Enterprises that operate thousands of stores with dispersed, siloed data can't get a clear view of their overall performance. For example, you may overlook that a particular product sells once in six months yet calls for effort and money to be maintained and kept on the shelf. Or you've failed to predict that another product would be out of stock soon – and now unhappy customers are buying it from your competitors.

Data Analytics, as well as Artificial Intelligence and Machine Learning development, help retail businesses address these problems. The results of their implementation can work wonders in almost any retail area.

Machine Learning in retail in numbers

Let's get deeper into the use cases of Machine Learning in retail and shed some light on whether you need the technology, the key aspects it can improve, and how to implement it.

Key business challenges Machine Learning in retail can solve

Machine Learning offers solutions to address several key business challenges for retailers. By harnessing advanced algorithms and data analysis, retailers can make data-driven decisions, enhance customer experiences, and more. Here are ten ML use cases in retail:

1. Demand prediction and stock optimization

A common goal for many retail companies is to effectively predict demand and ensure that their products are always in stock. Let's consider a scenario where a customer wants to buy champagne before New Year's Eve at a store in their neighborhood and can't see it on the shelves. First of all, such a stockout will leave the customer dissatisfied. Then, they might go to a competitor's store and buy the champagne there. Furthermore, the сustomer might choose to shop elsewhere in the future.

Demand prediction is a complex task. Even a small store offers 10,000+ products, and the buying trends for bananas and microwave ovens, for example, will differ. Some items sell 100 units daily, while others sell once a week. Building a universal model that will work the same way for all products is challenging.

However, this is one of the critical tasks. Implementing Machine Learning in retail helps to reduce shrinkage and wasted products on the one hand and lost opportunity on the other hand. For example, if a grocery store sells 30 kg of bananas per three days, but we didn't know that beforehand and bought from a supplier just 20 kg – that's a lost opportunity. Thus, it's crucial to predict the demand and be geared to satisfy it.

Machine Learning models analyze historical sales data, seasonality, and external factors (like holidays or events) to predict future demand accurately. This capability, in turn, enables retailers to optimize their stock levels, reducing overstock and understock situations.

2. Gross profit optimization (price/demand balance)

Price formation and designing demand is another issue to be addressed by retail enterprises. It's a common truth that retailers can create the desired demand by setting a specific price. However, finding the right price/demand balance to maximize profits is also a common pain.

Factors such as competition, market positioning, production costs, distribution costs, the period of the year, the current state of the market, and others should be taken into account. Machine Learning algorithms can help enterprises analyze vast amounts of data to find the ideal pricing strategy that maximizes sales volume and gross profit. It helps retailers adjust prices dynamically based on demand fluctuations. 

In addition, you can optimize pricing strategies on the level of an individual product instead of using general markdowns. Machine Learning predictive models use time-tested techniques such as statistical analysis to determine the most suitable price for each specific product or service.

Keep reading: Price optimization with Machine Learning: Implementation, tips, and success stories

3. Solving logistics issues

Machine Learning can also help retail companies streamline supply chain operations, optimize routes, predict delivery times, and minimize logistics costs. This way, you can ensure that products are readily available in stores and to customers when needed. ML models spot patterns in supply chain data by quickly pinpointing the most critical factors to the supply networks' success while constantly learning in the process. 

In addition, Machine Learning can be efficiently used to optimize warehousing (as champagne is better to be stored in warehouses before the New Year for a while, and bananas, for example, are better to be delivered to the store points as soon as possible). The same rule applies to thousands of other different products. 

Related: Supply chain control tower: Helicopter view of your supply chains

4. Merchandising optimization with visual search

Merchandising is a task that requires much time and effort and may be prone to carelessness if people are to do it. That's why many companies start delegating this job to code. Visual search powered by ML allows customers to search for products using images. Retailers can use this technology to enhance their merchandising by recommending visually similar products, improving the overall shopping experience.

For example, retailers can monitor merchandising in stores using cameras. This enables businesses to identify when items are out of stock or improperly arranged in real time, helping to maintain an organized and visually appealing store layout.

5. Personalized offers

Retailers use ML algorithms to analyze vast amounts of customer data, including purchase history, browsing behavior, and demographic details. Personalized offers improve the customer experience by offering relevant information, enhancing customer engagement, and driving sales. Machine Learning enables precise product recommendations, suggesting products that align with individual preferences and boosting the chances of customers making purchases. Furthermore, with ML models in place, enterprises can offer real-time personalization. ML allows timely discounts based on a customer's browsing or shopping activity.

6. Fraud detection

Machine Learning in retail helps data scientists efficiently determine which transactions are the most likely to be fraudulent. The ML techniques are highly effective in fraud prevention and detection, allowing for the automated discovery of patterns across large volumes of real-time transactions.

More on the topic: Fraud detection with big data analytics and machine learning: How to make it work

7. Churn prediction

One more application of Data Science in retail is churn prediction, which is particularly effective when tracking the activity of daily-used products. By analyzing customer behavior and purchase patterns, data science models can identify signs of potential churn or customer attrition. This allows retailers to take proactive measures, such as targeted marketing campaigns or personalized offers, to retain customers and prevent them from switching to competitors. However, you need to understand that there may be something you can't see regarding an individual customer's behavior, even leveraging their buying history and ML models. In such cases, both your predictions and effort to retain the customer may be ineffective.

8. Selecting locations

Another critical task for retailers is choosing where to build a new store and finding the best location for a specific product type. They should take into account such factors as demographics, the closest competitors, the number of population in the neighborhood, etc. Machine learning algorithms can analyze many factors and provide insights into the optimal location for a specific type of product or store.

9. Sentiment analysis

Many companies use NLP techniques and sentiment analysis to monitor and track customer reviews and customer satisfaction. In retail, you can also use it to analyze a particular brand's customer reviews to see if it's a good idea to cooperate with a supplier and sell a specific product. Positive sentiment from customers is a sign of a potential opportunity for collaboration, indicating that customers have a favorable opinion of the brand or product. At the same time, negative sentiment may suggest caution or the need for improvements before partnering with a supplier or introducing a new product line.

10. Document work automation

Using NLP and automated processing of documents and agreements goes a long way toward simplifying and speeding up paperwork in many industries. For example, a manual review of 12,000 annual commercial credit agreements typically takes around 360,000 labor hours. At the same time, Machine Learning allows reviewing the same number of contracts in just a few hours. The retail industry doesn't make an exception here, and many retail companies are already using it to automate their work with multiple suppliers and processing of customer claims.

Machine Learning use cases in retail

How to implement Machine Learning in retail?

Depending on a business case and specific objectives, the process of implementing Machine Learning for the retail industry can vary. However, in general, it involves several crucial steps:

1. Introduce Big Data engineering

Like in any other domain, the biggest part of any data science project is building an orchestrated ecosystem of platforms that collect siloed data from hundreds of sources like CRM, reporting software, spreadsheets, Excel tables, and more.

Most businesses, including retail companies, don't need complex prediction models or Data Science. In many cases, enterprises try to apply DS models even if they don't bring any actionable results or reveal the truth already known to everybody. However, the most crucial part is to collect the data from dispersed sources and visualize it to actually see what's going on, what needs fixing and more effort, and what needs to change.

That's the hardest part since the data needs to be structured and cleaned before it can be used for ML models. ETL (extracting, transforming, and loading) and data cleaning typically consume a significant portion (70-80%) of the project's time, especially in large retail companies with multiple stores and legacy IT systems. Compliance with data privacy regulations like GDPR also adds complexity to data handling.

2. Implement DataOps

The next step is to introduce DataOps – a crucial practice that combines DevOps teams with data engineers and data scientists. It provides the tools, processes, and organizational structures necessary to manage data effectively. This is particularly important when dealing with large volumes of data from various sources in a retail setting.

3. Apply Machine Learning models

Most machine learning projects deal with issues that have already been addressed. Сompanies such as Google, Microsoft, Amazon, Facebook, and IBM sell machine learning software as a service. For instance, N-iX Data Scientists are using Prophet, an open-source tool by Facebook, for Time Series Forecasting. Google offers a wide range of effective plug-and-play recommendation systems.

To apply these services and introduce Machine Learning in retail, you need a team of skilled engineers to implement the system focusing on your specific data and business domain. Your Machine Learning engineers need to extract the data from different sources, transform it to fit this particular system, receive the results, and visualize the findings.


Though you can't make absolutely precise predictions, Machine Learning is indeed a powerful tool for addressing key challenges in the retail industry. What you need and can do with ML is to have a vivid picture of what is going on in your business, as the more info you have at hand, the more clearly you can see that something is going wrong and needs fixing.

From demand prediction and stock optimization to personalized offers and fraud detection, ML offers a range of applications that can significantly enhance retail operations. Adopting ML in retail involves a structured and tailored approach implemented by experienced Machine Learning specialists. Here, at N-iX, we help companies develop effective tools to navigate their business data and always be one step ahead of the competition.

Reach out to top Machine Learning experts at N-iX


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