in Blog

January 29, 2021

The Best and Worst Practices for Machine Learning in E-commerce 2021


Edwin Lisowski

CSO & Co-Founder

Reading time:

9 minutes

In the modern business scenery, machine learning plays a crucial role in large e-commerce enterprises. This AI-based technology helps sellers reduce customer churn, improve personalization, and optimize marketing campaigns. However, as always, there’s the other side of the coin. In fact, improper use of machine learning in e-commerce can get you into trouble. And this can mean losing money and time. That’s why we decided to write this piece. We want to show you all the critical dos and don’ts of machine learning in e-commerce. Let’s just begin.

The entire e-commerce industry is tightly associated with the IT world. E-commerce businesses require at least one extended IT system–an e-commerce platform like Shopify or Magento. Naturally, the list of possible IT tools goes on; after all, you have mailing systems, affiliate marketing platforms, social media management and monitoring platforms, and so on. At some point, when your store is developed, and you’re serving at least 1,000 customers per month, you should start looking for even more advanced solutions that will help you grow. You should start looking for machine learning in e-commerce.

Machine learning in e-commerce

The importance of machine learning solutions for e-commerce can never be overrated. This technology helps you sell more and improve your efficiency. Also, machine learning can improve many of the UX-related aspects of running an online store, just to mention personalization, product recommendations, and search engine. Let’s talk for a few moments about how machine learning changes e-commerce.

Machine learning in e-commerce shopping

Mpre conversions, more sales

If you run an e-commerce business, you surely realize that conversions mean everything. You can have a beautifully designed website, brilliant ads, and an amazing offer. But if there are no conversions, it’s all in vain. Machine learning helps you increase conversions each month and, this way, grow sales. How so?

First of all, machine learning algorithms can deliver better search results. Thanks to an NLP technology (Natural Language Processing), ML algorithms understand more thoroughly what’s typed in the search bar. And it goes further; these algorithms learn from each query, making them more and more effective over time. Thanks to this technology, your customers can easily find what they are looking for.

Secondly, machine learning in e-commerce improves product recommendations. They become personalized and tailored to each customer’s interests and shopping history. This way, your store proposes only products that your customers should be interested in.

And thirdly, machine learning in e-commerce helps you curb the customer churn index. Customer churn is deadly to any online business. If more customers depart your store than come to it, you make less and less money every month. If you are interested in curbing customer churn, read about our customer retention analysis.

Machine learning in e-commerce sales

More effective marketing campaigns

Performance marketing is a tremendous tool every online store needs. Do you know why performance marketing campaigns are so effective? Because they are highly relevant to the given store’s target audience. If you want people to buy more, you have to give them a good reason. And that’s what performance marketing is all about.

Thanks to machine learning in e-commerce, your store can maintain this wanted high level of relevance. How is that possible?

You see, machine learning makes the most of the customer and market data you process. As a result, this technology can help you make more sense of the customer data you possess in order to tailor marketing campaigns in a better, more effective way. For instance, if you know that your customers are particularly interested in one specific category in your store, you can think of expanding offers within this category in order to attract more customers.

It is also worth saying a few words about remarketing. This is another powerful tool. Machine learning algorithms can analyze each customer’s behavior and devise a perfect retargeting campaign that will encourage your potentially-lost customer to go back to your store and place an order. In many instances, remarketing is the cheapest and most straightforward way to save a nearly-abandoned shopping cart.

marketing campaigns, analysis

Improved efficiency and the decision-making process

Bear in mind that e-commerce is not just the sales department. You still have to deal with inventory management, operational costs, cooperation with other entities (partners, vendors, subcontractors), taxes, accounting, and many more business aspects. Many machine learning algorithms and applications are designed strictly to enhance operational efficiency.
Almost every element mentioned in that article can be easily implemented in an e-commerce company.

As you already know, machine learning is a base for another AI-related tool called business intelligence. BI is all about using your data in order to tweak the decision-making process. With more information available in a transparent and straightforward way, you can make more relevant business decisions, which also helps you grow.

Now, you know how machine learning transforms modern e-commerce companies. Now, we are going to take a closer look at dos and don’ts regarding machine learning in e-commerce.

decision-making process

Machine learning in e-commerce: Dos and don’ts

What are the best and worst practices in e-commerce machine learning? What should you use machine learning for, and what mistakes ought to be avoided at all costs? Let’s analyze them. We will take a look at dos first.

Machine learning in e-commerce: Dos

What machine learning practices can be freely used? The list is quite long, and it comprises the following elements:

Personalized recommendations

You can freely utilize machine learning to devise an effective personalization algorithm. How does such an algorithm work? By analyzing collected data on your websites’ traffic, you can determine which sub-pages your customers visited most frequently. You could identify what they were looking for and where they spent most of the time. This way, your recommendation algorithm can propose new products that your customers should like and be interested in purchasing. An effective recommendation engine is priceless in every online business.

Personalized recommendations

Dynamic pricing

It’s, by far, one of the most beneficial ML-related aspects of e-commerce. Pricing is a beast difficult to tame. If your prices are too low–you don’t make enough money. If they are too high–you lose customers. That’s why retail companies use various tools that help them assess the perfect price level.

Machine learning can support these efforts by analyzing prices in your store and comparing them with your competitors and largest marketplaces (Amazon, eBay, Zalando, etc.). If your dynamic pricing strategy is effective, you can easily stand out from the competition and win the market.

Dynamic pricing

A/B testing

The whole point of A/B testing is based on discovering customers’ preferences. As it turns out, sometimes, even a small change in the offer or page layout can result in significantly higher sales. A/B testing helps you with this endeavor. With a developed A/B testing algorithm, you can adjust every element of your website and offer in order to make them as effective as possible.

A/B test


Just several years ago, chatbots were a bizarre novelty. People didn’t want to talk to them and tried to avoid them at all costs. Of course, if they have ever encountered one. They were not so popular at that time. Today, thanks to machine learning, they are much more popular and get more and more sophisticated and natural in communication. If you want to start using a chatbot in your store, feel free to do so. It’s a great way to improve CX in your online store, as chatbots offer valuable 24/7 customer service functionality.


Machine learning in e-commerce: Don’ts

What should you avoid when it comes to machine learning in e-commerce?

Ignoring seasonality

Many e-commerce businesses experience seasonality, which can severely affect your results. For instance, surely you know by now that sales go sky high during Black Friday and Christmas. Ignoring that trend can be a costly mistake. For example, you could end up spending the vast majority of your annual marketing budget during Christmas, leaving almost nothing for the rest of the year. If your company experiences variations in sales due to seasonality or other temporary fluctuations–always take them into account.

black friday

Using one ML algorithm for all problems

Machine learning is by no means an AIO (All In One) tool. Each problem you deal with, each goal you want to achieve, each strategy you want to implement require a separate ML algorithm. You have to understand that and act accordingly. Otherwise, you’ll end up frustrated because ‘machine learning doesn’t work.’ It works and, if set correctly, works effectively, but you need the right approach and understanding of this technology and its limitations. For now, every machine learning algorithm has to be trained and adjusted to one specific goal.

Ignoring various possibilities

Don’t forget that machine learning is no magic wand. Each decision, each version of events comes with some level of uncertainty. You should base on machine learning in e-commerce, but don’t turn your common sense off. Many inexperienced data scientists frequently fall into the trap of saying with 100% confidence that action X will result in Y. In real life, things are more complicated. In most cases, there is more than just one possibility, and you shouldn’t stick to only one of them.

machine learning in e-commerce, boxes


It’s not strictly a machine learning mistake, but it applies to the e-commerce world and can affect your sales and conversions. In one sentence, you shouldn’t be pushy. Don’t attack your customers with tens of banners and ads. If you want to promote a product, be subtle. People don’t like when you’re shouting at them. Keep your ads unobtrusive, and don’t interrupt your customer in placing and finishing an order.

In this article, we showed you the best and worst practices for machine learning in e-commerce. If you are interested in this technology–drop us a line. E-commerce is one of our major areas of expertise. We will gladly guide you through this technology and help you introduce machine learning consulting services in your online store. We are waiting for your message!



Machine Learning