Understanding price effects is of high importance to any business, but usually it’s not easy to measure. To do this, Wayfair’s algorithms team has been designing modeling and experimental approaches so that we can disentangle the intricate web of causality. In this video, Wayfair Senior Data Scientist Lin Jia explains……
Stylistic preference is an important factor when a home goods customer is deciding which product to buy, but it is very difficult to identify and define. Although designers have established different style categories, labeling a scene as adhering to a particular style is a highly subjective task. Furthermore, customers often……
Introduction When looking for a new piece of furniture to add to a room, customers generally consider two major points: the visual appearance of the new item, and its harmony with the existing furniture inside the room. Together, these two considerations define a customer’s style preferences. Wayfair’s vast product catalog……
Wayfair sells over 10 million products on our website. This vast selection ensures that customers have numerous options when shopping for a particular item; but it also makes effective, personalized product recommendations of vital importance in helping our customers find products that are relevant to their interests. This week in……
Experimentation plays a central role in understanding the business impact of data science strategies and solutions. A few weeks ago, Wayfair Data Science Manager Jerry Chen shared one way Wayfair has improved the experimentation process by building a unified test design and measurement platform (Gemini) for our marketing AB tests……
Introduction One of the core principles of marketing is that messaging customers about what they want or need is much stronger than messaging them about what they don’t. It’s especially important for Wayfair to identify our customers’ needs, since many of our core products are items that are purchased once……
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……
Product tags reveals the detailed characteristics of a product which can be used to power PDP (product display page) creation, searching, filter creation and more.This week in Wayfair Data Science’s explainer series, Senior Data Scientist Jinnie Chen outlines some of the strategies we applied in Wayfair to perform automated product……
Executive Summary Object detection is an important component of the computer vision workflow at Wayfair. The goal of object detection is to detect bounding boxes of the objects in an image along with the class information (Figure 1), which empowers many downstream applications. In this post, we will share a……
Serving effective personalized product recommendations is critical to providing a pleasant shopping experience for customers at Wayfair. To do this, the Wayfair Data Science team builds state of the art recommender systems that leverage the customer’s previous browsing history to surface products that match their interests. This week in Wayfair……
Most machine learning algorithms are designed to train on balanced datasets. Resultantly, when our data are highly imbalanced, a typical model will have atrocious recall. In this video, Wayfair Senior Data Scientist Trent Woodbury explains the three most common ways of handling this imbalanced data problem. Trent Woodbury……
