4 Machine Learning Applications in Ecommerce


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Data growth has exploded bringing along challenges of developing data driven infrastructures. Be it an e-commerce industry where customer data should remain intact and secure. Or any other industry, where data insights have fruitful results.

Initially, there was a lot of noise around AI and machine learning applications. Now, realistically companies understand that neither ML solves all the problems nor can it be understated.

E-commerce industry got boom with the advent of AI and ML as purchases boosted with time. That’s the main reason why retailers are more interested in gathering detailed customer data.

Imagine a company has handsome yet properly jot down customer data. They can easily customize their marketing efforts and achieve maximum return on investment.

Customer experience is the whole point. Where they land first, how they navigate through the product pages and how they react to offers? All can easily be modified for better results if machine learning applications are used wisely.

Customer product recommendation

You must have visited Amazon once or any other retail website out of interest. There, you will find products as you like after applying filters. After the website learns user purchasing behavior, it starts recommending such as, ‘if you like ‘x’ product, you may also like…’

Collaborative and content-based filtering is used on customer navigation patterns.

Image result for amazon product recommendation

Estimated relevance for search results

ML has features like search ranking which allows products showcase on the basis of estimated relevance. If a customer finds it hard in reaching out for a product, efficiency of the ecommerce portal has a question mark.

The relevance estimation depends on the frequency of the used term, previous product views, age range, - customer profile as a whole.

Dynamic product pricing

To understand the concept, dynamic pricing means high prices when product is in high demand, low prices when the demand is low. However, there are plenty of other variables for estimation of optimal pricing. For instance, competitors price, stock of warehouse, time of a day etc.

Chatbots for customer support

Customers often get irritated of not getting any responses and hence change their minds for a purchase. Chatbots integration can help customers in need. For example, chatbots have information of product prices, range, sizes and for which occasion the product may be of use.

Machine learning automates the process through robots and can answer phone calls. Whereas in previous old systems, there was always a limitation and only few customers were dealt with at a time. Chatbot can answer to hundreds of queries all at the same time.

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