Outlier helps companies grow faster by finding hidden growth opportunities and problems hiding in their data. One example is Zillow Group’s HotPads, a leading rental marketplace, which uses Outlier to analyze their user behavior.
“I’ve heard other products claim to automatically detect insights, Outlier actually does it.”
– Oleg Salnik, Principal Analylist, HotPads
HotPads is a leading map-based apartment and home rental search brand that closely tracks their site and app analytics to make data-driven decisions about their business. Started in 2005, it is a unique rentals marketplace with the first ever map-based search. HotPads was acquired by Zillow Group in 2012.
The HotPads team is constantly making customer-facing improvements to its site to improve the customer experience. In just a few clicks, Outlier integrated with their Google Analytics website and iOS and Android app data, along with its SendGrid email data, so that the HotPads team could be automatically notified when something important happens in their data.
“I’ve heard other products claim to automatically detect insights, Outlier actually does it.” says Oleg Salnik, Principal Analyst of HotPads. “Not only are the insights actionable, but I find Outlier easier to use than Google Analytics, so now my workflow is to go to their website first.”
Here’s one example of the insights Outlier has found for HotPads and what it meant for their business:
Historically, the number of unique page views to two specific page paths on the HotPads site had moved consistently over time. However, Outlier automatically detected a sudden change, in one case a spike and another a drop, in these page paths. These conflicting movements were unexpected and the HotPads team took immediate action.
This bug would have been difficult for the HotPads team to uncover on its own. First, these specific metrics live in the depths of Google Analytics so they are not tracked closely every day. Also, the code that caused the issue was completely unrelated to the resulting bug, so the HotPads team would not have known to look at these metrics to test the deployment.
“This bug would have been almost impossible for us to find. And if we did, there is no way we would have found it this quickly. We likely would have had months of bad data that we would have had to retroactively had to fix – meaning Outlier saved us weeks of headaches and manual effort.” says Oleg.