Data Science

Some experiments at Wayfair can last 60 days or more. To speed up learning in experiments while still optimizing for long term rewards, our team developed a data science platform called Demeter, that uses ML models to forecast longer term KPIs based on customer activity in the short term. In this post we provide an overview for Demeter and its theoretical foundation in causal inference

We’ve designed and developed a scalable algorithmic decision-making platform called WayLift for paid media marketing. In this blog post, we will provide an overview of the theoretical foundation of WayLift — the contextual bandit algorithms for optimizing marketing decisions.

Out of  a zillion options, customers want to find the one item perfect for them. The recommendations team makes that happen by leveraging advanced machine learning techniques and our immense datasets to provide ever improving recommendations. A key step in this process is experimentation, i.e. the design, launch and analysis of A/B tests to understand which algorithm provides the best customer experience. This blog post describes a simulation tool our Analytics team has built and used prior to every test launch. This tool has improved our test success rate and testing velocity dramatically, and led to new insights/improvements of our recommendations.

When a Wayfair customer shops for a product that is just right for her home, color plays a big role. For example, a customer might say, “I am looking for a green chair.” Or she might say, “I am looking for a teal chair.” There may be various versions of these customer color search stories. As data scientists at Wayfair, we want to solve these customer problems by algorithmically extracting the color information from the products and assigning customer-friendly color names.

If you are among the tens of millions of customers who have shopped at Wayfair, you have experienced one of the many technologies built by the Machine Learning and Data Platforms team. We serve the business, engineering and data science teams across Wayfair, who are working on solving one of the most intriguing and gratifying challenges — helping our customers make their dream home a reality.

How data scientists at Wayfair build scalable ML systems to programmatically optimize marketing decisions.

High-quality lifestyle imagery is core to Wayfair’s ability to merchandise its catalog effectively and help customers shop confidently. Creating lifestyle images through 3D models enables rapid production of product visual merchandising at scale, drives simultaneous reduction in cost and speed to produce imagery, and unlocks new ways (e.g. AR, VR) for customers to engage with products. Early tests show 3D-generated images can provide high quality visual detail of our products and increase conversion rates for flagship brands (main products in Wayfair’s portfolio and to which customers most relate or identify Wayfair). Therefore, producing 3D models quickly and accurately is key to further reducing 3D imagery costs and driving revenue. In this blog, we will introduce how machine learning is utilized to aid 3D modeling at Wayfair.

At Wayfair, we recommend millions of products across all styles and budgets. Because of the scale of our catalog, we constantly ask ourselves: how can we help our customers find the right products efficiently while discovering exciting complements to fill out their space?

Wayfair is an international company with a presence in multiple countries: US, Canada, UK, and Germany. And where our UK customer might look to purchase a “heater”, our US customer might use the term “radiator” to describe the same thing. We want to ensure we’re able to understand that both customers are referring to the same item and interpret feedback left by customers in the form of product reviews correctly.

In early August, the Data Science (DS) team put on their first DS-wide hackathon. The global hackathon event brought together Boston and Berlin based data scientists for one day of hacking and demos. The event challenged teams to think big about how algorithms can transform Wayfair’s business and help unveil unseen possibilities. Some successful tools and products on our site, like our Visual Search tool, started as hackathon projects! The hackathon is a fun challenge to try out new methods or algorithms, explore a different part of the business, and collaborate with team members across disciplines.

Explainable Artificial Intelligence (XAI) is an emerging field within Machine Learning (ML). There has been considerable advances in the state of the art in recent years.

Feedback Hub is Wayfair's inhouse web application, built by Data Science Analytics, which enables Analysts all across Wayfair to leverage data and insights generated by Natural Language Models developed by our Voice of the Customer DS team.