Product recommendation engines

by Reemi Shirsath Chief Operating Officer

A product recommendation engine can also be defined as a filtering tool which runs on technologies such as machine learning, data analytics and deep learning to provide purchase suggestions that match the prospective clients' tastes and interests as accurately as possible. The success of the engine or the recommender systems is highly dependent on the accuracy of predicting suggestions.

Type of Product Recommendation Engine:

Collaborative Filtering:

Content-Based Filtering:

Hybrid Filtering:

Benefits of Product Recommendation Engines:

Improve Traffic:

A recommendation Engine allows you to send personalized emails and promotional offers to individual customers due to collected data

Customised Content:

Since time immemorial customers like nothing more than shop staff knowing their specific taste. Personalized recommendations gives them a sense of privilege. Recommendation engines help replicate the same sentiment on e-commerce sites by showing personalized product recommendations through data collected in real time.

Engage Shoppers:

Other than a sense of privilege customized content also engages the viewer more efficiently than random suggestions. When suggestions are in tune with his/her tastes chances are high that the customer will be interested.

Increase conversions:

An ecommerce recommendation engine is undoubtedly a body double for a salesman in an online store. It subtly urges the shopper to have a look at products he or she could be interested in. By showing a variety of products according to the viewer's interest, it helps them make a purchase.

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