{"id":83856,"date":"2022-06-02T13:35:53","date_gmt":"2022-06-02T20:35:53","guid":{"rendered":"https:\/\/thegood.com\/?post_type=insights&p=83856"},"modified":"2025-05-21T09:39:38","modified_gmt":"2025-05-21T16:39:38","slug":"ecommerce-product-recommendation","status":"publish","type":"insights","link":"https:\/\/thegood.com\/insights\/ecommerce-product-recommendation\/","title":{"rendered":"21 Ecommerce Product Recommendation Tips (That Increase Conversions)"},"content":{"rendered":"\n

A timely product recommendation can lead shoppers to choose one product over another. It can be the nudge they need to make a purchase or, at the very least, inspire them to browse a category of products they didn\u2019t even know they needed. <\/p>\n\n\n\n

What is a Product Recommendation? <\/strong><\/h2>\n\n\n\n

A product recommendation is exactly what you think\u2013a recommendation for a product. <\/p>\n\n\n\n

Have you ever made a selection at a local store, based on a product recommendation from the owner or a sales clerk? Has a product recommendation from a friend or family member ever been the deciding factor in your choice of which product to purchase?<\/p>\n\n\n\n

Celebrity endorsements (paid or unpaid), advice given by a current user, \u201cBest 10\u201d lists on blogs<\/a> \u2013 all of these mentions, and more, leverage the power of product recommendations. Looking for the best dried flower bouquet? Look no further than product recommendation lists on Google. <\/p>\n\n\n\n

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As an ecommerce manager, though, you don\u2019t have to wait for someone else to recommend a product to your customers. You can make product recommendations while the prospect is in the process of shopping on your website. Personalized product recommendations can work to improve the user experience as well as the conversion rate of your site.<\/p>\n\n\n\n

In this article, we\u2019ll reveal some of the most effective ways we\u2019ve found to deliver product recommendations to your customers. The information we provide here will help you sell more products more often.<\/p>\n\n\n\n

Guaranteed.<\/p>\n\n\n\n

How Do Ecommerce Product Recommendation Engines Work?<\/strong><\/h2>\n\n\n\n

An ecommerce product recommendation engine is a piece of technology that displays recommended products to shoppers throughout your store. It uses machine learning to get smarter and show increasingly relevant products to shoppers based on their interests and previous browsing behavior. <\/p>\n\n\n\n

While it\u2019s possible to manually implement rudimentary \u201calso-liked\u201d recommendations on your ecommerce site, product recommendations best practices call for the deployment of a \u2018product recommendations engine\u2019.<\/p>\n\n\n\n

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There are three basic approaches used to configure the underlying algorithm:<\/p>\n\n\n\n

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  1. The content-based filtering method<\/strong> analyzes customer data on the likes and dislikes of each user (cookies allow tracking over multiple visits), then makes recommendations based on the browsing history of that user. The idea behind content-based filtering is that if you enjoy a certain item, you\u2019ll likely also enjoy a similar item. An example of a content-based filtering system would be if you were listening to Pandora and consistently \u2018liked\u2019 downtempo jazz music. The filtering system would take that information and begin recommending similar music to you based on the songs you preferred.\u00a0<\/li>\n\n\n\n
  2. The collaborative-filtering method<\/strong> incorporates data from users who have purchased similar products, then combines that information to make decisions about recommendations. The advantage to this filtering method is that it is capable of making complex recommendations on items such as music or movies without having to \u2018understand\u2019 what the item is. This method of filtering operates under the assumption that users will prefer recommendations that are based on purchases they made in the past. Here\u2019s an example: If customer A likes a specific line of products that customer B also likes (assuming they have similar interests), then collaborate-filtering would assume that customer A would like other products that customer B purchased and vice versa.\u00a0<\/li>\n\n\n\n
  3. A hybrid method<\/strong> combines the content-based and collaborative-based methods to incorporate group decisions but focuses the output based on the attributes of a specific visitor. An example of a hybrid filtering system would be how Spotify curates its personalized \u2018Discover Weekly\u2019 playlists. If you\u2019ve ever listened to a personalized Spotify playlist, it\u2019s shocking how accurately they\u2019re able to recommend songs based on what you like. The secret behind how they pull this off is through a complex hybrid filtering system that aggregates data on your listening habits as well as similar users\u2019 listening habits, to create a playlist of unique songs that align with your personal taste.\u00a0<\/li>\n<\/ol>\n\n\n\n

    All three methods use machine-learning algorithms to fuel the process and provide personalized product recommendations. While the mathematical principles behind each are elaborate and complicated, the application to your online store doesn\u2019t have to be overwhelming.<\/p>\n\n\n\n

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    Source<\/a><\/p>\n\n\n\n

    Why Are Product Recommendations Important? <\/strong><\/h2>\n\n\n\n

    Is the product recommendations process really worth the trouble? Isn\u2019t the incorporation of machine learning a bit beyond the scope of all but the largest ecommerce websites?<\/p>\n\n\n\n

    Those are the types of questions we often hear. There are times when it seems the high-tech movement is going too far, and machine-learning algorithms are a prime example of that complaint.<\/p>\n\n\n\n

    Given the potential benefits, though, the argument often settles itself. When a tool proves itself sufficiently valuable, the question moves from \u201cWhy?\u201d to \u201cHow?\u201d.<\/p>\n\n\n\n