Machine learning in ecommerce changes online shopping

Machine learning in ecommerce changes online shopping

June 28, 2021

Andrea Di Stefano

Technology Research Analyst

If we asked anyone for their opinion on the recent explosive growth of ecommerce, they would probably take a protective mask out of their pocket and wave it in front of our noses. At least, anyone who has been living on this planet for the past two years.

To be fair, that would sound like a pretty good explanation, as the global pandemic certainly acted as a massive catalyst for the shift to online retail. In this regard, research seems to confirm the obvious assumption that many people would share after having experienced curfews, quarantines, social distancing, and long queues to access supermarkets.

Based on Statista’s estimates, indeed, ecommerce sales accounted for 11% of the total U.S. retail sales in 2019, jumping to 14% in 2020. The extent of this peak can be easily spotted also by taking into account global data. Again according to Statista, the worldwide retail e-commerce market grew from $ 3,354 billion in 2019 to $ 4,280 billion in 2020, an annual increase never recorded before.

Global retail ecommerce market growth, 2014-2020

However, the graph above also shows us how the positive trend enjoyed by eсommerce started well before the COVID-19 epidemic. Why? Probably because the success of this new approach to shopping derives not only from a health crisis but also from the entire technological substrate that has allowed this massive transformation of our daily habits. And a significant component of that substrate is represented by artificial intelligence and its sub-branch—machine learning.

Let's see how these innovations are reshaping the way we buy and sell on the web, and why online businesses should consider involving machine learning consultans into their ecommerce project.

A customized shopping experience

Between 2011 and 2012, Amazon enhanced its purchasing processes with a new recommendation system based on machine learning algorithms. The first effect of its integration was an astonishing 29% increase in sales (equivalent to about $12.83 billion) recorded in the second fiscal quarter of 2012.

But considering such a sales spike as the main benefit ensured by Amazon’s machine learning implementation would be simplistic. After all, this is just a consequence (more than welcome, actually) of something far wider and significant, namely machine learning's ability to connect buyers with the products they want. Including those they didn't realize they wanted.

In traditional shopping, this routing process to guide purchasers towards the right merchandise is based on the interaction between the customers and the sellers. Shopkeepers can ask what customers are looking for, understand their preferences, address their concerns, and offer the right products based on all the information collected and a mix of gut and sales experience.

In technical terms, sellers carry out a simplified, homemade version of the marketing process known as customer or market segmentation, i.e. the classification of potential buyers into sub-groups based on shared characteristics. Then, according to this classification, they offer customized recommendations by targeting customers with the products that best suit their needs.

Major variables of market segmentation

Unlike physical stores, however, ecommerce portals don't have the luxury of leveraging sellers' experience to operate these two essential tasks, namely segmentation and targeting. And that's where machine learning comes in as part of retail digital transformation.

Machine learning and targeted recommendations

Machine learning is a discipline within the vast realm of AI, specializing in creating computer algorithms that can autonomously improve their performance through experience. Specifically, machine learning algorithms can be trained by processing huge datasets, spotting patterns among all this information, and building mathematical models representing such patterns.

These models, which are refined as the algorithms process more and more data, offer us valuable insights into certain phenomena and the interconnections among all the variables beneath them. Something that has proven extremely useful to analyze current events, forecast future trends and make data-driven decisions. But how can we apply the extraordinary capabilities of machine learning to ecommerce?

Well, for example, we can leverage algorithms to segment customers and target them with the right offer, somehow replicating the customized experience offered by human sales assistants. That’s what happens with machine learning-based recommendation systems, a rather popular marketing solution among major ecommerce platforms.

Recommendation tools can process past sales data, recognize recurring purchase patterns among typical buyer archetypes, and predict the items that might grab the attention of specific users to provide them with personalized suggestions—a mechanism typically used on a larger scale for predictive analytics in marketing.

According to McKinsey's 2019 The Future of Personalization paper, product recommendation systems can easily increase revenues by 5-15% and marketing-spend efficiency by 10-30%. However, not all recommendation engines are the same. Indeed, they may follow two distinct approaches known as content-based and collaborative filtering, as well as some sort of a hybrid system combining them.

Content-based filtering vs collaborative filtering

Content-based filtering provides recommendations by focusing on two main factors:

  • The items’ features, such as their price or category. In this regard, each product is described by assigning it some keywords.
  • The characteristics of customers, which basically means their purchase preferences and reviews on previously purchased items.

Following this approach, the machine learning algorithm will check a customer's purchase history and recommend other products with similar characteristics to those already bought.

Unfortunately, such a mechanism suffers from some significant drawbacks. First, it requires scrupulous cataloging of all the merchandise present on the ecommerce platform. Secondly, it is a rather "conservative" approach as it tends to suggest the same types of products already purchased previously. And finally, it may have a hard time providing effective suggestions to new users without historical data relating to them.

Content-based filtering

The other possible solution to segment customers and target them with customized offers is via collaborative filtering. This approach, adopted among others by Amazon, is based on the idea that people with similar preferences in the past will still agree in the future. Hence, an algorithm that follows this strategy will suggest to a customer some new products which have already been ordered and positively rated by other users with similar buying patterns.

In this case, what matters are not the characteristics of a product per se, but the perception that the community has of this product. There are many ways to measure this perception, such as asking users to rate items, probing the merchandise that shoppers check on the ecommerce platform, and so on. The main advantage of this system over the previous one is that collaborative filtering does not require a tedious description of each item in the online store.

However, machine learning-powered recommendation systems using this approach have their flaws too.

  • Cold start: once again, it may be difficult to provide recommendations for users with limited or no purchase history.
  • Scalability: relating such massive amounts of variables (basically millions of customers and millions of products) requires considerable computational power.
  • Sparsity: the most important ecommerce corporations offer a huge variety of merchandise. This means that there may be a relatively small amount of reviews for each product and therefore a not-so-accurate recommendation choice.
Collaborative filtering

More ways to tailor online shopping

When it comes to personalizing the shopping experience and connecting customers to their favorite merchandise, the role of machine learning in ecommerce is not limited to recommendation engines. As you may expect, the same logic examined above can be applied to other aspects of marketing.

  • Targeted advertising: Before offering products to users of an ecommerce platform, it may be necessary to attract these users to the aforementioned portal. To accomplish this, potential customers can be targeted with personalized ads based on their social media interactions, previous online purchases (including those made in virtual stores), Google search history, and other types of big data used in ecommerce. As previously described, all this data can be processed by machine learning algorithms to analyze users' behavioral patterns and forecast the products or services they may like.
  • Smart search engines: How could customers reach the desired products without an efficient search engine? This may sound like a minor problem until we are dealing with a huge stock of merchandise with literally millions of products to choose from. Traditional systems rely on matching the keywords used for the search and those chosen to describe the products in stock.Machine learning algorithms, combined with other AI-related technologies such as natural language processing, can empower search engines with a deeper degree of context understanding. For example, a machine learning-driven engine can take into account a broader range of synonyms. It may also adjust the search process in real time by prioritizing some results according to each user's purchase habits and taste.
Machine learning-driven smart search engine

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Bots and other interactive solutions

Customization is not the only secret to improve the digital customer experience. Another ace in the hole of machine learning is its ability to greatly enhance the quality of interactions between users and online shopping platforms.

The most striking example of this synergy between machine learning and eсommerce is certainly that of chatbots and virtual assistants, an increasingly widespread tool across every industry and deployed in an infinite number of AI use cases (especially those related to customer care).

The ability of modern chatbots to interact with humans relies on natural language processing and deep learning, the newest and most powerful variant of machine learning. These technologies allow chatbots not only to solve simple queries (as in the case of standard, rule-based bots) but also to understand the context in which they operate and, above all, to learn from experience, i.e. from previous interactions with other users.

This results in the ability to maintain a realistic conversation and offer 24/7 customer support. For example, a chatbot can clarify available shipping options, ask questions to understand customer preferences and offer coupons depending on their responses.

How chatbots operate

Contextual shopping

Another significant example of machine learning in ecommerce, specifically applied to route customers towards products while offering them a more interactive shopping experience, comes from contextual shopping solutions.

Powered by machine learning and computer vision technologies, these applications can identify and highlight specific products that appear in online videos, giving users the ability to purchase those items without leaving multimedia content. Itransition offered machine learning consulting and development services to build such a solution for the video ecommerce platform provider AiBUY.

A couple more noteworthy use cases

Let's conclude this overview with two additional ways to take advantage of machine learning in ecommerce, related to logistics and anti-fraud measures respectively.

Restocking and delivery optimization

When it comes to the logistical aspect of ecommerce predictive analytics, machine learning may play a double pivotal role. First, its forecasting capabilities can be leveraged to match supply and demand, particularly by predicting upcoming sales trends and setting up proper restock planning. Therefore, if your ML-solution is integrated with different types of POS, it can analyze inventory data, and, in case some product is going to run short, notify a team that they need to replenish stocks.

In this regard, you might think about the interconnection between purchases and holiday seasons, but it's not always that simple. Luckily, machine learning-driven systems can certainly have a broader picture of what is going on compared to "mere mortals", as they’re able to take into account an immense number of variables (such as economic trends, customer behavioral patterns, social media sentiment, and more).

Second, machine learning in retail proved to be a useful tool for delivery optimization, processing data from prior cases to select and suggest the best expedition methods and conditions according to customer expectations. For example, a machine learning-based eсommerce platform may decide to offer free or one-day delivery as an incentive to purchase if this option is valued as a deciding factor.

Fraud detection

As the world abruptly shifts towards a full digitalization, online fraud and other cybercrimes become an increasingly widespread reality. In such a context, ecommerce platforms can be easy prey for hackers and scammers.

That's why many companies have already turned their gaze to machine learning as a potential weapon to protect their ecommerce portals from a wide range of criminal actions, including identity theft and fraudulent electronic payments.

As we've said earlier, machine learning algorithms can easily spot recurring patterns among the datasets they process. But this implicitly means that they can also notice if something "breaks the rules". Indeed, machine learning in fraud detection is commonly deployed to detect anomalous behaviors among the credit card accounts under scrutiny (such as an increasing frequency of transactions) that may be signs of fraudulent attempts.

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3 tips to do things right

In a changing world, it pays to embrace change. And if there's one industry that's evolving faster than the wind, that industry is ecommerce. However, when it comes to implementing machine learning in ecommerce, it might be worth adopting this powerful technology while taking into account three useful tips:

  • Start with chatbots: These tools offer a massive boost to the user experience and customer care without requiring massive investments, as you can build your bot on already-existing solutions or develop one from scratch. So, consider it as a good first investment to cross the gates of digitization.
  • Keep an eye on your machines: Machine learning-driven systems suffer from the so-called black box problem, as no one knows exactly how they come to their conclusions. This means that they may end up behaving in unexpected ways or showing some inconvenience.
    For example, a recommendation engine might over-refine until it completely stops promoting low-selling products. Put simply: check and fine-tune your system regularly.
  • Use data wisely: Alongside the growing data traffic, the regulation in this regard has also increased significantly. Think about initiatives such as the EU GDPR, carried out to provide an official framework for data protection.
    Any machine learning solution for ecommerce should be developed in strict compliance with such rules, also by virtue of the growing sensitivity among users regarding personal data management and cookie intrusiveness. Many newer companies decide to avoid the regulatory burden associated with machine learning implementation and allow ecommerce SaaS providers to handle that. 
3 tips to implement ML in ecommerce

In short, treat machine learning in ecommerce like web advertising for your online shopping platform. Once implemented wisely, it will prove to be an invaluable tool. If deployed poorly or, even worst, over-deployed, it may end up being annoying like those flashy importunate ads on social networks.