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Wharton Students Help Zillow Turn Data into Results

by EasyDailyCrypto News
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The Zillow project promised to be a tough one.

The online real estate company wanted to find meaningful patterns in the copious data it collects on site visitors, so the leaders turned to Wharton Customer Analytics for help. The project was put into the Analytics Accelerator, giving Zillow access to a team of Wharton and Penn students who worked for seven weeks to solve the problem.

Keshav Ramji, W’24 EAS’24, who is earning a dual degree in economics and computer science, was one of those students. A budding data scientist with a knack for numbers, the 18-year-old sophomore enjoys making order out of the chaos of data. And with Zillow, there was plenty of data.

“We were given data from 10,000 Zillow users, all anonymized,” said Ramji, who served as a senior analyst on the project. “At first, it seemed quite challenging because the data was disaggregated, so it was just not immediately helpful. The first thing we needed to do was figure out how to aggregate it to make it useful.”

The ultimate project goal was customer segmentation. Zillow wanted to know what makes some customers browse properties on the site without taking further action, while others click to learn more about a property, eventually contacting a listing agent. Understanding the data can help Zillow target different customers, especially the ones referred to as “dreamers” – site visitors who window shop without ever clicking through to buy or rent.

“We wanted to identify those dreamers, but we didn’t exactly know how those dreamers behave,” Ramji said. “So, there were quite a number of different features we had to examine and then aggregate appropriately.”

Forrest Dougan, marketing science principal at Zillow, said the company deliberately refrained from giving too much guidance to the Accelerator team because it didn’t want to bias the results.

“Our project that we picked was a little bit exploratory. We knew that there could be value in it, but it was a little bit of panning for gold,” he said.

At the end of the seven weeks, Dougan was more than impressed. Ramji and the rest of the team worked diligently to design models, revise those models, and find results with real-world applications.

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