Skip to content
Home » Blog » Best Practices For Personalized Product Recommendations In e-commerce

Best Practices For Personalized Product Recommendations In e-commerce

  • by
personalized product recommendations

An eCommerce site needs many features to boost sales. These range from the best product photos to the best payment options and videos. You can use the best AI product photo generators and AI video generators to produce high-quality product photos and videos for your eCommerce site. There are also great payment apps you can integrate into your eCommerce site i.e., Shopify Payments if you have a Shopify store. 

You’ll also need personalized product recommendations on your site to offer your potential site visitors recommendations that are in line with their interests. The odds of a site visitor buying something from your site increase if they can see products that resonate with their interests, age, gender, etc. That’s where a personalized recommendation system comes in! 

What Are Personalized Product Recommendations?  

If you’ve visited an eCommerce site before and seen a product recommendation that is in line with products you are interested in or similar to products you have bought before, that is a great eCommerce personalized example.  

An eCommerce site doesn’t just give product recommendations. A product recommendation app must be installed on a site to provide recommendations that match the user behavior, profile, interests, etc., of individual visitors.  

How AI-powered Product Recommendation Engines Work  

The best upsell and cross sell apps like Candy Rack are powered by Artificial Intelligence. They use powerful AI and machine learning algorithms to predict the products shoppers are most likely to like or buy based on current/past user experience on the site and/or profile information.  

7 Tips for Personalized Product Recommendations in Ecommerce 

Now that you know how useful a personalized product recommendation could be to your site, it’s time to discover what’s in the best product recommendation apps/systems. There are countless apps out there today. What makes some stand out? What tips are the most important when using personalized product recommendations? 

1. Collect and Analyze Relevant Customer Data 

Since personalized product recommendation systems are heavily reliant on data, it helps to gather as much comprehensive data as possible about site visitors. This should, of course, be done within stipulated guidelines. Examples of important data that will help you know customer journeys and increase the effectiveness of your product recommendation system include browser behavior, demographics, and purchase history. 

Knowing what products your customers buy, look at, their age, gender, the specific pages they check, etc., is critical in creating the most accurate personalized recommendations that have a high chance of generating actual sales. Some of the Personalized Product Recommendations Apps would help. For example, you can use the Octane quiz personalization app to create quizzes using AI that allow customers to answer the questions, then display the best product(s) for their needs.

2. Real-Time Recommendations 

It also helps to have real-time recommendations i.e., popups that appear during a site visitor’s browsing or shopping session. Product recommendations won’t work if they don’t capture a site visitor’s attention. While popups may interrupt a customer’s personalized shopping experience, they will be appreciated if they recommend something that a customer is interested in.  

Ecommerce personalization pop-ups

3. Context-Aware Recommendations 

Context can’t be overlooked when communicating. You are also bound to offer your potential and current eCommerce customers better relevant recommendations if they are context-aware i.e., they consider factors like a site visitor’s location, time of day, device they are using, etc.  

4. Diversity in Recommendations 

Since product recommendations operate on probability, it’s important to have a good variety. While AI and machine learning technologies are incredibly powerful in predictions, they aren’t 100% foolproof. The programs are as good as the information/data they use to make predictions. To increase the odds of making great predictions and boost the existing customer base, it helps to have a diverse range of recommendations.  

Over-reliance on trending items or recommendations based on collected data and previous purchases may miss out on serendipitous suggestions that introduce customers to relevant products that they wouldn’t normally consider. For instance, an glass shopper can be shown cleaning kit or glasses repair products. 

Ecommerce personalization example

5. Incorporate Social Proof 

It also helps to have product recommendations that are backed by good ratings or reviews. Other social proof includes products in use or showcased by known persons. The recommendations can also contain user-generated content.  

The importance of reviews can’t be overlooked. While having typical verifiable reviews on a homepage helps with customer retention, you can take it a notch higher and include them in product recommendations to establish trust, customer loyalty and increase the likelihood of engagement, as online shoppers are more likely to use products that have a positive social validation. 

6. A/B testing and Experimentation 

The best-personalized product recommendations are also tested before being used. A/B testing helps to test real-time with real customers how a specific personalization strategy will perform. A/B testing focuses on metrics like conversion rate, click-through rates, shopping carts abandonment rates, actual sales, etc. Before you decide which product recommendation algorithm to run, it must have been tested and proven to be effective on the bottom line – boosting sales. 

7. Customer Feedback & Iteration 

Lastly, the best-personalized product recommendations systems in eCommerce have a way of gathering customer preferences and feedback and using that information to improve recommendations. In some cases, an individual customer may not be interested in a product recommendation that pops up. They should be able to say why for whatever reasons. Recommendation systems should then pick up such feedback and use it to offer better recommendations. This can be through automatic changes to an algorithm to meet changing needs.  


Personalized recommendation systems are a critical feature in any eCommerce site. As your current and potential customers browse on your site, you should create opportunities for upselling by recommending other products. The above information gives the best practices to follow regarding personalized product recommendations. 

In a nutshell, there must be a way for your program to collect and analyze data. It also helps to offer real-time recommendations when site visitors are in a buying mood. While at it, don’t forget about context, offering diverse product recommendations, utilizing social proof, and testing product recommendation algorithms before putting them to work. Lastly, collect customer feedback on your recommendations and make automatic improvements. 

There are obviously other best practices like incorporating email marketing, however, the above tips are bound to set you apart from most eCommerce sites using custom product recommendation apps.

Leave a Reply

Your email address will not be published. Required fields are marked *