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.
6 Min Read
How data scientists at Wayfair build scalable ML systems to programmatically optimize marketing decisions.
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Wayfair hasn’t always been a household name, but technology has always been at the center of everything we do. When co-founders Niraj Shah and Steve Conine built our first website, racksandstands.com, they did so with ambitious plans for the future. As they built more than 250 more sites, they used data to understand how best to scale their effort to provide a better shopping experience across various Home categories.
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At Wayfair, data scientists help optimize our marketing channel performance by rapidly and iteratively developing machine learning algorithms and data-driven strategies. When it comes to measuring model efficacy, AB testing plays a central role and serves as a gold standard in strategic decision making within marketing. Due to the natural complexities in marketing operations, marketing-related test design and measurement require special techniques to account for multidimensional, sparse, seasonal, and autocorrelated observations. In this blog post, we will discuss these challenges, and introduce how Wayfair’s advanced marketing test platform —Gemini— remedies these issues.
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In April, I attended the Data Science Research Symposium at University of Massachusetts Amherst and gave a short talk highlighting Wayfair’s marketing data science projects. The full video and slides are below, but here's an overview.
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At Wayfair, the data science teams collaborate with other partner teams to translate business problems into analytical frameworks, leverage data and machine learning to make robust predictions and recommendations, and build engineering architecture to scale up machine learning solutions. In a recent machine learning seminar at Northeastern University in Boston, Data Science Manager Jen Wang gave an overview of the team’s structure and a few examples of machine learning models used to solve business challenges at Wayfair. The final part of the seminar illustrates a case study (beginning at 23:40 in the video) of how marketing data scientists at Wayfair use uplift modeling to drive incremental revenue in display remarketing.
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As data scientists, we face a variety of problem types. One of our critical challenges is identifying the proper methodological approach to solve each problem. By doing this, we avoid force-fitting the wrong tool to solve a problem, and we avoid having to reformulate a question to fit a specific method.
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