Machine Learning & Artificial Intelligence

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.

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.

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.

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.”

Style transfer: How Wayfair leverages customer browse activity in one furniture category to inform recommendations for a category they have never shopped before.

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.

Images from product reviews can inspire and inform our customers. We created a machine learning model that helps us in prioritizing the most helpful and good looking images on the product page.

Wayfair and its subsidiary brands operate globally and we send out millions of marketing emails every day to communicate with our customers. To improve customer satisfaction and increase customer engagement, we have developed a new generation of daily sales email models (Nightingale) to make email sending decisions. The Nightingale model is built via a scalable retraining pipeline to serve different business needs across all regions and brands.

Today, many companies rely heavily on third-party event tracking platforms for collecting event data. Perhaps the most well-known of these is Google Analytics. However, at Wayfair, we chose to go another route, largely eschewing the third-party vendor model to develop our own first-party event tracking platform. It’s called Scribe.

I recently had the honor of speaking at EmTech Digital, MIT Technology Review’s signature conference on artificial intelligence (AI) and business leadership. This isn’t my first time speaking at the conference, but it is my first time doing it in person.

As a technology company, Wayfair encourages all team members to roll up their sleeves and innovate. We take immense pride in how our work impacts customers and contributes to the business’s success. We also derive great satisfaction when a team member is recognized by the larger technology community.

Machine learning (ML) profoundly transformed the digital shopping experience for millions of people worldwide. We’ve seen this at Wayfair, where our teams are continuously delivering intelligent and improved experiences for our customers by integrating ML at every stage of their journey with us. Right now Wayfair is leveraging hundreds of ML applications, which on the outside are improving marketing campaigns, improving the visual merchandising of the products we sell, personalizing product recommendations, enabling high-quality customer service, and more.