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Using Collaborative Filtering in E-Commerce

Instructor: Beth Hendricks

Beth holds a master's degree in integrated marketing communications, and has worked in journalism and marketing throughout her career.

Collaborative filtering delivers recommendations to website visitors. In this lesson, you'll learn more about this filtering technique and how it's used.

Personalized Recommendations

Imagine you're visiting an e-commerce website. As you browse, you see various products that the website has chosen to recommend to you as items you might be interested in or might want to purchase. You're struck by how relevant some of the items are, and even add a few to your online shopping cart.

Collaborative filtering can help retailers generate product recommendations to consumers.
Collaborative filtering can help retailers generate product recommendations to consumers

But, how did the website know what your likes and interests are? How did they choose those products, and how did they magically appear when you launched the webpage? Welcome to a little tool called collaborative filtering.

What is Collaborative Filtering?

Collaborative filtering, according to the website Techopedia, is a 'technique commonly used to build personalized recommendations on the Web.' The process matches the purchase patterns of one shopper to similar shoppers, or it might make recommendations based on your prior online purchases or browsing history, or the purchases of individuals who have shopped for similar items. Collaborative filtering is an example of a new wave of customer segmentation - that is, using buyer behaviors to divide consumers into groups that share similar purchasing or browsing habits.

How It's Used

The purpose of collaborative filtering is to make recommendations to website visitors that the retailer or website believes will entice the customer into taking action - whether buying a product similar to a previous purchase, purchasing an item that other customers have bought, adding a new contact on a social networking site who is friends with some of your other friends, or, perhaps, checking out a book or movie title that is recommended to you based on previous reading or watching or is in a similar genre to one you have expressed interest in.

Collaborative filtering looks for similarities either between you and other users or between items, such as those previously purchased or browsed for, and makes recommendations based on those similarities. The more relevant a recommendation or suggestion, the more likely you are, as a consumer, to take an action for that product or service. Personalized recommendations create a more targeted marketing effort toward earning and retaining consumers.

Collaborative Filtering in Action

There are a wealth of e-commerce sites now employing collaborative filtering to provide recommended products or services to their customer base.

Think about popular online streaming or movie rentals websites. Have you noticed how these sites recommend movies to you that they think you'll enjoy? They are using collaborative filtering based on either your previous movie-watching history or the history of people who have liked or watched movies similar to the ones you enjoy.

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