Machine Learning For E-Commerce: How does it work?

With the advancements occurring in the technology sector at lightning speed, a few terms or rather technologies like artificial intelligence, machine learning, and augmented reality are creating a huge uproar. As we all know, these technologies are used to automate various business operations and make them more efficient. So obviously they seem to offer more value to businesses in comparison to humans.

We are here to discuss machine learning and how it contributes to eCommerce trends. ML technology is a method that utilizes data from past experiences to enhance future performance. Machine learning technology is helping businesses to provide better eCommerce development services that will lead to providing an enhanced customer experience.

Implementing technologies such as ML can make your online business more agile which is effective for generating more revenue. And that's not all, machine learning can be beneficial for eCommerce in more than one way. So, let's discuss how machine learning for eCommerce is a profitable bet.

The best Machine Learning Use Cases in E-commerce

Fraud protection

Detecting fraud is one of the most important tasks in the eCommerce business. And that's where machine learning technology excels the most. It is estimated that credit card fraud caused by criminals is costing merchants $1 billion each year. Machine learning can help reduce this loss by analyzing data and making predictions about whether a transaction is likely to be fraudulent or not.

A common approach for fraud detection is to use a rule-based algorithm that looks for patterns in transactions. This works well for simple rules, but can't handle situations where there are no obvious patterns or there are too many variables at play. Machine learning can help in this case by taking into account more information about each transaction, from how it was made to what kind of device was used to who the user is, and so on.

Product recommendations

Another use case for which ML technology is widely used is to recommend customers the products of their preferences.

You can see this work in action daily, think of Amazon's "Customers who bought this item also bought" recommendations and product comparisons, or Netflix's movie recommendations based on what you've watched. 

Machine learning for product recommendations is about finding patterns in your data that can help predict the preferences of other customers.

Let's say you're selling sunglasses online, but you don't know whether people who buy your products also buy sunglasses from other sellers. With machine learning, you can train a model to make predictions about how many people will purchase your product from a new seller if they buy from you. You could then use these predictions to determine whether those sellers are competitors or partners and potentially even provide them with personalized recommendations based on their past purchases.

Optimized pricing

Optimized pricing, also known as dynamic pricing, is one of the most powerful machine learning use cases in e-commerce. The idea is to use machine learning algorithms to determine the
optimal price for your product based on supply and demand, so customers can find a product at the right price. It's easier said than done.

You need to make sure that you have enough data to train your model and that you don’t overfit your model (that means having too many variables in your model). But once you get the hang of it, you can save customers hundreds or even thousands of dollars per month by using optimized pricing across multiple products.

To use optimized pricing effectively, you need to keep track of what people are willing to pay for each product and when they buy them. This can be done manually with Excel spreadsheets, but we recommend using an automated solution like Amazon Seller Central Analytics (SCA) or Shopify Analytics instead.

Personalization of the content on the website

We all know that e-commerce sites have a huge amount of data, and they can use this information to make their website more personalized and relevant to the user. For example, if you are a doctor and you are looking for a new pair of glasses, then it would be useful for your website to suggest other items which might be suitable for you.

This is achieved with the help of neural networks which is one kind of ML algorithm. The algorithm will learn from your past purchases, so it will know what kind of products you like and dislike. This way, when you come back to shop again, your preferences will be taken into account when searching for new products.

Another example is that if you buy something from Amazon, then there's an option to set up notifications for when it arrives or if there are any issues with shipping (e.g., damaged goods).

These notifications are sent through email or SMS messages but they could also be sent automatically via chatbots or voice assistants such as Amazon Alexa or Google Home.

Smart chatbots to improve customer service

Since their inception ten years ago, chatbots have made remarkable progress and are more advanced nowadays. Today, they are used in almost every industry and on most platforms from messaging apps to e-commerce sites.

They can deliver personalized responses to customers, automate complex tasks and even create new products.

Here's how chatbots can help your business:

Improve customer service: Chatbots can help your customers get answers to their questions faster by automating basic queries that are typically answered by customer support agents. They can also be programmed to ask questions when you need them or provide information on
products or services that relate to the user's query.

Automate repetitive tasks: Chatbots can simplify workflows for customers by automating certain processes that would otherwise require human involvement such as sending out invoices or generating reports. They can also be used for more complex tasks like financial analysis for forecasting sales data through machine learning algorithms.

Create new products: Chatbots aren't just about simple conversations between people; they're also about helping businesses create new products that meet customer needs. For example, if you sell clothing online, you could build a chatbot that helps customers find the right size of clothing based on their body measurements.


Machine learning technologies have made considerable development in recent years and it's still nowhere near their peak. Many online business owners have already implemented ML technologies to increase the efficiency of their eCommerce operations.

And many more online stores have started seeing the value it provides. In almost every aspect of online business, from labor-intensive to costly processes, ML technology can empower eCommerce business owners with meaningful insights, proving itself a huge asset.

You too can use machine learning technologies to bring in more traffic to your site, increase your customer retention rates and maintain strong customer relationships as well. So what do you want to use ML for in your online business?

Author : Joy Anderson

This post is written/compiled by Joy Anderson. Thank you Joy for an article. 

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