Machine learning in ecommerce: 9 use cases reshaping online shopping

Machine learning in ecommerce: 9 use cases reshaping online shopping

July 7, 2022

Andrea Di Stefano

Technology Research Analyst

If we asked anyone for their opinion on the explosive growth of ecommerce, they would probably take a protective mask out of their pocket and wave it in front of our noses.

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. Based on Statista’s estimates, indeed, the worldwide retail ecommerce 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 way before the COVID-19 epidemic. Why? Well, one of the key reasons is to be found in the growing implementation of artificial intelligence and its sub-branch, namely machine learning (ML), experienced by this sector in recent years.

Let's frame the nature and use cases of this technology to understand how the combination of machine learning + ecommerce is reshaping the way we buy and sell on the web and why online businesses should consider involving machine learning consultants into their ecommerce project.

Machine learning in ecommerce: an overview

Machine learning in ecommerce represents a key trend of retail digital transformation and involves the adoption of self-learning computer algorithms that can autonomously improve their performance through experience.

Specifically, machine learning algorithms can be trained by processing huge datasets, spotting recurring patterns, relationships, and anomalies among all this data, and building mathematical models representing such correlations.

These models are refined as the algorithms process more and more data and offer us valuable insights into certain ecommerce-related phenomena and the interconnections among all the variables beneath them. Something that has proven extremely useful in analyzing current events, forecasting future trends, and making data-driven decisions.

Business benefits of machine learning in ecommerce

But how can we use the extraordinary capabilities of machine learning for the ecommerce industry and which benefits can we expect to get from its deployment? Here are some of the most relevant applications and payoffs.

  • Personalization: Tailoring the shopping experience and connecting customers to their favorite merchandise through recommender systems, targeted ads, and smart search engines.
  • Interactivity: Implementing highly interactive solutions, such as chatbots and contextual shopping features, both to provide non-stop customer support and boost sales.
  • Logistics: Optimizing inventory management through demand forecasting and streamlining product delivery via real-time analytics and self-driving vehicles.
  • Security: Ensuring safe transactions, user data protection, and therefore regulatory compliance via machine learning-powered fraud and anomaly detection tools.

The impact of machine learning in numbers

+ 5-15% revenues

+ 10-30% marketing-spend efficiency

By implementing ML-based product recommendation systems, according to McKinsey's 2019 The Future of Personalization paper.

+ $1.7 trillion (12.39% of total sales)

The value that the retail industry is expected to create thanks to AI and machine learning, based on McKinsey's insights.

+ 25% customer satisfaction, revenue or cost reduction

The potential achievements of most organizations using AI and machine learning for digital commerce by 2023, according to Gartner.

Top use cases of machine learning in ecommerce

From marketing and customer care to logistics and security, machine learning in ecommerce is paving the way for a wide spectrum of innovations in a variety of business functions. Let's find out how machine learning is leveraged in these use cases, starting with one of its most famous and impactful incarnations.

1. Recommendation engines

Nowadays, machine learning-based recommendation systems represent a marketing staple among all major ecommerce platforms and online stores. These 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.

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 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.

Content-based filtering

The other possible approach, adopted among others by Amazon, is collaborative filtering. This 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.

Collaborative filtering

2. Targeted advertising

Before offering products to users of an ecommerce platform, it may be necessary to drive these users towards the aforementioned portal. To perform this routing process, potential customers can be segmented, namely classified into subgroups based on shared characteristics, and then targeted with personalized ads according to a variety of parameters. These include 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.

All this data can be processed by machine learning algorithms to analyze users' behavioral patterns and forecast the products or services they may like.

Major variables of market segmentation

In this regard, it’s worth mentioning the example of Macy's, a major American department store chain that leveraged ML-powered predictive analytics software to drive its marketing campaigns and benefitted from a 10% growth in online sales within the first three months.

3. 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 site search and those chosen to describe the products in stock.

Combined with other AI-related technologies such as deep learning and natural language processing, machine learning 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 site 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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4. Pricing optimization

Attracting potential customers with fancy ads won't work if the merchandise you sell is much more expensive than what they're ready to pay. That's why several ecommerce platforms and online stores leverage machine learning to optimize their pricing strategies and enhance personalization with tailored discounts or other promotions.

This technique, usually referred to as dynamic pricing, involves periodic and customized price adjustments (even every few minutes, as for Amazon) based on personal user data, pricing history of similar products, sales trends, competitors' offers, demand compared to supply, and more. The aim, as you may expect, is to maximize revenue while minimizing customer churn.

5. Chatbots and virtual assistants

Customization is not the only secret to improving the digital customer experience and increase customer lifetime value. 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 support).

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

6. Contextual shopping

Another significant example of machine learning in the ecommerce industry, specifically applied to route users towards products while offering them a more interactive customer 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.

7. Trend analysis and restocking

When it comes to the logistical aspect of ecommerce predictive analytics, machine learning can play a pivotal role in inventory and supply chain management. Indeed, its forecasting capabilities can be leveraged to match supply and demand, particularly by predicting upcoming ecommerce sales trends and setting up proper restock planning. This can also come in handy for optimizing the catalog, therefore reducing space utilization at the warehouse, the maintenance cost of products and the risk of food spoilage.

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. These typically include economic trends, purchase patterns driven by seasonality or celebrations, social media sentiment, product reviews, ratings, and many more.

All this information can be gathered from a variety of data sources, such as socials and ecommerce platforms or online stores. Furthermore, if your ML-solution is integrated with POS solutions, it can analyze inventory data, and, in case some product is going to run short, notify a team that they need to replenish stocks.

8. Delivery optimization and autonomous vehicles

Since we’re talking about logistics, machine learning in retail also proved useful to facilitate product distribution via delivery optimization solutions and self-driving vehicles.

As for the first point, the idea is to process data from prior cases 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. Another aspect of delivery optimization concerns the actual shipping process, which can be accelerated through machine learning-based route planning. This implies analyzing real-time traffic data and weather conditions, along with drivers' experience and performance, to recommend the fastest route.

Amazon, on the other hand, has gone a step further by upgrading the "optimized shipping" approach with an even more innovative "anticipatory shipping" system. The ecommerce leader relies on machine learning algorithms to keep an eye on its customers' purchase habits, predict their future orders, and therefore transfer these products to a closer warehouse. This means that Amazon will be able to deliver such items in one day and with standard, relatively inexpensive shipping methods as soon as the customer actually orders them.

Regarding the other form of machine learning for product delivery, namely self-driving vehicles powered by ML algorithms, deep learning, and computer vision, it is worth pointing out that, for now, we are still in the realm of experimentation, rather than at a full implementation stage. However, several pioneering companies like Amazon and Kroger are investing in these technologies with rather promising results.

9. Fraud detection

As the world abruptly shifts toward 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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Starting with machine learning in ecommerce

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 some useful guidelines:

  • Select the right tool: To enhance your e-store, you can invest in a fully personalized machine learning-based solution built from scratch, benefit from the embedded tools of major online platforms (especially if your business already relies on Amazon, eBay, etc.), or opt for off-the-shelf, SaaS solutions like Granify and Deloitte’s Trellis.
  • Complement technology with expertise: Nowadays, most ML-based solutions for customer analytics, pricing optimization, and demand forecasting are carefully designed to foster usability with user-friendly interfaces. Still, their implementation may require additional competencies that should be developed through proper training, along with the creation of centers of excellence to coordinate their actual deployment.
  • 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 a good first investment to cross the gates of digitization.
  • Supervise your machines: ML-driven systems suffer from the so-called black box problem, as no one knows exactly how machine learning algorithms 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.
  • Comply with data legislation: 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.

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 worse, over-deployed, it may end up being annoying like flashy, importunate ads on social networks.