Machine learning in ecommerce: 10 use cases, examples, and best practices

Similarly to recommendation systems, this technique embraces customization to drive sales. Potential new customers can be segmented and targeted with tailored ads according to a variety of parameters, like 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. Then, machine learning algorithms process this information to frame customer behavior and purchase patterns, forecasting the products they may like.

Traditional search engines integrated into ecommerce stores match the keywords used for the site search and those chosen to describe the products in stock. Enhanced with deep learning-based natural language processing capabilities, however, these tools can achieve way deeper context understanding. For example, an ML-driven engine can take into account a broader range of synonyms. It can also adjust the site search process in real time by prioritizing some results according to each user's purchase habits and taste.

Several ecommerce platforms and online stores leverage machine learning to optimize their pricing strategies and personalize discounts or other promotions. This technique, usually referred to as dynamic pricing, involves periodic and customized price adjustments (even every few minutes, in the case of Amazon) based on personal user data, pricing history of similar products, sales trends, competitors' offers, demand compared to supply, and more.

Compared to standard, rule-based bots which can only solve simple queries, AI chatbots powered by natural language processing also understand the context in which they operate and, above all, learn from experience, i.e. from previous interactions with other users. This enables chatbots to maintain a realistic conversation and completely replace humans in a variety of customer support tasks. For example, they can clarify available shipping options, ask questions to understand customer preferences, and offer coupons depending on their responses.

Another significant example of machine learning in the ecommerce industry, specifically applied to route customers towards products while offering them a more interactive experience, comes from contextual shopping solutions. Powered by machine learning and computer vision technologies, these applications identify and highlight specific products or brands that appear in online videos. Sellers can embed links to the related product page, enabling users to purchase such items without leaving multimedia content.

ML can help retailers match supply and demand by predicting upcoming ecommerce sales trends and planning out restocking. This also comes in handy for optimizing the catalog, therefore reducing storage utilization and the risk of food spoilage. The variables considered include economic conditions, seasonality-driven purchase patterns, social media sentiment, product reviews, and ratings. All these data points come from various sources, such as social media, ecommerce websites, or POS solutions.

Machine learning in retail and ecommerce facilitates product distribution via delivery optimization solutions. They process data from prior cases and suggest the best expedition methods and conditions (such as free or one-day delivery) according to customer expectations. As for actual shipping, the process can be accelerated through machine learning-based route planning. These solutions analyze real-time traffic data, weather conditions, and drivers' experience and performance to recommend the fastest route.

Another embodiment of machine learning aimed at improving product delivery involves deploying self-driving vehicles powered by ML algorithms, deep learning, and computer vision. However, we are still in the realm of experimentation, rather than at a full implementation stage. Still, several pioneering companies like Amazon and Kroger are investing in these technologies with rather promising results, especially to speed up last-mile deliveries and minimize their costs.

Many companies have already turned their gaze to ML as a potential weapon to protect their ecommerce portals from a wide range of criminal actions, including identity theft and fraudulent electronic payments. ML algorithms can spot recurring patterns among the data they process, but also notice if something "breaks the rules". Indeed, ML in fraud detection is commonly deployed to detect anomalies among the credit card accounts under scrutiny (such as an increasing frequency of transactions) that may be signs of fraudulent attempts.