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Vungle Inc. Improves Monetization Using Big Data Analytics

De Reyck, B; Fragkos, I; Grushka-Cockayne, Y; Lichtendahl, C; Guerin, H; Kritzer, A; (2017) Vungle Inc. Improves Monetization Using Big Data Analytics. Interfaces , 47 (5) pp. 454-466. 10.1287/inte.2017.0903. Green open access

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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.

Type: Article
Title: Vungle Inc. Improves Monetization Using Big Data Analytics
Open access status: An open access version is available from UCL Discovery
DOI: 10.1287/inte.2017.0903
Publisher version: https://doi.org/10.1287/inte.2017.0903
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Social Sciences, Science & Technology, Technology, Management, Operations Research & Management Science, Business & Economics, mobile advertising, logistic regression, big data, feature selection, computational advertising, machine learning, cloud computing, Optimization, Regularization, Television, Models
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management
URI: https://discovery.ucl.ac.uk/id/eprint/10039003
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