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.

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Keywords Big data, Cloud computing, Computational advertising, Feature selection, Logistic regression, Machine learning, Mobile advertising
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Journal Interfaces (Hanover)
De Reyck, B, Fragkos, I, Grushka-Cockayne, Y. (Yael), Lichtendahl, C. (Casey), Guerin, H. (Hammond), & Kritzer, A. (Andrew). (2017). Vungle Inc. Improves monetization using big data analytics. Interfaces (Hanover), 47(5), 454–466. doi:10.1287/inte.2017.0903