecasting capabilities of the algorithms of machine learning. This means taking into account several variables, such as price fluctuations or buying habits based on seasonality, predicting future sales trends and therefore planning appropriate restocking initiatives.

7. Optimization of delivery

Another aspect of logistics that can be improved with machine learning is product delivery. ML-powered systems, powered by traffic and weather data collected through IoT sensor and camera networks, can easily calculate the fastest delivery routes. By processing user data, they can instead recommend appropriate delivery methods to best meet customer needs.

But the apotheosis of this approach is probably Amazon’s ML-based advance shipping technique, which predicts future deliveries based on customer buying habits, moves products to a more close, and therefore to be able to ship them more quickly and at a lower cost when the actual order is placed.

8. Autonomous vehicles

This embodiment of machine learning and computer vision for product delivery is still a long way from being perfected and implemented at scale. However, companies like Amazon and Kroger are betting on this technology and soon we could count on autonomous vehicles to speed up the distribution of products.


ML-powered computer vision systems can drive vehicles…and spot thieves. The main difference between these tools and traditional video surveillance solutions is that the latter identify intruders based on a rather imprecise rules-based approach that suffers from many false positives. Machine learning systems, on the other hand, can recognize more subtle patterns of behavior and alert management if something suspicious is happening.

10. Fraud detection

When it comes to online retailers and e-commerce platforms, thieves are more likely to steal money from credit cards than products off the shelf. Since machine learning algorithms are designed to identify recurring patterns, they can also identify any event that deviates from the norm, including abnormal transaction frequency or account data inconsistencies, and flag it as suspicious. for a closer inspection.

Overcoming modern challenges with machine learning

Artificial intelligence, machine learning and cognitive technologies have proven invaluable in increasing profits and optimizing costs, personalizing the customer experience, improving operational efficiency in logistics and inventory management and ensuring a safe retail.

Indeed, Fortune Business Insight’s 2020 report highlighted that the global AI in retail market is expected to reach $31.18 billion by 2028, with machine learning representing its leading segment.

From a retail perspective, this will make machine learning a beacon for finding the right course and mooring in a safe harbor after more than two years of rough seas.

About the Author

Andrea Di Stefano is a Technology Research Analyst at, a Denver-based software development company. He studies emerging technology trends and their most impactful business applications, focusing on AI, machine learning, analytics, and big data.

Featured image: ©Алексей Олейник

Previous Safe Stocks to Buy: Invest in Electronic Equipment Stocks in 2022
Next Grand Rapids-area election worker admitted to using USB drive in poll book: Court records