Glossary

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Intelligent product recommendation

Intelligent product recommendation

Intelligent product recommendations are AI-driven systems that are used by eCommerce businesses to offer their shoppers better product recommendations based on their past purchases and behavior. In the same way that brick-and-mortar stores have salespeople to help recommend and find products for you, eCommerce businesses now have intelligent product recommendation systems. 

Many companies use these systems because offering customers smart recommendations is a great personalization strategy that helps enhance customer’s user experience, increase customer retention and loyalty which ultimately leads to an increase in conversion

How it Works 

To give a basic understanding of how these product recommendation systems operate we can break down their functions into 4 parts - Data collection, data storing, data analysis, and finally filtering. 

The system will begin by collecting data that will help understand the customer’s behavior. This will include information about past purchases, items that have been liked or added to a wishlist, ratings, comments left, viewed products, clicks, and more. Once this data is collected it is then stored, as the system collects and stores more data it allows the data to then be analyzed to come to a probable assumption of what the customer is interested in. Once the data is collected it will then be analyzed this can either be done in real-time to give immediate recommendations as soon as data is collected or by using past data to later offer a recommendation. Finally, the data will be filtered and grouped either based on similar features of products the customer is interested in or based on the customer’s actions. I.e clicks, pages visited, etc. 

These recommendation systems are used by eCommerce companies for quite a few aspects of their websites such as - 

  • On the homepage to show recommended products and personalized offers. For example, Netflix’s homepage recommendations. 
  • On product pages to show customers more products that are similar to the ones they've been looking at to make them browse more, 
  • In carts before check-outs companies will show similar, complementary, or products that are frequently bought together to get people to make an impulse decision and add to the cart before checking out. 
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Intelligent product recommendations are AI-driven systems that are used by eCommerce businesses to offer their shoppers better product recommendations based on their past purchases and behavior. In the same way that brick-and-mortar stores have salespeople to help recommend and find products for you, eCommerce businesses now have intelligent product recommendation systems. 

Many companies use these systems because offering customers smart recommendations is a great personalization strategy that helps enhance customer’s user experience, increase customer retention and loyalty which ultimately leads to an increase in conversion

How it Works 

To give a basic understanding of how these product recommendation systems operate we can break down their functions into 4 parts - Data collection, data storing, data analysis, and finally filtering. 

The system will begin by collecting data that will help understand the customer’s behavior. This will include information about past purchases, items that have been liked or added to a wishlist, ratings, comments left, viewed products, clicks, and more. Once this data is collected it is then stored, as the system collects and stores more data it allows the data to then be analyzed to come to a probable assumption of what the customer is interested in. Once the data is collected it will then be analyzed this can either be done in real-time to give immediate recommendations as soon as data is collected or by using past data to later offer a recommendation. Finally, the data will be filtered and grouped either based on similar features of products the customer is interested in or based on the customer’s actions. I.e clicks, pages visited, etc. 

These recommendation systems are used by eCommerce companies for quite a few aspects of their websites such as - 

  • On the homepage to show recommended products and personalized offers. For example, Netflix’s homepage recommendations. 
  • On product pages to show customers more products that are similar to the ones they've been looking at to make them browse more, 
  • In carts before check-outs companies will show similar, complementary, or products that are frequently bought together to get people to make an impulse decision and add to the cart before checking out. 
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