3 Ways Companies Use AI Recommendation Engines Differently

Post by
Suraj Venkat
3 Ways Companies Use AI Recommendation Engines Differently

You must have come across statements like "Movies You May Enjoy" or "People You May Know," these are forms of recommendation engines. The big companies like video-based Netflix and YouTube, E-commerce-based Amazon have been using this AI technology to attract their consumers to buy more, watch more, or use their platforms more.

With the increase in the number of companies utilizing the recommendation engines, their usage for different purposes has also been increasing. Here, in this article, we will briefly explain the three ways companies are using it differently.

Three Ways That Companies Are applying The Recommendation Engines:

Usage of Predictive Analysis to Improve the Product Offerings:

The best way to lure consumers is to show them just what they would like to have. Wouldn't it be great for the business if we could predict the products our users will prefer? Therefore, companies like Amazon or Myntra use predictive analysis to predict the preferences of the user.

Predictive analysis uses data mining and machine learning to predict future events, that is, provides the owner with a detailed understanding of user behavior and decision. It considers all the factors like the search queries, browsing history, and other variables that form data, which gives an idea for the product offerings.

Updating the Products Prices After Observing the Competitors:

Updating prices based on current marketing needs is a pricing strategy to set flexible prices. This pricing system is called dynamic pricing, which is the future of the eCommerce industry. Dynamic pricing strategy uses big data and Artificial Intelligence to alter its price based on the ongoing pricing trends and competitor prices.

Imagine a scenario where a user is browsing for a particular product on your competitor’s site and finds it too pricey. Therefore, he/she decides to visit your site, in the hope of finding a better range. If you have the AI that can provide you with that data and predict the best price to pull the user to your platform, your chances of growing in the market will increase.

Inventory Management and Demand Predictions:

Managing inventory is not a simple task. You have to look for both the overstocking and understocking factors to manage your inventory. These two critical factors can affect your online shop revenues.

Overstocking means when the companies have a stock of products, which are not in trend and have fewer demands. And obviously, the less trendy the product will be, the fewer chances of buying them. Understocking means the company does not have enough stock of the products that are in demand. Therefore, both factors can cause a considerable loss. This is where AI comes into the scene. Artificial Intelligence helps companies minimize such losses by predicting the products' demands based on the users' previous orders.


Every company's motto is to deliver the best user experience, and AI is helping them achieve that. AI helps predict a user's preferences, the best price of the products, and demands of the goods, and many more.