One of our missions at Wayfair is to help our 22 million customers find the products they are looking for. For example, when a customer searches for a “modern yellow sofa” on Wayfair, we want to show the most relevant options from the tens of thousands of sofas available in our catalog. To do that, it is important to have a strong understanding of the products we sell. We use machine learning algorithms to analyze and understand the descriptive information (e.g. color, shape) of over 40 million products provided by more than 20 thousand suppliers in our catalog.
10 Min Read
How Wayfair leverages Graph Neural Networks to thwart account-hopping fraudsters
7 Min Read
Geo experimentation unlocks incrementality testing when customer-level experimentation is not possible.
7 Min Read
How to make deliveries faster without touching any packages
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Let’s look at how we at Wayfair use custom embedding models to capture complex customer behavior patterns for advanced Fraud prevention.
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Search relevance – the relationship between users’ queries and the products returned in search results – is one of the most important performance indicators for ecommerce storefronts. However, the sheer volume of the data makes evaluating and improving search relevance a difficult proposition. To give just one example, the Wayfair catalog has millions of products, which makes it difficult to create relevance-focused evaluation datasets manually.
5 Min Read
Wayfair works with over 25,000 suppliers to sell more than 30 million products in a variety of styles from classic to contemporary at compelling price points. In order to create a frictionless shopping experience, we need to enable customers to explore our massive catalog efficiently, and find the right products that help them create their unique sense of home.
6 Min Read
Wayfair has tens of millions of products that serve the needs of over 33 million customers. Each of our customers have nuanced preferences as they buy products to help them create their own unique sense of home. Our search and recommender systems play an important role in helping customers find these products as quickly and conveniently as possible.
5 Min Read
According to Colin Gray, a growing number of economists are being drawn to the prospect of carrying out interesting work at technology companies like Wayfair.
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Over thirty million customers from around the world shop on Wayfair to look for furniture that helps them create their unique sense of home. Wayfair serves a variety of webpages catered to niche customer sub-segments and help customers discover new products – these include pages with advice for people buying “twin futons,” or “nursery chairs with removable cushions.”
5 Min Read
Style transfer: How Wayfair leverages customer browse activity in one furniture category to inform recommendations for a category they have never shopped before.
2 Min Read
Not everyone wants to speak with someone when they need to set up a return or report an issue with their order. We built an NLP-powered Virtual Assistant to provide our customers a fast and easy option that’s available 24/7 to resolve their service-related issues.
10 Min Read