Improving eCommerce Discovery With Recommendation Engines
What do YouTube, Netflix, Amazon, and Spotify have in common? Powerful recommendation engines that surface highly relevant products and content, so their users have a customized, delightful experience.
In eCommerce, the level of personalization that recommendation engines deliver makes a huge difference when it comes to both conversion and customer satisfaction. In this post, we’ll look into eCommerce recommendation engines, and how they can improve your bottom line through individualized product discovery.
What are product recommendation engines?
A recommendation engine or a recommender system is a type of information filtering system that uses algorithms to predict and recommend the most relevant content based on user interactions, ratings, and preferences.
In eCommerce, online recommendation engines customize the shopping experience. Showing products that matter to customers not only increases their basket size and engagement, but also, and more importantly, makes the overall shopping journey as friction-free as possible.
According to McKinsey & Co., “Already, 35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendations based on such algorithms.”
Customers today are overloaded with information. Product recommendation engines simplify the decision process and help them find products that they love. As a result, you have delighted customers who will keep coming back for more.
How do recommendation engines work?
The secret of effective recommendation engines is data. It starts with data collection, followed by data storage, and then, data filtering. There are many approaches but the most common ones are collaborative filtering, content-based filtering, knowledge-based systems, and hybrid systems.
- Collaborative filtering bases suggestions on a user’s past behavior such as previously bought and selected items, and similar preferences from other users.
- Content-based filtering considers the characteristics (or tags) of an item and recommends others with similar attributes.
- Knowledge-based systems include domain expertise in addition to a user’s needs and/or previous interactions; are mainly used for complex domains and uncommon products.
- Hybrid systems combine any of the above and other approaches.
The unifying element among all of these recommendation engine techniques is the consideration of user behavior. The latest advancements in technology specifically computer vision, machine learning, and deep learning make it possible for online recommendations to be personalized on an individual level, as opposed to just the demographic group level.
Examples of recommendation engines improving eCommerce discovery:
Product recommendation engines make the online shopping experience not only more engaging but also seamless. When you show products that match your customers’ preferences, you have more opportunities to cross-sell and upsell.
So, how do recommendation engines work in action? Here are some examples!
Shop similar and shop the look carousels
The most common recommendation engine use case is suggesting visually similar products to shoppers. Providing customers with similar options allows them to uncover more products with visual attributes that appeal to them until they find exactly what they’re looking for.
A related application is enabling customers to shop all items within an image without having to navigate throughout the site. This recommendation engine identifies all shoppable products in a photo and suggests comparable items. This way, your customers can be inspired and easily purchase an entire outfit or complete interior design look.
Others also like or also bought carousels
Recommendations from family and friends as well as online reviews influence shoppers’ purchase decisions today. Brands can replicate that sense of social proof with online recommendation engines. Depending on which item your customer is viewing, you can expose related products that customers with similar buyer behavior liked or bought.
Inspiration galleries and related looks
Product recommendation engines can also help shoppers discover more of your products through social media images. You can show relevant user-generated content that features and contextualizes your catalog products.
Moreover, brands can use both product and social media content to curate photos into shoppable inspiration galleries that instantly connect inspiration to purchase. Personalizing these galleries for your shoppers enables them to get not only the right product recommendations but also relevant styling ideas.
Personalized search results
In addition to upgrading your homepage and product pages, recommendation engines can also improve text search results. Brands can prioritize and personalize results based on shopper data and past purchases.
So, every time customers visit your website and perform a search, you’re consistently improving the search results that you deliver. Recommendation engines can also surface additional content related to the products they’re searching for.
With these features, you can boost the performance of search, delivering the most relevant and accurate product suggestions and content for each shopper.
Customer experience is all in the data
To sum it up, online recommendation engines are proven to be powerful tools that help customers connect with products that they love. They make online shopping relevant, personal, quick, and easy, by enabling brands to deliver meaningful customer experiences.
To ensure your recommendation engines succeed in offering hyper-personalized options to your customers, you need to collect lots of data via their browsing behavior and consistent use. Since delivering an individualized, delightful customer experience is key for modern brands, the sooner you invest in recommendation engines that make product discovery feel truly personal the better.