E-commerce Machine Learning Use Cases: Examples

In e-commerce, machine learning can be used to improve a number of decisions thereby resulting in creating a positive business impact. Not only does it help e-commerce organizations increase conversion rates and find the best deals for their customers, but it also helps them understand the customer better. This blog post will look at various different use cases where AI/machine learning and deep learning have been used in eCommerce.

What are some key machine learning use cases in eCommerce?

Here are some key areas in eCommerce where AI/machine learning can be leveraged:

• Product recommendation: One of the key use cases where machine learning has been used is to provide product recommendations for ecommerce websites. eCommerce businesses rely on product recommendations to drive more sales. Product recommendation helps increase onsite engagement and also boosts ecommerce conversions significantly. A product recommendation system helps increase sales because it provides personalized recommendations for each customer based on their purchase history & preferences (price range, color preference, size, style preference, etc.). It also allows eCommerce brands to upsell/ cross-sell existing customers through personalized promotions which are otherwise not possible without knowledge of shopping. Recommendation engines are designed based on a collaborative filtering algorithm that uses historical purchase data of customers who bought similar products or their buying patterns. There are different algorithms like user-based, item-to-item matrix factorization which can be used depending upon the business requirement. This helps eCommerce platforms provide more relevant product suggestions to their customers. A few examples include Amazon’s ‘Customers Who Bought This Item Also Bought’, Flipkart’s ‘Recommended For You’ feature among others. One of the challenges in product recommendation is the lack of traceability of users if he/she is not logged in. In e-commerce websites, given that the users might be untrackable as they are not logged in, and they can have rapidly changing shopping preferences, providing recommendations based purely on the interactions that happen in the current session is an extremely important and challenging problem. Many methods have been explored that leverage the sequence of interactions that occur during a session, including session-based k-NN (k-Nearest Neighbours) algorithms like V-SkNN and neural approaches like GRU4Rec. In addition, the approaches based on transformers architectures based on NLP techniques have demonstrated significant advantages over sequential and CNN-based approaches. T
• Pricing recommendation: Pricing is an important aspect of eCommerce and machine learning can be used to recommend the best prices for products. This use case was famously implemented by Netflix when it recommended different rental prices for its users based on their past viewing behavior. There are various ways in which machine learning can be used for pricing recommendation such as regression analysis, gradient boosting, Bayesian optimization, etc.
• Fraud detection: Fraudulent activities are a big concern for ecommerce businesses and machine learning can be used to detect them early. Machine learning models can be trained to identify typical patterns associated with fraudulent activities like abnormally high order values or customers placing orders from new IP addresses. Some eCommerce platforms like Alibaba & Amazon have built-in fraud detection systems that use machine learning algorithms.
• Delivery time prediction: eCommerce businesses face the challenge of accurately predicting delivery times for their products. This is important to ensure that customers are not left disappointed and also to manage expectations properly. Machine learning can be used to predict delivery times by taking into account factors like order size, item weight, customer location, etc.
• Personalized shopping experience based on customer segmentation: eCommerce businesses can use machine learning to identify different customer segments and then personalize the shopping experience for them. Customer segmentation can be done on the basis of demographics (location, age, gender), psychographics (lifestyle, interests), or buyer persona (needs & wants). Amazon does this very well with its ‘Customers Who Bought This Item Also Bought’ feature which recommends similar items based on a customer’s purchase history.
• Automated eCommerce chatbots: Chat is one of the most popular ways to engage with eCommerce customers and this use is expected to grow by 30% over the next few years. Machine learning can be used in eCommerce chatbot systems for a number of purposes such as personalizing the shopping experience, answering customer queries, generating reports & alerts based on user behavior, etc. There are cloud tools such as Amazon Lex, Amazon Kendra, Google Dialogflow, Azure bot engine, etc., which can be used to build your custom chatbots.
• Demand forecasting: eCommerce businesses like e-commerce marketplaces face the challenge of accurately forecasting demand for their products. Forecasting helps eCommerce companies to manage inventory, plan logistics & warehouse space accordingly, and also decide on pricing strategies. Machine learning can be used in eCommerce demand forecasting systems by taking into account factors like historical data (item sales volume), seasonality, holidays or festivals calendar, weather conditions, etc.
• Campaign optimization: eCommerce businesses use marketing campaigns to attract new customers and increase sales. Machine learning can be used to optimize these campaigns by identifying the most effective channels (email, SMS, social media) & target audience for each campaign. It can also be used to determine the best time of day or week to run a particular campaign.
• Product comparisons: Product comparison engines are a special case of a product recommendation system, in which a product detail page displays alternative choices in a table containing informative product specifications. There can be three different aspects of such a product comparison system including the following: A. Fast retrieval to narrow down products B. Further, shortlist the product from narrowed down results with high precision C. Rank the result. Fast retrieval can be achieved through algorithms such as K-NN over the embedding space.
• Product categorization: eCommerce businesses need to categorize their products so that they can be easily found by customers. This is often done using machine learning algorithms such as k-means clustering, latent Dirichlet allocation (LDA), or support vector machines (SVM). These algorithms can be used to automatically group similar products together based on factors like product features, customer reviews, etc.
• Predicting customer churn: eCommerce businesses face the challenge of predicting which of their customers are likely to churn i.e., stop doing business with them in the future. This is important for eCommerce companies because it allows them to take proactive measures to prevent churn and retain more customers. Machine learning can be used in eCommerce churn prediction models by taking into account factors like customer purchase history, demographic information, social media data, etc.
• Product search: eCommerce businesses rely on product search engines to help their customers find the products they are looking for. Product search engines use a variety of techniques to rank products including indexing of product data, ranking algorithms, and evaluating user feedback. Machine learning can be used in product search engines to improve the accuracy of results by taking into account factors like the popularity of a product, its price, customer reviews, etc.
• Product Question & Answering system: eCommerce businesses can use e-commerce chatbots to answer questions about the product like how it is different from another similar item or specific questions related to the products and their attributes. Customer spends a lot of time looking for answers in relation to a specific product on the product page and this is where chatbots can reduce this time providing valuable information in question and answer format.
• Customer lifetime value (LTV) prediction: Measuring customer future purchases and lifetime value is an important performance indicator for marketing management and budgeting. When the customer and firm have no formal agreements, this approach is nearly impossible. As a result, future purchases must be forecast based almost entirely on previous purchases. There are different solution approaches including statistics-based solutions and machine learning based solutions that can be used to solve the problem of predicting future purchases. A series of probabilistic models also termed “buy-till-you-die (BYTD)” models link simple past purchase summary statistics with a theoretically well-grounded behavior. The Pareto/NBD model, which assumes a Poisson purchase process and an exponentially distributed lifetime, is the most widely known BTYD framework. Then there are machine learning algorithms such as random forest which can be used to predict customer purchases and lifetime value.

eCommerce Machine Learning use cases are a great way to improve the e-commerce customer experience and drive more sales. There are several types of machine learning use cases such as product recommendation, product search, customer churn prediction, product categorization, etc. eCommerce businesses can also utilize chatbots for answering questions about the products they sell as well as using machine learning algorithms to predict future purchases or lifetime value of their customers that have no formal agreements with them. If you like to learn more, please feel free to reach out.