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Carvana: IsBadBuy?
Lichtendahl, Kenneth C. Jr.; Holtz, Paul Case QA-0886 / Published November 21, 2019 / 6 pages.
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Product Overview

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.


Learning Objectives

1) Understand data science and its applications to business analysis. 2) Practice use of a visual analytics platform and Tableau. 3) Apply data visualization to organize and explain data.

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  • Overview

    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.

  • Learning Objectives

    Learning Objectives

    1) Understand data science and its applications to business analysis. 2) Practice use of a visual analytics platform and Tableau. 3) Apply data visualization to organize and explain data.