Goal At Wayfair, we want to predict the performance of new sorting algorithms before we launch them. Why? Sorting algorithms make money! Think about it: what’s the main difference between scrolling randomly through our ~18 million products versus actually finding the thing you want and buying it? A sort order……
By Sarah Cotterill It’s critically important for Wayfair to understand the causal impact on customer’s future spend of various customer actions, like downloading our mobile app, subscribing/unsubscribing from our email list, or purchasing in different product classes. These estimates not only inform strategic business decisions at a high-level, such as……
Many approaches to ranking a list of products or search results are based on assigning a score to each item and sorting in descending order—in other words, greedy sorting approaches. In e-commerce, predictive models place the product that the customer is most likely to be interested in at the top,……
1. Introduction In the last century, scholars across a variety of fields have explored the mathematics of decision-making, or learning to intervene within a system. A challenge that relies on decision-making is fundamentally different, and more complex, than a classification problem. For example, designing a machine to distinguish a photo…
Introduction Creating a recommendation system for home decoration poses unique challenges: each customer has his/her own taste and would like to maintain a cohesive personal style across his/her home. We at Wayfair know that it is hard to describe that taste in words and then to search through huge product……
Part I: How to build tools that you can hand off to data scientists [feature of Hoover tool] Summary Business moves fast and data science runways are long. What can we do to remove friction and iterate on data science models as quickly as possible? In this two-part blog……
Wayfair has a huge catalog with over 14 million items. Our site features a diverse array of products for people’s homes, with product categories ranging from “appliances” to “décor and pillows” to “outdoor storage sheds.” Some of these categories include hundreds of thousands of products; this broad offering ensures that……
Introduction The main objective of our recommender systems is to narrow down Wayfair’s vast catalogue to assist customers in finding exactly the products they want. Most recommendation algorithms leverage a customer’s browsing history, which includes various types of interactions with products, such as clicks, add-to-cart actions, and orders. The diversity……
Introduction Every day, Wayfair receives tens of thousands of messages from customers, telling us about their experiences with our products and services through the likes of product reviews, return comments, and various surveys. Considering that our customers spend valuable time writing these, it is fair to assume that they have……
Choosing between school and work can be an either-or proposition for many, whether for financial reasons or because there simply don’t seem to be enough hours in the day. When faced with this decision myself, I initially shared similar concerns. However, I soon found that at Wayfair this can be……
Introduction Uplift models predict the incremental effect of a treatment on the outcome of an individual. In other words, they answer the causal question of how an individual’s behavior will change if we give him/her a specific treatment. At Wayfair, we build uplift models to inform us of whom to……
