The Best Machine Learning Use Cases in e-Commerce

E-commerce was one of the first sectors to embrace machine learning at scale. Today, modern AI solutions for the retail industry support almost every area of online commerce, from inventory management and customer service to personalization, dynamic pricing, fraud detection, and demand forecasting. Companies such as Zalando and ASOS invest heavily in AI and deep learning to better understand their customers, improve shopping experiences, and automate manual processes. You can explore more examples in our guide to machine learning solutions for e-commerce.

Recommendation engine and machine learning in the e-commerce industry directly convert into profits and increases the company’s market share with better customer acquisition.

Addepto machine learning consulting team has analyzed which solutions have the biggest potential today. You can find 9 machine learning applications in e-commerce below. They can help monetize your data and improve customer experiences like Asos and Zalando:

1. Recommendation engine (recommender system)

Machine Learning in e-commerce has few key use cases. Personalization and recommendation engine is the hottest trend in the global e-fcommerce space. With the use of machine learning algorithms for e-commerce and the processing of huge amounts of data, you can thoroughly analyze the online activity of hundreds of millions of users. On its basis you are able to create product recommendations, tailored to a specific customer or group (auto-segmentation).

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How does the recommendation engine work in e-commerce?

Let’s see how the recommendation engine in e-commerce works. By analyzing collected big data on the current traffic on websites, you can determine which sub-pages the client used. You could identify what he was looking for and where he spent most of the time. This is one of the most common examples of how big data solutions in e-commerce help businesses personalize customer experiences and improve commercial performance.

Moreover, based on various information: profile of previous customer activity, its preferences (e.g. favorite color), social media data, location and weather – results will be displayed on a personalized page with suggested products that will most likely interest them.

Companies like Netflix, YouTube, Amazon, etc. use recommendation engines based on Python that easily interact with their microservices. Recommendation systems also allow you to carry the experience of other customers and recommend products that are bought by people who live near the customer. And it’s only the first example of how machine learning in e-commerce can successfully work.

This same behavioral data is also what powers remarketing. Machine learning algorithms can analyze each customer’s behavior and devise a retargeting campaign that encourages a potentially-lost customer to come back and place an order. In many cases, remarketing is the cheapest and most straightforward way to recover a nearly-abandoned shopping cart.

2. Personalization of the content on the website

Properly personalized content on the website or mobile application increases conversion and customer engagement. The selection of the best content is possible thanks to machine learning algorithms for e-commerce. Thus, algorithms could find patterns in the data based on the processing of a large amount of structured and unstructured data (including images and text).

Machine learning algorithms in e-commerce take into account various factors such as favorite style and color, image intensity, activity history, preferences, etc. The results on the website are adapted to each individual person’s personal preferences. In this way, recommendations for using machine learning in e-commerce could help you increase your revenues.

3. Machine Learning for dynamic pricing in e-commerce

Machine Learning in e-commerce can be very helpful in case of dynamic pricing and can improve your KPI’s. This helpfulness comes from ML algorithm ability of learning new patterns from data. As a result, those algorithms continuously learn from new information and detect new demands and trends.

This is why online retailers could use ML models in the e-commerce industry for dynamic pricing. Instead of simple price markdown. E-commerce companies could benefit from predictive models that can allow them to determine the best price for each particular product. You can choose the offer, the optimal price and display real-time discounts, which will also take into account the state of the warehouse.This is done to maximize sales and to optimize inventory.

In fact, Amazon is still the market leader in the area. The company is making significant progress in the use of machine learning for dynamic pricing. Furthermore, they change prices every 10 minutes, which is fifty times more than Walmart and Best Buy, resulting in a 25% increase in profits. [4]

4. A/B tests using AI

A/B tests enable the product (e.g., website) to be adapted to consumers. Almost 80% of the A / B test variants do not yield positive results. Conducting this process is very hard and laborious, which is why the algorithms of machine learning for e-commerce will definitely help you with:

• Process automation of selecting platform`s (product`s) features that should be changed through the use of a genetic algorithm. That is based on the best-suggested changes to the product that an algorithm may offer. For example, noticing that the bigger “BUY” button on the page increased sales by 1% so we can check whether its further enlargement may improve the results.
• Automatic customer segmentation into groups using unsupervised Machine learning models for e-commerce depending on their characteristics (age, gender, expenses, preferences, etc.) and personalization of the content (product for their needs). For example, for women over 40, the main color of the page will be burgundy, while for men under 20 years old, it will be blue.
• Faster finding optimal options of pages / products through the use of self-learning AI algorithms instead of repetitive and tedious work. Machine learning in e-commerce allows online retailers to shorten orders of magnitude from months to days.

5. Predictions using Machine Learning in E-commerce

• Predicting whether a given user will make a purchase in a specific product category in real time – so that the seller can react accordingly (eg, call that person or send email with engaging content). It gives you the opportunity to increase conversions while the customer, for example, is considering buying.
• Predicting whether the user will be returning and what purchases he will make at certain times. This will help in matching the right marketing message to that person to increase the conversion of the future purchase and to encourage the person to return.
• Customer lifetime value prediction (CLTV or LTV) – to predict how much money a particular user will spend in your shop. Accurate estimation of the future customer value allows effectively allocate marketing expenses, identificate and care for high-value customers and reduce exposure to losses.
• Customer churn prediction will discover customers who are risky to leave. The implemented machine learning solution in e-commerce will allow you to react quickly to the customers who will probably stop buying from you. Such a system will increase retention rate and will bring you a stable stream of revenue.
• Prediction of client’s size – personalized size recommendations reduce the chargebacks for both the company and customers. Predictions using machine learning in e-commerce reduces company’s or customers’ costs and definitely increases customer satisfaction.
• Prediction of demand for specific product categories – this will help to meet all customer needs and trends in the future. This will cause customers to be happy to return to your online store where most of the goods are available and can be bought immediately. These same forecasting techniques are also widely used for sales and order prediction in e-commerce, helping retailers optimize inventory and purchasing decisions.

6. Image processing

Retailers invest in AI and image recognition systems to influence customers (buyers) behavior and also for process automatization. Investment into computer vision technology with visual search possibilities could help you to match customers photos e.g. with similar clothes sold online. This could be defined by user’s preferences based on the category of products the person usually buys (what color, what brand) and based on the data from social media (eg Instagram, twitter, facebook, vkontakte).

Another machine learning application in e-commerce could be automatic completion of information about the subject on the basis of the photo (what is the article, what category to add it, what color it has).

7. Improving the quality of the search engine using Machine Learning in E-commerce

Users use search engines to quickly find what they need. They have less and less time and patience to formulate queries, wait for results and analyze them. That is why there is a need for personalized results of search queries.

A personalized search engine could play an increasingly important role. It is based on machine learning models with short-term and long-term user preferences, history or previous queries. In addition, such search engines are able to increase the user’s conversion better than non-personalized search engines based on traditional information retrieval (IR) techniques.

This is especially important for giants like eBay.

With over 800 million items on its website, eBay uses artificial intelligence and data to predict and represent the most relevant search results.

8. Smart chatbots to improve customer service

An intelligent chatbot based on NLP and AI can interpret individual users’ questions and respond to them individually. The role of virtual assistants is to imitate the best consultants to be able to help the users of e-stores in the purchase process in the most effective way. For example, help in getting to the products, suggest the best pricing solutions, carry out through the transaction process.

“8 out of 10 consumers who have engaged with a chatbot, report it as an overall positive experience.” – Hiconversion.com

9. Fraud detection

The cost that online stores lose due to fraud continues to increase steadily. Therefore, fraud identification and protection are important processes for all online stores. Machine learning algorithms for e-commerce can improve these processes and make them more effective.

10. Common pitfalls to avoid when using Machine Learning in e-commerce

Machine learning can bring real gains to an online store, but using it carelessly can also cost you money and time. A few practical mistakes are worth watching out for:

• Ignoring seasonality. Many e-commerce businesses experience seasonality that can severely affect results — for example, sales spike during Black Friday and Christmas. Ignoring that trend can be a costly mistake, such as spending most of your annual marketing budget during the holiday peak and leaving almost nothing for the rest of the year. If your business experiences seasonal or other temporary fluctuations, always account for them in your models.
• Using one ML algorithm for all problems. Machine learning is not an all-in-one tool. Each problem and each goal you want to achieve requires its own model, trained and adjusted for that specific purpose. Treating machine learning as a single universal fix tends to lead to disappointing results.
• Overtrusting predictions. Every machine learning output comes with some level of uncertainty. It’s a common mistake to state with 100% confidence that action X will result in Y — in reality, there is usually more than one plausible outcome, and predictions should inform decisions rather than replace judgment.
• Being too aggressive with marketing. This isn’t strictly a machine learning issue, but it affects how ML-driven personalization and retargeting are perceived. Don’t overwhelm customers with excessive banners and ads; keep promotions subtle and avoid interrupting the customer while they’re placing or finishing an order.

Case studies of Machine Learning in E-commerce

According to a recent Juniper Research research, investment on machine learning in the e-commerce industry would increase by 230 % between 2019 and 2023, with 325,000 retailers worldwide using machine learning algorithms in some form by 2023. [1]

eBay: Machine Learning for language translation

eBay, one of the largest e-commerce platforms in the world, has created a technology called eBay Machine Translation (eMT). This eMT system translates product names with 90% accuracy. Thanks to this machine learning solution, the company’s total sales increased by 10.9%. [2]

“Machine translation at eBay is key in promoting cross-border trade. Our technology helps overcome language barriers and lets buyers order items from foreign countries” – Evgeny Matusov, eBay’s Senior Manager of Machine Translation Science. [2]

Anheuser-Busch: Using machine learning in e-commerce for optimization route planning

The brewing giant has developed a machine learning platform for daily plan routing. As a result, the company noted an increase in productivity and efficiency a couple of months later. The machine learning algorithms for e-commerce also take into account the collective experience of drivers to offer the best delivery time for each customer. [3]

American Eagle Outfitters: Machine learning in e-commerce for visual search engine

American Eagle, a well-known clothing brand, is partnering with Slyce, a promising image recognition startup. Through its mobile app, Slyce provides a visual search engine that allows customers to search for specific clothing based on photos captured by their handheld device’s camera. [5]

To sum up

E-commerce is an industry where machine learning applications directly affect customer service and business growth. With machine learning applications in e-commerce, you can create business benefits for each department of your e-commerce business.

Moreover, improve customer service, increase efficiency and productivity, improve customer support, and make more informed HR decisions. As machine learning algorithms for e-commerce continue to develop, they will continue to be of great benefit to the e-commerce industry.

Check our case studies to see how e-commerce companies use machine learning and deep learning. Drop us a line and we will explain to you how to use Machine Learning in E-commerce and how companies could benefit from it. We are at your service!

Also check out our machine learning services to learn more.

This article is an updated version of the publication from Jun 23, 2021.

References

1. Data-axle.com. 4 case studies on how Machine Learning is helping retailers drive revenue.
   URL: https://www.data-axle.com/resources/blog/4-case-studies-on-how-machine-learning-is-helping-retailers-drive-revenue/. Accessed June 23, 2021.
2. Ebayinc.com. eBay’s Machine Translation Technology Breaks Down Borders. URL: https://www.ebayinc.com/stories/news/ebays-machine-translation-technology-breaks-down-borders/. Accessed June 23, 2021.
3. Towardscience.com. Disruption in Retail — AI, Machine Learning & Big Data. URL: https://towardsdatascience.com/disruption-in-retail-ai-machine-learning-big-data-7e9687f69b8f. Accessed June 23, 2021.
4. Icicletech.com. Changing the e Commerce Game: 8 Ways AI and ML are Disrupting Online Shopping. URL: https://www.icicletech.com/blog/ai-ml-changing-ecommerce. Accessed June 23, 2021.
5. Datadriveninvestor.com. Machine Learning Applications in Retail: 6 Real World Examples from Market Leaders. URL: https://www.datadriveninvestor.com/2019/07/11/machine-learning-applications-in-retail-6-real-world-examples-from-market-leaders/. Accessed June 23, 2021.