An Intro to Recommendation Systems in Ecommerce

Have you ever wondered how Spotify shows you a list of similar songs that you have liked and listened to on a loop, or how Netflix displays the shows and movies of the same genre that you have been binge-watching? Have you ever wondered what logic forms the “Picked for you” or “Frequently bought together” lists?

Did you know that Amazon drives about 35% of its eCommerce revenue from product recommendations, while Netflix drives 75% of its revenue from product recommendations? This generates over $1 billion per year of Netflix’s revenue.

Thanks to Spotify's “Discover Weekly” recommendation algorithm, its users have listened to over 2.3 billion hours of music!

These lists or options are recommendations that are given by recommendation systems, which are tailor-made by these organizations to understand their users, their preferences, and past interactions with the systems using the power of AI/ML.

Recommendation Systems and Customer Demand

Recommendation systems have changed how people interact with a lot of websites and services. A recs engine can help you and your team drive an increase in your customer engagement, retention, and growth by giving customers the services or products they desire based on their purchasing history.

With the change in market trends, the eCommerce/ retail platforms have seen several key trends emerge: reduced in-store shopping visits, customer experience being a high priority, and enhanced personalization.

Thus, in this digitized world, customer demand is always changing. Consumers always expect an improved and enhanced experience every time they interact with a digital platform. And it is frequently difficult for a user to choose among the many available options by checking each one - whether it be movies in an online cinema, consumer goods in an online store, or any other content. A user may experience information overload if a service contains a large amount of material. Taking advantage of targeted recommendations is one solution to this issue.

For example, the “Genius Recommendations” feature of iTunes, and the “Frequently Bought Together” of Amazon make surprising recommendations that are similar to what we already like.

The entire recommendation process of this system consists of three basic steps:

1. Collection of user information
2. Algorithm processing of data
3. Recommendation suggestion

To offer content that users may be interested in, the NYT (New York Time RS) recommender engine employs an advanced hybrid filtering strategy. It includes a content modeling algorithm that compares two documents based on their subject weight. In other words, the system gets the topic of this specific article.

Individual case studies also demonstrate the efficacy of dynamic product recommendations. EyeBuyDirect.com saw a 175% rise in email click-through rate and a 30% increase in conversion rate, whereas Lux Fix saw an 85.7% rise in email rate of conversion.

Highlighting the Effectiveness of Recs Engine in the E-Commerce Business Model:

RS personalization increases the CTR. Customers who click on the product recommendations have a 5.5 times higher conversion rate than those who do not.

RS can help businesses increase their average order value by recommending related products that can improve the effectiveness of a particular product.

The visitor to customer ratio can increase because when personalized recommendations are displayed, the CTR is then 2Xif non-personalized suggestions are shown.

With the advantages of RS comes a fair share of challenges:

Cold Start Problem - RS relies heavily on user data; however, this also has its own downside, especially when the customer is either new or has very little data available, or a new item is added to the catalog. The recs engine here does not understand what the taste of the new user is, or what the rate/reviews of the newly added product are, which can lead to less accurate results.

Privacy concerns: As recommendation systems collect and use personal data to create personalized experiences, there is a risk of violating user privacy and data protection laws. Businesses must ensure that they are transparent about the data they collect and how they use it, and that they obtain user consent where necessary.

Data quality: Recommendation systems rely heavily on data, and the quality of the data can significantly impact the accuracy and effectiveness of the recommendations. Poor quality data, such as incomplete or inaccurate user data, can lead to irrelevant or even incorrect recommendations.

Just like every coin has two sides, RS comes with its own positives and negatives. However, if built with proper research, the positives will supersede the negatives. From Netflix to Amazon, recommendation engines are everywhere, quietly working behind the scenes to make our lives better. With these engines constantly evolving and improving, we can expect to see even more amazing and personalized experiences in the future, and who knows? Maybe one day we'll look back and wonder how we ever managed without them.

The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "ecommerce website".