Originally published in Medium
Data products have always been an instrumental part of Airbnb’s service. However, we have long recognized that it’s costly to make data products. For example, personalized search ranking enables guests to more easily discover homes, and smart pricing allows hosts to set more competitive prices according to supply and demand. However, these projects each required a lot of dedicated data science and engineering time and effort.
Recently, advances in Airbnb’s machine learning infrastructure have lowered the cost significantly to deploy new machine learning models to production. For example, our ML Infra team built a general feature repository that allows users to leverage high quality, vetted, reusable features in their models. Data scientists have started to incorporate several AutoML tools into their workflows to speed up model selection and performance benchmarking. Additionally, ML infra created a new framework that will automatically translate Jupyter notebooks into Airflow pipelines.
In this post, I will describe how these tools worked together to expedite the modeling process and hence lower the overall development costs for a specific use case of LTV modeling — predicting the value of homes on Airbnb.
Customer Lifetime Value (LTV), a popular concept among e-commerce and marketplace companies, captures the projected value of a user for a fixed time horizon, often measured in dollar terms.
At e-commerce companies like Spotify or Netflix, LTV is often used to make pricing decisions like setting subscription fees. At marketplace companies like Airbnb, knowing users’ LTVs enable us to allocate budget across different marketing channels more efficiently, calculate more precise bidding prices for online marketing based on keywords, and create better listing segments.
While one can use past data to calculate the historical value of existing listings, we took one step further to predict LTV of new listings using machine learning.
Data scientists are typically accustomed to machine learning related tasks such as feature engineering, prototyping, and model selection. However, taking a model prototype to production often requires an orthogonal set of data engineering skills that data scientists might not be familiar with.
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