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.

How Wayfair leverages Graph Neural Networks to thwart account-hopping fraudsters

Last week I participated on a panel at the Milken Institute Global Conference. It was a fantastic event featuring more than 200 sessions, including mine, “Retail Renaissance: Businesses Embracing Technology, Sustainability, and Innovation.”

In early April, Wayfair introduced the Digital Design Studio in our AllModern Dedham store. The Digital Design Studio (DDS) is a photorealistic interactive experience that lets customers explore Wayfair’s endless catalog by designing 3D furniture arrangements with product cards. Creating this tool was a collaborative effort between Wayfair Next (our R&D group tasked with exploring various emerging tech use cases), Physical Retail, Visual Merchandising, Creative, 3D, and many other Wayfair teams.

Geo experimentation unlocks incrementality testing when customer-level experimentation is not possible.

How to make deliveries faster without touching any packages

Let’s look at how we at Wayfair use custom embedding models to capture complex customer behavior patterns for advanced Fraud prevention.

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.

Dive into the ways Wayfair’s data scientists are using Vertex AI to improve model productionization, serving, and operational readiness velocity.

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.

OverviewAs Wayfair technology teams begin their replatforming journeys, more engineers will make decisions for their single purpose database. Choosing the right database technology is one of the most important decisions you can make when building a new microservice. If you realize down the line that you have made the wrong choice, migrating to another database can be a costly and risky procedure. We committed to Google as our cloud platform provider, and they offer multiple robust database options. Learning enough about all of them to make an informed decision can be a steep hill to climb; therefore, we set out to streamline this process, and provide our teams with a guide to help make these decisions.

To B2B, or not B2B, that is the question.

A closer look at engineering’s role in Wayfair’s design system, Homebase

Last October, at the Lesbians Who Tech in San Francisco, I had the opportunity to speak to a packed house about navigating a career in technology. Whether you’re an aspiring technologist or a seasoned veteran, career navigation is essential in this sector, where unprecedented evolutions and disruptive shifts in innovation require us to plot our course carefully. This is especially the case in the areas of software and algorithms.

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.

You’ll be hard-pressed to find a business area that’s garnered more attention over the last 2+ years than the supply chain. Just recently, Wayfair CTO Fiona Tan spoke about the supply chain at the FORTUNE Brainstorm A.I. 2022 event in San Francisco, where the focus was, you guessed it, artificial intelligence (AI). More specifically, how businesses are shifting from dabbling in A.I. to putting it into the hands of every worker.

Not too long ago, Wayfair came to a crossroads with our aging 20-year-old, commercial off-the-shelf (COTS) EDI platform, which was not only slow but could not handle the critical new data formats used by our businesses as well as our suppliers and partners.

Testing is the critical part of software development. Unit testing is usually where it starts. That’s because it is quick and easy to get started and builds the safeguard to protect the future code changes to ensure they are inline with the corner cases. It is also much easier to maintain. The biggest advantage of unit tests comes from forcing the developers to write modularized and testable code.

(This article originally appeared on Medium)

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.”

Feature Toggles (sometimes referred to as Feature Flags) have revolutionized software development by allowing features within a software product to be enabled individually or disabled at any point, even after they’ve been rolled out to customers. Feature Toggles at Wayfair boost productivity and provide teams with complete control over when and how to make new capabilities available to end users.

Like many interns at Wayfair, Shima Dezfulian works on projects that have a high impact on the company’s operations. More specifically, Dezfulian is part of a Wayfair’s finance and algorithms team that leverages machine learning to make predictions about Wayfair’s profitability.

As with many e-commerce companies, recommender systems are widely used at Wayfair to show the most relevant products to customers at every stage of their shopping journey. Machine learning models used in recommender systems typically optimize for metrics related to customer satisfaction: actions such as clicks, conversions and Add-to-Cart. My talk at the 2022 Annual Meeting of the Institute for Operations Research and the Management Sciences (INFORMS) was related to how Wayfair is utilizing inputs from our supply chain to influence the products we showcase to consumers as they search and browse our storefront.

The annual conference for the Institute for Operations Research and the Management Sciences (INFORMS) brings together professionals and experts from the industry and academia. At INFORMS 2022, I will be giving a presentation on Global Network Optimization (GNO), which is an ongoing project carried out by Wayfair’s operations research team.

On Tuesday October 11, 2022 Google announced in a press release that Wayfair completed the migration of all our data center applications and services to Google Cloud Platform (GCP). This was a significant move that will provide huge benefits, enabling us to increase agility, technical innovation, and more. You can read the full press release [here].

“My job involves making highly business critical technical decisions,” says Alex Maclinovsky. “In my role, I have to go through a large number of technical documents, presentations and make a lot of time-sensitive decisions.”

As the CTO of Wayfair, Fiona Tan leads the global technology organization responsible for shaping market-leading experiences for the company’s 30 million plus customers, 14,000 suppliers and thousands of employees.

Wayfair recently celebrated a significant milestone. I’ll give you a hint-it was featured in a roundup of the top 20 innovations from Wayfair’s first two decades of operation. The answer is Wayfair’s new homegrown warehouse management system (WMS), Nexus.

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