Personalized Product Recommendation Tips and Stats

Product recommendations can multiply profits. 


Unfortunately, many eCommerce companies install a simple plugin and leave it at that. The truth is... not all recommendations are the same.


We've found that detailed, personal recommendations vastly outperform generic ones. To be successful, you need sophisticated product recommendation engines that are able to make sense of shopper's web behavior. 

Select below to see how personalized product recommendations work, statistics on how effective they are, or best practices when implementing your own personalized content strategy.

Note: this page was updated on September 7th, 2020 to reflect the latest findings on product recommendations, content personalization, and their effect on eCommerce sales. 

Personalized, predictive product recommendations: examples & how they work

We covered in detail how advanced product recommendation engines work here.

However, in brief, I like how Amazon details how their recommendation engine works. 

Creating a predictive, retail product recommendations system

Barilliance helps eCommerce store's create predictive, effective product recommendations with machine learning and AI capabilities.

Here is how it works. 

Step 1: Collect data to base personal recommendations on

Personalization depends on customer data. 

Barilliance incorporates three major sources of data to create personalized product recommendations. 

They are:


1.Aggregated data (category/product views, adding to cart and purchase data, internal search queries, etc.)

2.User specific data that is used to personalize the recommendations. Similar to aggregated data, user data is the specific user interactions such as which categories and products the user viewed, bought, etc.

3.Static product data that is supplied by the client in the product feed. Product feed data typically includes price, availability, brand, tags, and other product attributes.

Step 2: Use AI to determine which algorithm to use based on user's context

To create effect personal product recommendations, Barilliance uses a variety of machine learning optimized algorithms.

Our AI technology selects which algorithm to use to fill the product recommendation widget based on who the user is and in what context they are viewing your site. 

To illustrate, take the home page experience.  

The visitor could either be a new visitor or a returning visitor

If the user hasn't visited the site before, than a series of best selling products will be displayed. 

However, if the visitor is returning, visitors will see personalized recommendations based on their previous engagement with your brand such as:


-    Products related to their recently purchased items
-    Products related to their recently viewed products
-    Top sellers from their recently viewed categories

Step 3: Overriding machine learning in select cases (merchandising rules)

Finally, you have the ability to define merchandizing rules for any number of demographic or behavioral segmentations.


We covered merchandizing rules in our article [Guide] Advanced Product Recommendation Tactics to 3x Revenue.


From there, we shared how:

"the best engines allow retailers to "overrule" the software's recommendations in lieu of explicit merchandizing rules you set up.

Examples include:

  • Restrict recommendations to only show full priced items
  • Avoid brand conflicts on particular product pages
  • Prioritize transitioning season items
  • Prevent low in stock items from being shown

Create Personalized Product Recommendations with Ease: Create compelling offers and increase AOV with Barilliance's AI and machine learning powered recommendation engine. Request a demo here.

Personalized product recommendation statistics: conversion rates and more

To demonstrate how effective personalized product recommendations are, we've gathered data on how recommendation widgets impact eCommerce stores across the customer journey. 


Below we look at statistics for: average order value, revenue, conversion rates, and shopping cart abandonment rates. 

Personalized Product Recommendations Statistics on Average Order Value

Personalized Product Recs Increase

Personalized product recommendations dramatically increase AOV (average order value)


Sessions that do not have any engagement with recommendations have an AOV of $44.41. 


This number multiplies by 369% when prospects engage with a single recommendation. The effect continues to climb until tapering off around 5 clicks.


It is clear that the more personalized and engaging recommendations are, the more stores benefit from larger purchase orders. 


*Note: This study went across multiple industries. The significance of this study is not the nominal amount, but the relative increase. 

Personalized Product Recommendations Statistics on Revenue

We conducted a study across 300 randomly selected customers. Here's what we found. 


Product recommendations account for up to 31% of eCommerce site revenues.


On average, customers saw 12% of their sales attributed to our product recommendation product. 

“Product recommendations account for up to 31% of eCommerce revenues. On average, customers saw 12% of their sales attributed to our product recommendation product” - Barilliance Research

Personalized Product Recommendations Statistics on Conversion Rates

Personalized Product Recommendations effect on conversion rate

We also found that product recommendations increase conversion rates


Above, we see the conversion rate of sessions increase in lock-step with their engagement.  


Again, the biggest improvement occurs at the first click. Prospects who do not engage with recommendations convert at 1.02%. That number increases 288% after a single interaction. 


Our findings fell in line with a similar study conducted by SalesForce. They found shoppers that clicked on recommendations are 4.5x more likely to add items to cart, and 4.5x more likely to complete their purchase.

Personalized Product Recommendations Statistics on Shopping Cart Abandonment

Personalized Product Recommendation Effect on Cart Abandonment

Lastly, recommendations have a significant effect on shopping cart abandonment. 


Here, we defined cart abandonment as sessions that completed a purchase divided by the total sessions that prospects added an item to their cart. We then segmented these numbers by how they engaged with recommendations in that session. 


We found that sessions that did not engage at all with recommendations, but simply added an item to their cart were much more likely to abandon their purchase. 


In fact, implementing personalized product recommendations can improve cart abandonment by up to 4.35%. 


Lastly, it is interesting to note that the effect on cart abandonment reverses after a certain level of engagement. This makes sense when you consider buyer behavior - especially those in the research phrase that use recommendations to find products.

Tips for Effective Personalized Product Recommendations 

1. Put Product Recommendations Above the Fold

Position of product recommendations influence how effective they are. We found widgets placed above the fold were almost twice as effective (1.7x) as widgets below the fold.  

2. "What Customers Ultimately Buy" Widgets are the highest performing

Out of the 20+ product recommendations types that were reviewed in this study, the most engaging recommendation type  was ‘what customers ultimately buy’.

3. Use "Best Selling" Recommendations for new visitors

When a new visitor comes to your store, you don't know what products to recommend.


The best practice is to supply the best sellers of your store toward the top. You can also consider having multiple widgets, one for each of your top categories. 


As customers engage with your site, your product recommendation engine will begin to understand what types of products this customer is interested in, and supply more personalized suggestions.


4. Personalize Product Recommendations Based on Web Behavior

Position of product recommendations influence how effective they are. We found widgets placed above the fold were almost twice as effective (1.7x) as widgets below the fold.  


This falls in line with our findings on dynamic content that increases conversion rate.

5. Inject Personal Recommendations into Emails

Another great way to personalize emails is via product injections. Software like Barilliance can inject product recommendations directly into the email.


The widget is tailored to reflect the products each customer is most interested in. Below is a great example of tailoring suggestions based on gender.

Below is an infographic we built with some of the key product recommendation stats we found. 

Product Recommendation Statistics

Next Steps...

Product recommendations serve as the foundation for your eCommerce personalization strategy.


The next step to increase conversions is to build out more advanced personalization tactics.

Lastly, to see if Barilliance is the right product recommendation engine for you, schedule a brief demo with us.