A Novel Way To Boost Online Sales With Recommendation Engine - The Next Tech

A Novel Way to Boost Online Sales with Recommendation Engine

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by Alan Jackson — 4 weeks ago in Business Ideas 2 min. read
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As more people opt to shop online because of the broader range of items available and accessible from different countries, the competition becomes fiercer. As a result, e-commerce businesses search for and utilize various apps to ensure heightened customer experience, improve customer loyalty, and increase revenue. The situation applies to the global retail e-commerce industry.

As e-commerce sites compete for buyers’ attention, one of the newer tools they implement today involves using artificial intelligence to recommend products to their customers. In addition, businesses use customer demographics through data analysis and other consumer metrics because shoppers today are more challenging to attract and retain. Thus, they must have the elements, tools, and features that satisfy the preferences of online shoppers.

Also read: 10 Best Android Development Tools that Every Developer should know

What is a recommendation engine?

The novel e-commerce tool they call a recommendation engine is helping e-commerce sites attract more customers. When set up correctly, it can positively affect one of the essential features of e-commerce: customer experience.


A recommendation engine makes the life of an online shop better. The tool filters data and uses algorithms combined with collected data to recommend relevant products to a specific shopper. It works as an automated form of a floor sales staff. For example, a customer looks for or asks for a product. Instead of showing only the product requested, the recommended engine automatically adds related products. In addition, the tool can take action to assist individual customers better by using various attributes, past transaction history, and customer preferences. In short, the tool is programmed to upsell and cross-sell.

The objective of a recommendation engine

The primary goal of a recommendation engine is to accelerate demand and engage users actively. It is a part of the personalization strategy of e-commerce. With the help of AI, the engine automatically populates emails, apps, or websites with relevant products, thereby increasing customers’ shopping experience.

How does the tool work?

A vital element of the recommendation engine is its recommender function. This feature considers the specific user information and automatically predicts the rating the consumer can give a product. This particular feature gives more power to the recommendation engine. With the help of techniques and specialized algorithms capable of supporting large product catalogs and using its orchestration layer, the recommendation engine selects the filters and algorithms it will apply to the current customer.

Its recommending process involves four stages. Those are:

  • Collection: It collects the information the user or customer willingly provides, including comments, product ratings, or information related to a customer’s page views, history of the order and return, and shopping cart activities.


  • Data storage: The organization decides on the types of data storage, such as object storage, standard SQL database, or NoSQL database.
  • Data analysis: The tool analyzes the data and looks for the items with the same user engagement date. It uses filters by employing various analysis methods, such as near-real-time system analysis, real-time analysis, or batch analysis.
  • Filtering: The recommender system filters the data to identify relevant information and provide the specific user with recommendations.

Using a recommendation engine can help your online business succeed. It is a powerful tool for e-commerce. Thus, you should set it up correctly to correlate all available data from the product to the customer.


Alan Jackson

Alan is content editor manager of The Next Tech. He loves to share his technology knowledge with write blog and article. Besides this, He is fond of reading books, writing short stories, EDM music and football lover.

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