This case, which has been taught successfully in a Darden online class, allows for an introductory application of the Tableau analytics platform. In 2012, Carvana Co., an e-commerce platform for buying used cars, hosted a competition called "Don't Get Kicked!" wherein 570 teams competed to predict if a car purchased at auction was a "kick" (i.e., a bad buy)—a vehicle with a major defect. To compete, teams downloaded Carvana's data from Kaggle's website. At the time of the competition, data science was a burgeoning field, and industry watchers wondered if machine learning could help a company such as Carvana develop a competitive advantage. This case analyzes the US used-car market, Carvana's history and Kaggle's role in its development, and the viability of data science—particularly visual analytics—in guiding business and consumer decisions.ExcerptUVA-QA-0886Rev. May 18, 2020Carvana: IsBadBuy?Carvana Co. (Carvana) was an online-only platform for buying used cars that allowed consumers to research and select a vehicle, view a 360-degree image of it, obtain financing and warranty coverage, and complete the purchase. Vehicles could then be delivered or picked up at one of about a dozen car vending machines in a handful of mostly southern US states. By 2017, the company had targeted 60 (and growing) metropolitan markets across the United States and had a nationally pooled inventory of nearly 10,000 used vehicles.Carvana had set out to disrupt the $ 1trillion-per-year US car market. With significant growth in sales since its service launched in 2013, there was a strong argument for the "Amazon of cars" to go public. "We clearly got to a place in the business where we got to execution mode and we needed to open more markets and ramp up sales in markets we were already in," Ernie Garcia III, Carvana's 34-year-old cofounder and CEO said. "It became a much clearer story to tell the public markets. We think public capital is good for the brand."But when the company held its IPO on the New York Stock Exchange on April 28, 2017, the results were surprising—the stock sputtered and closed down 16%. That led some observers to question the decision to take the business public, while others waited patiently for the appeal of the "disrupter" to fuel interest and growth
