How Apartment List Partnered with ByteGain to Identify and Target its Highest Value Users


  • ByteGain used machine learning to identify Apartment List users who were likely to rent an apartment
  • Showed targeted ads to the 20% of users most likely to rent in the next 3 months
  • 17% increase in user activity
  • 51% increase in click-through rate
  • 108% increase in revenue

Apartment List

John Kobs and Chris Erickson both had terrible experiences trying to rent an apartment.

Together in 2011, they founded Apartment List with the aim of making the rental process more efficient, transparent, and enjoyable. Today, Apartment List is the fastest growing apartment site in the U.S. with its website and mobile apps reaching millions of renters every month, and matching them with a home that fits their needs.

John Kobs and Chris Erickson

Objective: Identifying and engaging in-market renters

Companies in the online apartment rental space find it difficult to distinguish between renters browsing for an apartment and those actively in-market to sign a lease.

While the industry traditionally markets to every renter the same way, regardless of intent, Apartment List sought to identify only those in-market renters to focus marketing efforts.

Apartment List Mobile App

Strategy: Predict user behavior using a deep learning model

Every marketer’s dream is to understand the intent of a customer. Using tens of billions of data points, Apartment List partnered with ByteGain to train a deep learning model to predict the preferences, behavior, and intent of every renter who visited Apartment List's website and apps.

First, the model predicted whether a user was likely to sign a lease in the next few months. And if so, whether they would organically sign with Apartment List or go elsewhere.

Apartment List Users Scored by "Rent Intent"

Using this model, ByteGain helped Apartment List buy retargeting ads on Facebook and Google Adwords to re-engage the most valuable segments of users.


Using insights from the deep learning model, Apartment List dramatically improved marketing efficiency.

ByteGain technology accurately provided predictions about:

  • Which users were in-market for an apartment
  • For those that were in-market, which would sign a lease on Apartment List versus elsewhere
  • The optimal sorting of rental listings to maximize conversion
  • The best method of retargeting in-market renters via Adwords, Facebook, and Doubleclick

Read the full report

Apartment List + ByteGain | Case Study