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Machine Learning in Ecommerce: Benefits and Examples

Now that ChatGPT has been tried by almost every person in the ecommerce business, machine learning is becoming more and more normalized. It’s also a lot more accessible to small and medium business owners.

Want to understand what machine learning is and how it can be used to benefit your business? Here’s a quick breakdown.

What is Machine Learning?

Machine learning is a branch of applied computer science that deals with training artificial intelligence to conduct certain operations without needing instructions in the form of algorithmic code. In a way, it imitates human learning in a process where AI learns what it should do through trial and error with the help of a human operator.

Thankfully, businesses don’t need to conduct machine learning from scratch as there are dozens of ready-made solutions on the market. All you have to do is add some specific knowledge on top of their already existing experience and use them for your needs.

Benefits of Using Machine Learning in Ecommerce

Machine learning can be difficult to implement at your company, but when you do, it has some amazing benefits to offer. Here’s what machine learning solutions can do for your ecommerce business:

Customer retention
Increase in revenue
Automation of menial tasks
Improved analytics

ML solutions can do customer support and optimize sales to drive the bottom line upward, but the biggest benefit of using them is qualitative, not quantitative.

If you use machine learning in analytics, it can provide insights that you may not find in the data on your own. Finding correlations in sales and marketing data may lead to quality improvements in your approach to the business and drive more revenue than simple optimization ever could.

Applications of Machine Learning in Ecommerce

Machine learning may sound intimidating for SME owners as just five years ago it was only accessible to large corporations. It’s not anymore, and you can use it effectively in these areas of your business.

Product Recommendations

One of the easiest ways to implement machine learning solutions at your ecommerce store is through product recommendations. It’s an easy way to increase revenue by up to 30% and implementing it takes nothing more than installing an app or an integration and learning to use it.

Product recommendations are quite an intuitive tactic — offer similar products or products that may complement the current one, and customers are likely to spend more. Adidas official store, for instance, has three recommendation sections on product pages, one for similar products, and two for complimentary ones.

Source: Adidas

Source: Adidas

The issue is, the recommendation has to be relevant, otherwise, sales won’t increase. It’s possible to add recommended products manually, but if your store has hundreds of products, it will be virtually impossible.

An ML-based app can scan your store’s sales data for items that are frequently bought together and add them in the recommended section. It’s more precise than simply offering recommendations based on product categories and will drive more sales.

Chatbots and Virtual Assistants

Traditional chatbots are a major improvement in customer service because they take the most frequent questions off your customer support shoulders and let them focus on the difficult tasks. But an algorithmic chatbot is essentially nothing more than a convenient FAQ page that works in a messenger.

Source: Facebook/Wendy’s PH

An algorithm-based chatbot can reply to some of your messages by searching through them and finding a keyword.

Source: Facebook/Wendy’s PH

But when it comes to understanding the content of your message, it doesn’t work.

An ML-based chatbot is a more sophisticated software as it has natural language processing that allows it to understand human speech commands instead of browsing menus. It also has full access to all your policies and help pages and can save users time on browsing those on their own.

Price Optimization

Correct pricing is one of the hardest parts of an ecommerce business. You need to understand the market for hundreds of products at a time, and make dynamic changes as the market shifts to maximize revenue.

Machine learning is the perfect solution for this task as it can work with enormous amounts of market data and draw conclusions that may take weeks for a human to make.

The result is a pricing model that gives discounts where needed and marks up where possible to achieve the best ROI for your store.

Supply Chain Management

The two largest benefits of implementing artificial intelligence in supply chain management are automation and forecasting.

An ML-based solution can fully automate fulfillment by forwarding order details to the logistics hub. What’s more important, with enough historic data, it can predict demand for certain products or problems with the logistical system.

For instance, it can suggest ordering a specific number of products before running a discount based on previous spikes in demand or ordering a new supply of products when inventory runs low based on the average delivery time from the supplier.

Customer Segmentation

There are dozens of variables in paid advertising and on-site marketing. There’s a specific combination of these variables like age, location, customer behavior, or interests that reflect the portrait of a specific customer group.

But finding what those are by viewing a sales dashboard or by combing through the datasheets yourself is hard. Machine learning makes it faster and easier because it’s perfect for catching patterns.

With a solution like that, you can figure out the types of customers, segment them, and make your offers personalized to drive conversions.

Fraud Detection

Because machine learning is so good at catching patterns, it’s a great tool for ecommerce fraud prevention.

Preventing fraud relies on catching suspicious behavior early. An ML-based solution can figure out that a purchasing pattern is not right for the customer or payment or shipment details are not in line with previous behavior and notify you of potential abuse.

Examples of Machine Learning in Ecommerce

What are some prominent examples of machine learning in ecommerce? The one most people have experience in is Amazon’s recommendation system. It decides what products to offer to customers not only based on their behavior but also based on the behavior of customers that are similar to them in behavior, saving time on the learning curve when it comes to new customers.

Another that people in the dropshipping business may be familiar with is Alibaba’s Smart Logistics. It’s an ML-based logistics system that uses natural language processing to confirm shipping addresses and automates fulfillment with dropshipping suppliers.

A machine learning system that takes big data to another level is Walmart’s Intelligent Retail Lab (IRL). It’s a system that monitors customer behavior in the store with a complicated system of cameras and sensors to draw conclusions based on it.

Then, there are Stitch Fix’s personal stylist services that now run on AI. It works with a ton of historical data of human stylists making recommendations to customers and makes

Future of Machine Learning in Ecommerce

Machine learning and artificial intelligence tech is in still its infancy. Despite gaining immense popularity in the few months ChatGPT has been publicly available, it’s far from perfect. Technologies like these are likely to progress rapidly as they’re used by more and more people are may change the way we do ecommerce.

Machine learning in ecommerce is likely to do this for the industry:

Reduce reliance on human resources in customer service
Establish chatbots as a popular way of interacting with businesses
Increase reliance on big data in making business decisions as processing it is easier
Introduce more potential for customization and automation of workflows and websites
Drive up the demand for personalization in all areas of business

Many of these trends are already present today.


Machine learning in ecommerce is a powerful tool that can help any business, no matter how large it is and what its goals are. It has become easy to implement and can make your workload a lot lighter and improve your bottom line.

Start implementing machine learning in ecommerce now to harness the potential of this technology in the near future.


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The information provided in our posts or blogs are for educational and informative purposes only. We do not guarantee the accuracy, completeness or suitability of the information. We do not provide financial or investment advice. Readers should always seek professional advice before making any financial or investment decisions based on the information provided in our content. We will not be held responsible for any losses, damages or consequences that may arise from relying on the information provided in our content.


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