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Ecommerce Product Recommendation Engine: What Is It & How Does It Work?

Ecommerce Product Recommendation Engine: What Is It & How Does It Work?

5 mins Read
Ecommerce Product Recommendation Engine: What Is It & How Does It Work?

Variety is the spice of life. 

Having too much variety can get pretty confusing and frustrating. 

Think about how it feels when you browse on Netflix for hours trying to figure out what show to binge on. (because you ignored all the recommendations).

A timely product recommendation saves time, eliminates confusion, and provides a positive customer experience. 

So how can large ecommerce stores keep their customers from overwhelm and confused?

Three words. Product recommendation engines. 

What’s a Product Recommendation Engine?

A product recommendation engine helps guide users on the products they should buy based on demographic data and previous shopping tendencies. 

Product recommendation engines use algorithms and data to recommend the most relevant products to a specific user. They also use a filtering system that predicts and shows the items that the buyer may like to purchase. 

Doesn’t that sound helpful? 

Let’s explore what a product recommendation engine can do for your business. 

What Do Product Recommendations Do? 

Product recommendation engines support KPI’s and intangible goals of ecommerce businesses. 

For example, if configured correctly, product engines boost revenues. Buyers are likely to buy more and make repeat purchases.

The result? 

Your ecommerce business can experience higher Click-through-rates and conversion rates. These metrics positively affect customer experience, impacting intangible goals such as customer satisfaction and retention. 

So are there different types of product recommendation engines? Or is it a one-size-fits-all? 

Curious? Let’s explore more. 

Types of Product Recommendation Engines

Collaborative Filtering

This filtering method is based on collecting and analyzing user information such as buying behavior, activities, or preferences. It involves predicting what they will like based on the similarity with other users.

This filtering method’s main advantage is that it can recommend complex items without understanding the object itself. 

Collaborative filtering is based on the assumption that user behavior is consistent. That means that users are likely to choose similar items to what they chose in the past. 

There are several types of Collaborative filtering: 

User Collaborative Filtering:

In this case, the algorithm recommends products based on what similar buyers have chosen. While this algorithm is effective, it takes a lot of time and data processing resources. 

This type of filtering would require the algorithm to pair off customers based on their information to work effectively. Therefore, it’s not ideal for ecommerce stores with large product catalogs or retailers with an extensive product assortment. 

Item Collaborative Filtering:

While this type of filtering is similar to user-user filtering, the algorithm works differently. Instead of pairing off customers, the algorithm works to pair off products. 

Once a buyer gets one product that’s determined to be “alike,” the algorithm recommends similar products to the buyer. 

Think about how Amazon works.

Shoppers are shown related products when they search for a particular item.   

This algorithm works faster and requires fewer resources. Customers are likely to find what they are looking for faster. It’s therefore ideal for large ecommerce stores.

Content-Based Filtering:

This filtering method uses cookies to track users over multiple visits. 

This type of filtering analyzes user preferences (likes and dislikes), then makes recommendations based on this data. In a content-based recommendation system, the use of keywords to describe items is essential. 

In this method, algorithms recommend products that are similar to what the user has liked in the past. 

For example, think about Youtube recommendations. Youtube will recommend videos and content based on the kind of content a user has interacted with in the past. 

Explore types of product recommendation engines for ecommerce websites.

Hybrid Recommendation Systems

Combining collaborative and content-based recommendations may be more effective for any ecommerce business. 

You can implement hybrid recommendation systems by combining collaborative-based and content-based filtering. Collaborative and content-based predictions are made separately and then combined to create the hybrid approach. You can also unify the methods into one model. 

For example, think about how Spotify curates the “Discover Weekly” personalized playlists. The recommendations made are based on what you like.

hybrid recommendation engine examples

So how do they do this? Through a complex hybrid filtering method that collects data based on your listening habits and similar user’s listening habits. 

Fascinating, isn’t it? 

All three methods use AI-based algorithms to fuel the process and provide personalized product recommendations.

So, do product recommendation engines make sense for your business?

Product Recommendation Engines-Are They Good for You?

The short answer is a resounding YES!


See Also
Composable product configurator guide

Here are some fantastic benefits you get from product recommendation engines:

Improves Personalization 

Product recommendation engines help your business deliver customized and relevant content. These engines help brands personalize the customer experience and make suggestions based on their preferences. 

A product recommendation engine allows you to analyze buyers’ visits to your website and their previous browsing history. This data is useful in delivering relevant product recommendations. 

Shoppers feel known, and this results in a positive customer experience. 

Enhance Sales and Average Order Value

Product recommendation engines encourage your site visitors to buy —and buy more. This allows your business to drive much higher conversions and enhance average order value. Relevant recommendations allow your customers to engage with your products in real time. 

Relevant recommendations expose your site visitors to a higher volume of products that are likely to interest them. 

The result? 

Your visitors are likely to engage in impulse buying. Moreover, you can bundle up products. This can allow you to upsell or cross-sell products across various price and description categories.

When users find what they’re looking for and more relevant products, it enhances their customer experience. 

It also doesn’t hurt your bottom line.

Product Recommendation Engines —The Build or Buy Debate

Product recommendation engines are suitable for your business. 

So should you build a product recommendation engine? Or is it better to buy it? 

The simple answer is to buy. 


AI typically powers product recommendation engines. They also require semantic data and sophisticated content tagging capability. 

It’s also expected recommendation engines will continue to evolve with time—it’s better to keep it all with experts. 

Embrace Your Future

Technology is continuously evolving. Do you want to scale or increase your product offering? 

Product recommendation engines are crucial to the success of any ecommerce business.

The only way to engage with buyers is to communicate with each as an individual. 

Partner with Zoovu today for the right expertise. 

Embrace your future.