Vungle Inc. Improves Monetization Using Big Data Analytics

Bert De Reyck (), Ioannis Fragkos (), Yael Grushka-Cockayne (), Casey Lichtendahl (), Hammond Guerin () and Andrew Kritzer ()
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Bert De Reyck: UCL School of Management, University College London, London WC1E 6BT, United Kingdom
Ioannis Fragkos: Department of Technology and Operations Management, Rotterdam School of Management, Rotterdam 3062 PA, Netherlands
Yael Grushka-Cockayne: Darden School of Business, University of Virginia, Charlottesville, Virginia 22903
Casey Lichtendahl: Darden School of Business, University of Virginia, Charlottesville, Virginia 22903
Hammond Guerin: Data Science Team, Vungle Inc., San Francisco, California 94107
Andrew Kritzer: San Francisco, California

Interfaces, 2017, vol. 47, issue 5, 454-466

Abstract: The advent of big data has created opportunities for firms to customize their products and services to unprecedented levels of granularity. Using big data to personalize an offering in real time, however, remains a major challenge. In the mobile advertising industry, once a customer enters the network, an ad-serving decision must be made in a matter of milliseconds. In this work, we describe the design and implementation of an ad-serving algorithm that incorporates machine-learning methods to make personalized ad-serving decisions within milliseconds. We developed this algorithm for Vungle Inc., one of the largest global mobile ad networks. Our approach also addresses other important issues that most ad networks face, such as user fatigue, budget restrictions, and campaign pacing. In an A/B test versus the company’s legacy algorithm, our algorithm generated a 23 percent increase in revenue per 1,000 impressions. Across the company’s network, this increase represents a $1 million increase in monthly revenue.

Keywords: mobile advertising; logistic regression; big data; feature selection; computational advertising; machine learning; cloud computing (search for similar items in EconPapers)
Date: 2017
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