ML use cases in Ecommerce

E-commerce — is one of the first industries that started using all the benefits of machine learning. Nowadays, there are machine learning applications for almost every area of e-commerce.

Machine learning solutions for e-commerce really helps-from inventory management to customer service.

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.

Some key machine learning use cases in eCommerce are-

1. 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).

2. 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-commerce 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).

3. 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.

4. 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.

5. 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.

6. Delivery time prediction

E-Commerce 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.

7. Easier checkout

A study conveys that 21% of online shoppers in the US?have abandoned an order just the checkout process took forever or was complicated. An easier checkout would not only improve the sales of products on the eCommerce website but also ensure that customers get a smooth shopping experience. Amazon has already entered this zone with its Amazon Go app that creates the experience of ‘Just Walk Out’?Shopping.

8. Handling fake reviews

A customer’s buying decision is influenced by the reviews they read online on any eCommerce platform. There are multiple cases where there are negative reviews of the product, But what if those reviews are prompting your customers to refrain from buying that product? This activity is called astroturfing?and there are many eCommerce players that take the help of AI to tackle fake reviews so that the customers can make an unbiased decision.

9. Smart chat bots to improve customer service

An intelligent chatbot based on NLP and AI can interpret individual user’s 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.

Conclusion

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. As machine learning algorithms for e-commerce continue to develop, they will continue to be of great benefit to the e-commerce industry.