Does Your E-Commerce Website Need a Recommendation Engine?

Trying to create an immersive, user-friendly experience is one of the key aspects to having a successful e-commerce store.

You want users to feel welcome.

You want them to spend lots of time on your site.

And, of course, you want them to buy products.

E-commerce recommendation engines are everywhere today, helping website users find relevant products with ease. But how do you know when it’s time to integrate an engine with your online store? Read on.

Regardless of what your e-commerce business is promoting - books, clothing, backpacks - placing what matters most to your clients directly in front of them is critical to your success.

Let’s take a closer look at how a recommendation engine fits into a helpful e-commerce store experience, and when you should use one.

What Is a Recommendation Engine?

A recommendation engine is a program that offers product suggestions to customers shopping in your e-commerce store. The system collects data from the user on any number of parameters, such as:

  • View or search history
  • Previous purchases
  • Demographics
  • Likes or dislikes

An algorithm in the program analyzes these data points and generates a list of specific products that are most likely to appeal to the user.

Some of the biggest online retailers use recommendation engines, with astonishing results. Case in point: Amazon’s recommendation engine drives as much as 35% of purchases on their website.

Netflix’s recommendation engine goes even further, with a whopping 75% of content watched on its platform coming from suggestions delivery by its algorithms.

Spotify and YouTube use similar systems to identify new music and videos for their customers, while social sites like Facebook and LinkedIn make suggestions to connect people based on who they know, similar likes, and (in LinkedIn’s case) past companies they’ve worked for.

Two Types of Recommendation

There are two main approaches to generate product suggestions: collaborative filtering and content filtering. Recommendation engines can use one of these methods or a combined approach.

Collaborative Filtering

Collaborative filtering recommendations are based on comparisons between the user and the preferences of other users who might have similar tastes or interests.

For example, let’s say you digitally buy the movies Titanic, Sleepless in Seattle, and Inception, and rate all three very highly. Another person also purchases Titanic and Sleepless in Seattle and enjoys both as well. Based on these similar interests, the second individual would be shown Inception as a suggestion for them to buy.

Content Filtering

Content filtering recommendations are based solely on the user and their previous search or purchase history.

Here, the recommendations are more straightforward. Relating it to our above scenario, say you buy Titanic and Inception because you enjoy Leonardo DiCaprio movies. Your recommendations might then include other Leonardo DiCaprio movies, such as Catch Me If You Can or The Revenant.

Combining the Engines

So which better filtering method yields the best results?

A hybrid recommendation engine that combines aspects of both collaborative filtering and content filtering may be your best choice.

Collaborative filtering recommendations leverages the ability to comb big data sets to make connections between seemingly arbitrary products which makes recommendations more diverse, while content filtering allows for a more focused and historical approach which offers a customized filter view.

Utilizing both filtering options can assist in providing a unique and diverse, yet seemingly familiar, set of products to choose from.

Benefits of a Recommendation Engine

There are several key benefits to using a recommendation engine, a couple of which we’ve already hinted at.

User Experience

The main advantage of a recommendation engine is what it does for the user experience.

If you want to remain competitive in a tightening marketplace, you can’t fall behind the competition. Many consumers today expect a personalized experience, meaning that a recommendation engine is becoming more of a need than a want for store owners.

By filtering out what your customers do and don’t like, you'll streamline the shopping experience, getting them to the products they want to see faster.

Not only that, but a well-designed system introduces items that clients may not have considered in the past. Recommendation engines present users with a deeper opportunity to select varied items, given the automated visibility that filters provide, along with a product’s relation to their historical interests or purchases.

Here, the suggestions tap into a consumer’s joy of discovery – finding and exploring something new. Ultimately, this leads to more time on your site and takes us to the next major benefit of a recommendation engine.


One of the most valuable aspects of making product suggestions to your customers is engagement.

As we’ve discussed, recommendation engines keep your customers engaged on your website longer. And the longer someone spends on your site, the more opportunities users have to engage with your content or buy something.

Of course, if you’re a subscriber to Netflix, you already know this. You’ve likely spent plenty of time scrolling through their catalog of movies and TV shows trying to decide what to watch. By pulling in various options that align with your tastes – even if they’re not “exact” – it compels you to consider that suggestion as a viable option.

By catering to the user’s needs, you help steer them toward products they’re more likely to be interested in, instead of wasting valuable moments on something they won't.

Increased Sales

A stronger and more personalized user experience leads to longer engagement in your online store, which ultimately influences revenue.

Customers appreciate when fewer hassles stand in the way of the content they want to view. That is the basic principle behind the recommendation engine: give the customer what they want, when they want it.

And recommendation engines aren’t limited to your existing customers, you can also use recommendation filters to promote popular, on-sale, or high inventory items to new or infrequent visitors.

This expands your sales potential, capturing transactions from prospects who’ve yet to establish any meaningful history, but might be drawn to new products, or products with high order volumes that provide social proof.

Influence Your Bottom Line

If you’re looking for your online business to do more than just succeed, but instead to thrive, a recommendation engine can give your store additional opportunities to positively influence your bottom line.

It's no accident some of the most successful online businesses today operate using a product recommendation engine to help their customers and increase their sales.

Shouldn’t your e-commerce company be the next in line for success?

Stay up to date with the rapidly changing e-commerce technology trends and download our white paper, The e-Commerce Technology Trends That Will Shape 2019. In the white paper, you’ll see many important e-commerce trends that can help online retailers meet their customers’ growing needs for personalization, convenience, and security.

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