Published on
June 2, 2022
Imagine having a shopping buddy that always understands shoppers’ preferences and comes up with the right items to offer. Such a helpful (yet not pushy) assistance for your customers, right?
That virtual buddy is called a recommendation engine and can be on your website in minutes.
But first things first.
Let’s have a look at how recommendation engines work and help eCommerce fashion businesses like yourself.
Product recommendations are filtering systems that predict and show items that the shopper would like to purchase. The system picks similar products to the ones searched and based on the search history and shopper behavior, it recommends relevant products that match the shoppers’ preferences.
Product recommendations in eCommerce are not a nice-to-have feature anymore - they’re a must-have. They help businesses deliver a personalized shopping experience, which results in improved engagement and higher average order value.
Having relevant product recommendations is one of the aspects that online buyers say make the shopping experience great.
ZARA is not selling so many items by chance. 😉
Suggested read: The What, Why, and How of Product Recommendation Engines
Product recommendations in eCommerce are powered by machine learning and AI algorithms. Thanks to the automated configuration and management, the recommendation engine can intelligently choose which products to offer to a specific customer.
Different systems work in different ways, but each product recommendation engine follows the same logic that can be simply explained in four phases.
The engine collects all the relevant and available data. It can collect data in an explicit way by collecting ratings and comments on products or in an implicit way looking at search history and previous interactions.
In this phase, the recommendation engine stores the collected data for each customer. The engines usually use large SQL servers to store the information.
By cross combining the collected data and the products in the eCommerce store, the engine comes up with similar matches.
In the last phase, the engine filters the findings and finally offers the most relevant products to each potential customer.
Based on the filtering, recommendation engines can be classified into three main types.
Let’s deep dive into each of them.
Collaborative filtering happens when a system analyzes customer behavior and predicts what they might like based on similarities with other customers. This recommendation type assumes that people who showed interest in something in the past will eventually show interest in similar items again.
For example, let’s say that two girls are shopping in Pull and Bear. Mary bought a red hoodie, blue jeans, and black sneakers. And Anna bought blue jeans, black sneakers, and a brown backpack.
The collaborative filtering engine will assume that they have similar tastes and styles and Anna would also like the red hoodie and Mary would like the brown backpack. So, they’ll see those products in the recommendations section on the online shopping site.
This type of filtering is based on the product’s description and the profile of the customer’s preferences and choices. In this system, the product’s description contains keywords to support the description and the user’s profile holds the items that the customer likes.
The recommendation engine collects the customer’s preferences and makes a profile. Then the algorithm tries to recommend other products that are similar to the already liked ones.
Imagine that you’re reading an article about fashion for Grammy’s 2022. The engine will process that information and will recommend similar articles, for example, an article about the fashion for the Academy Awards and another one about copying Zendaya’s look.
The logic behind this is that if a customer likes a particular item, he will probably like other similar items.
As the name suggests, this recommendation system uses a combination of both - collaborative and content-based filtering to find the perfect products that the customer will like.
This type of recommendation in eCommerce works by collecting and comparing the searching habits of similar users as well as offering products that share characteristics with the products the customer liked and rated lately.
Let’s clear it up.
Image this situation. Toby and Dave are shopping for Nike running shoes. Toby looked for Pegasus 38 and Revolution 6. Dave liked Pegasus 38, Revolution 6, and Flex. The recommendation engine will show Toby Flex (collaborative filtering element) and Pegasus 39 and Revolution 5 (content-based filtering element).
This type of filtering and product recommendation offers the widest specter and brings the shopping experience and personalization to another level.
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Regardless of which product recommendation type you use on your eCommerce store, recommendations will significantly improve and boost product discovery. Think of it like a virtual assistant that stands next to your customers, understands their desires, and offers similar products that they maybe couldn’t notice themselves.
Here are some ways in which recommendations improve product discovery and the overall shopping experience:
Suggested read: Product Discovery: A Practical Guide for Ecommerce
A recommendation engine is here to help your customers what they really want and help them discover new products they will love. And by recommending relevant products you’re ensuring a seamless shopping experience that will convert users into returning customers.
Pixyle.ai’s similar recommendations will help you offer an even more personalized experience to your customers. It will allow you to improve product visibility, increase the conversion rate, and make highly relevant offers.
Take Pixyle.ai for a spin now!
How to develop solutions that help shoppers find what they are looking for.
How to develop solutions that help shoppers find what they are looking for.
How to develop solutions that help shoppers find what they are looking for.
How to develop solutions that help shoppers find what they are looking for.
Pixyle AI generates e-commerce product data that enables brands, retailers and marketplaces to deliver exceptional product discovery experiences.
