Ecommerce Technology

Ecommerce Machine Learning: AI’s Role in the Future of Online Shopping

Nick Shaw / 11 min read
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Ecommerce Machine Learning: AI’s Role in the Future of Online Shopping

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Table of Contents

    Once you’ve been in ecommerce for a while, you’ll know the industry’s symbiotic relationship with technology. Ecommerce only exists thanks to the rise of the internet. The global spread of smartphones then helped put online stores in everyone’s pockets. 

    Ecommerce Machine Learning 1


    Ask those in the know what the next trending tech is, and many will tell you it’s artificial intelligence (AI). AI is fast taking hold across niches and in a plethora of guises. The ecommerce sector is far from immune. 

    AI, and particularly the machine learning subset of the tech, is having a profound impact on ecommerce businesses. There are many applications of machine learning within the ecommerce industry.

    Read on, and you’ll learn about a few of the most notable. What’s more, we’ll explain why you’re missing out if you’re not already leveraging the potential of AI for your brand. First, though, let’s cover the basics.   

    Machine Learning Has Developed Over the Years  

    Before we get to the nitty-gritty of machine learning and ecommerce, it’s vital to understand just what machine learning is. At a basic level, it is what it says on the tin, a process by which a machine can learn. As you may guess, though, things are a bit more complicated in practice.

    Machine learning is an application of the wider tech area of artificial intelligence. It involves creating algorithms or programs that can access and learn from data. All without having to get programmed by a human. 

    How those algorithms ‘learn’ is primarily by pattern recognition. You train a machine learning algorithm by introducing as much data as possible. It then analyzes the information and finds the trends included within. Eventually, the algorithm is ‘intelligent’ enough to apply what it’s learned to new data sets. 

    Machine learning algorithms are typically categorized in one of three areas: 

    • Supervised – These apply what’s been learned in the past to new data using specific labeled examples. They can predict future events and compare their output to the intended results. That helps the algorithms improve themselves with ‘practice’.
    • Unsupervised – These algorithms analyze unlabeled and unclassified data. There are no specific examples upon which to base predictions. Such programs, then, draw inferences and ID hidden structures or patterns within data. 
    • Reinforcement – Reinforcement algorithms interact with their environment to test outputs. Through trial and error, the programs discover correct behavior. They then tailor their future responses in accordance. 

    The premise of machine learning stretches back longer than you might think. The discipline began soon after scientists found out how neurons in the brain worked. 

    In 1952, Arthur Samuel created a computer program that could play checkers. Six years later, Frank Rosenblatt built the first wholly artificial neural network. That’s a machine learning algorithm based on the general structure of human neurons. 

    The field of machine learning continued to develop in the ensuing decades. By 1997, IBM had created a computer called Deep Blue. It successfully beat the world chess champion. It’s in the 21st century, though, that the field has accelerated in earnest.

    That acceleration is primarily thanks to the invention of GPUs (Graphics Processing Units). These processors have the power to let algorithms analyze far more data in a far shorter time. As such, modern machine learning can understand more complicated data-sets. It can also make far more accurate and complex predictions. 

    Differences Between Machine Learning and Artificial Intelligence

    You may have read this far and thought ‘hang on, aren’t you describing AI rather than machine learning?’. Well, the answer is both yes and no. Much like fingers and thumbs, all machine learning is AI, but not all AI is machine learning. 

    1. Machine learning. 

    Machine learning is a subset of artificial intelligence. Machine learning technology uses data to make predictions or perform actions. The more data the tech gets exposed to, the more accurate its outputs. That’s how algorithms in this area can get described as being able to ‘learn’.  

    2. Artificial intelligence. 

    A far broader range of tech falls under the umbrella of AI. Artificial intelligence is any technology that exhibits human behavior. That may mean learning, but could also be reasoning, sensing, or adapting. 

    Deep learning, too, is another subset of AI, and in many ways of machine learning. It’s where complex neural networks analyze and learn from massive data sets. We’re talking the volume of information that’s only become available in the era of big data.  

    Business Benefits of Ecommerce Machine Learning 

    Along with other tech such as augmented reality, machine learning offers many business benefits. Especially to online retailers. The ability of algorithms to make sense of vast swathes of data is invaluable. 

    There are now machine learning applications for almost every area of ecommerce operations. From inventory management to customer experience, ecommerce machine learning truly delivers. Let’s dig deeper into how machine learning could benefit your business.  

    1. Increased conversions. 

    Turning browsers into online shoppers is crucial for any ecommerce website. That’s why you’ll no doubt be a little obsessed with your site’s conversion rate. One reason machine learning is so useful to ecommerce is that it can help boost that rate in many ways. 

    We’ll cover how machine learning aids conversion rate when we look at ecommerce use cases. Typically, though, its value in this regard comes in two areas. These are how it can empower on-site search engines and product recommendations.

    Machine learning algorithms can deliver smarter search results. Via natural language processing, they can understand what’s typed in the search bar. They’ll then use what they’ve learned from previous searches to show what the searcher genuinely wants to find. That’s even if they don’t type the name of a specific product or even an accurate description. 

    Product recommendations powered by machine learning are also smarter. Algorithms can analyze the behavior of visitors to an ecommerce site. They’ll recognize products a visitor browses or buys, and the content with which they interact. 

    When an individual returns, then, they get presented with similar items to those they’ve shown an interest in before. That’s how, when you visit Amazon, you’ll see swathes of things related to those you’ve recently bought or looked at. 

    2. Run more relevant marketing campaigns. 

    Ecommerce marketing shares many similarities with sales prospecting. The best campaigns are highly relevant to their target audience. Machine learning can help an ecommerce company maintain that level of relevance.

    In the era of big data, ecommerce stores have access to more information than ever before. Machine learning can help them make sense of customer data to better tailor marketing campaigns. 

    The patterns IDed by machine learning algorithms are vital. They show what interests different customers or visitors to your website. That allows for more accurate customer segmentation. You can split your prospects based on their interests. That lets you target them with far more relevant marketing material.

    Retargeting is another area where machine learning is invaluable. Algorithms can understand customer behavior to suggest highly relevant retargeting campaigns. For instance, say a would-be customer visited the Bliss website.  

    Machine learning bliss

    That visitor might have browsed the brand’s skincare products for dry skin. They may even have added items from that range to their cart. In the end, though, they didn’t buy. What they did do, though, is provide an email address.  

    Via machine learning, Bliss will see the visitor is a prime target for a retargeting campaign. The firm can then send an email selling precisely the dry skin products it knows the lead is interested in.  

    3. Improve in-house operational efficiencies. 

    Not all benefits of ecommerce machine learning regard customer-facing processes. Algorithms can also deliver real-time insights to help you make your other operations more efficient. 

    Take managing your stock levels and accounting for your inventory as an example. Many brands struggle with the age-old FIFO vs LIFO decision. The best way to choose which method is best for you is by analyzing customer data.  

    Machine learning makes such an analysis swifter and more accurate. A program can crunch the numbers on ecommerce sales, warehousing costs, tax implications, and more. It can also help predict future demand. Thus, you have all the info you need to adopt the most efficient possible processes. 

    4. More informed decisions. 

    Following on from the previous point, machine learning is an excellent tool for improved decision-making. You might need to decide whether dropshipping is right for you. You may be wondering whether there’s consumer interest in a new product line. Whatever the choice that faces you, machine learning can help.

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    How machine learning helps in this area is by allowing all your decisions to be backed by data. Algorithms or programs process and make sense of high volumes of information swiftly. That delivers actionable insights you can use to inform your choices. 

    Use Cases for Ecommerce Machine Learning 

    We’ve looked at the business advantages of machine learning in general terms. Now it’s time to get more specific about the tech’s impact on the online shopping experience. The following are six use cases for ecommerce machine learning.  

    1. Personalization. 

    Today’s consumers don’t want to get treated as one of many customers. They prefer a highly personalized customer experience. 

    It’s that kind of personalization that keeps a customer loyal to your brand. If you can’t provide it, they’ll find a competitor who can.  

    Why should you use machine learning for personalization?

    AI, and specifically, machine learning, is the only way to deliver high-level personalization online. Algorithms analyze customer data and behavior to tailor the user experience to each site visitor. 

    Your site can show each user product recommendations based on their known preferences. Such a recommendation engine is an excellent way to deliver personalized customer experiences. It’s also the tech utilized by hugely successful brands like Amazon and Netflix.  

    2. Site search. 

    Anyone who’s used Google lately can tell you that online search has come a long way. Far too often, though, site searches on ecommerce stores don’t measure up. Unless you know precisely what to type, it can be maddeningly tough to find the products you want.  

    There’s no excuse for that in the age of big data and machine learning. Intelligent algorithms – leveraged correctly – make smart searches a cinch to deliver. 

    Why should you use machine learning for site search? 

    Many visitors to your online store will have an idea of what they need. What they might not know is the name of a specific product. Or even which item would meet their needs. Your site search, then, must be intelligent enough to present the right solution. That’s no matter what gets typed in the search bar.  

    3. Managing supply and demand. 

    When you get down to it, ecommerce, like many areas of business, is about supply and demand. As an online retailer, you must ensure you have the right stock in the correct amounts to satisfy consumer’s needs. 

    Those needs change over time. As such, the more proactive your inventory and supply chain management, the better. That’s why demand forecasting is so crucial to online stores. Being able to predict fluctuating customer needs keeps you ahead of the competition. Machine learning helps you make those real-time, accurate predictions. 

    Why should you use machine learning for supply and demand management? 

    Managing your supply chain is essential to success in the ecommerce sector. Balancing consumer demand with expenses like landed costs and logistics is how to get ahead. Via machine learning, you can crunch all the relevant numbers with ease. 

    By using an AI-powered algorithm, you can perform quantitative forecasting. That means making predictions based on cold, hard evidence. It’s the best way to ensure the forecasts you produce are as accurate as possible. As a result, the inventory and supply chain changes you make in response are more likely to pay off.  

    4. Churn prediction. 

    Customer churn is often discussed in the B2B niche. It’s the rate at which customers abandon a brand – potentially to patronize another instead. It’s worth considering as an ecommerce company, too.

    Machine learning


    It’s quite simply more straightforward to sell to an existing customer. That’s why retention marketing is so valuable to online retailers. But what if you could improve that part of your marketing strategy by predicting the customers most likely to churn? That’s the opportunity afforded by machine learning.   

    Why should you use machine learning for churn prediction?

    Churn prediction is about using data on existing and prior customers to find patterns. What behaviors, for instance, do customers do when they are about to churn? These are the insights machine learning algorithms can deliver. 

    With that knowledge on hand, you can pinpoint the people who may be about to leave you. Then, you can tailor marketing campaigns, by email, social media, or other channels, specifically to keep them on board.  

    5. Fraud detection. 

    In this age of cybersecurity awareness, you may think ecommerce fraud is a thing of the past. Unfortunately, you’d be mistaken. The value lost by online retailers to fraud continues to grow steadily.

    Fraud detection and fraud protection, then, are essential processes for all online stores. Machine learning technology can beef up these processes and make them more efficient.  

    Why should you use machine learning for fraud detection?

    It’s the sheer volume of data machine learning algorithms can process that helps with fraud detection, too. They’re able to analyze customer data when it comes to genuine transactions.

    That means they can pinpoint the hallmarks of an actual purchase. What’s more, they’ll immediately notice a transaction that diverges from the norm. If something about a supposed purchase is off, it’ll get flagged up as potentially fraudulent. That may be if the payment comes from an unusual location, happens on an unverified device, or occurs at a strange time. 

    6. Improved customer service. 

    All ecommerce businesses understand the importance of customer service. Just what is world class customer service, however? In today’s competitive retail world, it’s characterized by delivering customer support both how and when each customer needs it.  

    One way to offer such 24/7, omni-channel support is by taking on a raft of additional staff. Even for the largest brands, though, that’s often not viable. Instead, companies typically seek to boost customer satisfaction via AI and machine learning. 

    Why should you use machine learning for improved customer service?

    Chatbots are amongst the most accessible examples of machine learning in ecommerce. Lots of sites feature a chatbot offering you assistance. For online stores, the tools help with common queries and direct visitors to specific products.

    Where machine learning comes in is when it comes to improving the responses of chatbots. An AI-enabled bot can use the interactions it has to learn and tweak its future replies. The more a chatbot gets used, then, the more human it seems, and the better the information it provides.  

    Steps for Adopting Machine Learning in Your Ecommerce Business

    You should now have a handle on how machine learning can apply to ecommerce. You may even have some ideas for your own online store. That’s great, but how can you get started adopting the tech? The following are six straightforward steps to get you started.  

    1. Get familiar with everything machine learning. 

    Before you can leverage machine learning effectively, you must fully understand its capabilities. That means putting in the time researching the present state of the technology. Look into the AI-enabled solutions around and what processes can get bolstered by machine learning. 

    2. Leverage third-party expertise. 

    If you can’t find all the answers yourself, look to existing experts in the field who can help. You might simply reach out to a pro to give you some general advice. If you’re going to go deep with the tech, you could hire a machine learning engineer. They’ll be able to manage adoption across your organization.  

    3. Identify problems you want machine learning to improve. 

    Before adopting any tech solutions, you must define what you want to achieve. The same goes for machine learning. It’s not enough merely to say you want to streamline your ecommerce store. You must draw up some identifiable goals. 

    For instance, you may find your home page has a high bounce rate. Your aim could then be to reduce that bounce rate with improved personalization. That’s a specific goal that a machine learning-driven solution can help you to address.    

    4. Acknowledge your technology and capability gap. 

    This step is best taken in concert with the previous one. When defining your machine learning goals, take your organization’s capabilities into account. Don’t dream bigger than your staffing or tech resources allow.  

    Many machine learning solutions have comparatively low barriers to adoption. That’s not always the case, however. Full-blown machine learning implementations, moreover, aren’t something to take on lightly.   

    5. Create a team dedicated to implementing machine learning technology. 

    With clear and achievable aims in mind, you can start the process of adopting machine learning. Creating a team devoted to the process will help keep things on track. It avoids putting extra work on your existing staff’s plate. It also ensures implementation gets the attention it deserves.

    Ecommerce Machine Learning


    Some of the tasks this team must handle will include: 

    • Collecting and collating data.
    • Setting up systems to centralize future data collection.
    • Choosing existing machine learning tools or coding unique solutions.
    • Implementing pilot programs of solutions. 

    6. Measure and scale. 

    Any adoption of a new machine learning solution should start on a small scale. Use a new tool or program to analyze a small and specific data-set first. That way, you can test the insights, predictions, or results that arrive. 

    If you’re pleased with the performance of your new application of machine learning, then you can scale up. What’s more, by proving its efficacy at a smaller scale, you’ll get more buy-in from key stakeholders. That will make it more straightforward to get their support for expanding the adoption.   


    Once upon a time, machines that can learn independent of human input were the realm of science fiction. Now, it’s very much a part of everyday life. Machine learning and other AI-driven processes are ubiquitous. And their influence is only growing. 

    If you’re an ecommerce business and you’re not on board with machine learning, you’re getting left behind. The benefits of the tech to your sector, after all, are numerous. From customer experience to inventory management, machine learning can make you more efficient.

    Leveraging solutions in the area is easier than you may think, too. You’ve taken the first step by learning more about the basics of ecommerce machine learning. Now all that’s left is to ID what you want the tech to do for you and set about working toward that goal.    

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    Nick Shaw

    Nick Shaw

    Nick Shaw is the Chief Revenue Officer (CRO) of Brightpearl and is responsible for Global Marketing, Sales and Alliances for the leading retail inventory management software provider. He has written for sites such as Hubspot and G2.

    View all posts by Nick Shaw

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