E-commerce recommendation engines are a hot topic when it comes to AI in e-commerce. Due to the pandemic, everyone rushed to open an online store. The goal is to provide a better shopping experience for customers. With relevant product recommendations, customers are more likely to buy several items in a bundle. What’s more, good suggestions will keep them on the site longer.
Statistics show that 35 percent of all products sold on Amazon come via their e-commerce recommendation engine. So it’s safe to say that using AI in e-commerce made Amazon into what they are today.
What Is a Recommendation Engine?
An e-commerce recommendation engine is an AI-powered tool that improves the customer experience. For example, it suggests items based on customer behavior and product similarity. A recommendation engine for e-commerce analyzes user data. It includes viewed/purchased items, browsing habits, and user preferences.
Machine learning for e-commerce found its purpose outside of e-commerce as well. Netflix claims that its recommendation system saves them $1 billion per annum.
Other platforms that use product recommendations include:
- Spotify – They manage to pinpoint your taste and provide song recommendations. This reduces the number of times you need to skip a song.
- YouTube – The next video on the playlist should be relevant enough.
- Instagram – The Explore section of Instagram analyzes your interests and habits. It is used to provide related content.
- Ads – Ad platforms generate ads relevant to your interests. It’s something that benefits both parties. If you have to see ads, at least they can be something you want to see.
How Does an E-Commerce Recommendation Engine Work?
There are many types of e-commerce recommendation engines. But, they work on the same principles. An e-commerce recommendation platform needs to collect information, including:
- Past activity on the website
- Previous ratings and product reviews
- Profile information (gender, age, location)
- Links you click on the website.
In e-commerce, product recommendations often relate to the current product. This is especially true for online fashion stores. An e-commerce recommendation engine needs to generate a list of recommended products. The items should be visible to website visitors at all times. There are two places where users should be able to see recommended products:
- Outside of context – The items that appear after visiting/logging in. The choice of these items depends on your history and not on your current interaction.
- In-context: The items that appear as recommended while you’re viewing another product. As a result, the opened product is used as the context to display recommendations. The idea is that the recommended items should be similar.
Credits: The Ultimate Guide to eCommerce Product Recommendations – Acidgreen
Types of Recommendation Engines
In general, there are three types of e-commerce recommendation engines.
Content-based filtering analyzes the behavior of a single user. Based on their interactions and preference, it generates recommendations that relate to them. The algorithm looks for patterns in user choices and behavior. So, the more data, the better the accuracy of these models.
E-commerce recommendation platforms based on content-based filtering are imprecise in the beginning. It’s only after the user spends enough time on the website that they become more accurate. A special type of content-based recommenders is case-based recommendations. They explore the similarity between products and provide smart suggestions. It makes them useful for fashion e-commerce stores.
Collaborative filtering is more common. It involves using information from other users to provide recommendations for you. With many users, it’s easy to find store visitors with similar preferences. After that, you can use their decisions to generate suggestions.
These e-commerce recommendation platforms are more accurate. Getting started is easy because recommendations can form immediately from other users’ data.
Recommendation engines in e-commerce include knowledge systems. Recommendations are based on explicit knowledge of user needs, combined with domain expertise. Rules exist to decide whether a user would like a specific product.
Knowledge systems work best for expensive, infrequently purchased items. It can include cars, digital cameras, and touristic arrangements. In these cases, users prefer that a recommendation satisfies a set of rules rather than other factors.
How to Choose an E-Commerce Recommendation Engine?
Choosing a recommendation engine depends on the store you own. For example, if you are selling fashion products, you will need a case-based recommender. But, if you are selling digital cameras, you would most likely want a knowledge system.
We singled out the top three characteristics for choosing an e-commerce recommendation platform:
- Implementation details: Integrating your solution with an e-commerce store is not simple. Online stores often use some of the major e-commerce platforms (Shopify, Magento, WooCommerce). But, they can also be custom work, which makes integration more difficult.
- Features: Recommendation engines for e-commerce can be sold as a separate product. Most companies bundle it with a review system, search engine, or an inventory manager. There can be several types of suggestions, like Recommended for you. Customers who bought this also viewed…, etc. It’s important to know there are several types of suggestions.
- Price: The biggest issue is if you’re getting enough for your buck.
Top 5 Recommendation Engines
Salesvision uses deep fashion tagging. Each product goes through a computer vision e-commerce algorithm. The input is a single outfit image, and the output includes product tags. Once the entire catalog goes through deep tagging, the possibilities are endless. Some cool features include:
- Visual search (uploading a photo of an outfit instead of filtering)
- Recommendations based on item similarity, matching, and customer behavior insight
- Automatic generation of SEO-friendly product description
Recolize is a personalized e-commerce recommendation platform. They offer two plugins: for Magento and WordPress. With Recolize, e-commerce owners can curate their blog content based on customer behavior insight.
Barilliance optimized product searches with useful features like auto-fill and detailed filters. E-commerce owners can use Barilliance in many ways. The most popular feature is sending notifications and newsletters to customers. Barilliance takes their interest into account to personalize the emails.
Magevolve offers a Magento plugin, and they’ve been in the industry for quite a while. It provides catalog synchronization, administrator control, and smart product recommendations.
Vue.ai is an amazing example of how to use AI and Computer Vision in e-commerce. They offer product tagging, personalization, personalized emails, AI styling assistants, and much more.
The pandemic has affected retailers and consumers alike. As a result, stores started switching to the digital world. However, they discovered it’s not as straightforward as for brick-and-mortar shops. So online store owners started using AI to enhance the experience of their sites’ visitors. Today, all significant retailers have an e-commerce recommendation platform installed.
Recommendation engines in e-commerce take advantage of user data and habits. They generate product suggestions that might interest the customer. Consequently, these suggestions persuade site visitors to order extra items in the store and increase cart size. Relevant suggestions help customers discover products they would have skipped otherwise. There are different types of e-commerce recommendation platforms. The reason is that they are useful for various types of online stores.
Product suggestions are not the only use of AI in e-commerce. Deep fashion tagging powers useful features. For example, visual searches and automated product descriptions. But, it’s most important use case is understanding how fashion products match.