Vungle Inc. improves monetization using big-data analytics

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 netwo...

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Main Authors: DE REYCK, Bert, FRAGKOS, Ioannis, GRUKSHA-COCKAYNE, Yael, LICHTENDAHL, Casey, GUERIN, Hammond, KRITZER, Andre
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Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/6765
https://ink.library.smu.edu.sg/context/lkcsb_research/article/7739/viewcontent/Vungle.pdf
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spelling sg-smu-ink.lkcsb_research-77392021-08-26T01:52:55Z Vungle Inc. improves monetization using big-data analytics DE REYCK, Bert FRAGKOS, Ioannis GRUKSHA-COCKAYNE, Yael LICHTENDAHL, Casey GUERIN, Hammond KRITZER, Andre 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. 2017-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/6765 info:doi/10.1287/inte.2017.0903 https://ink.library.smu.edu.sg/context/lkcsb_research/article/7739/viewcontent/Vungle.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University mobile advertising logistic regression big data feature selection computational advertising machine learning cloud computing Business Administration, Management, and Operations Databases and Information Systems Operations and Supply Chain Management
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic mobile advertising
logistic regression
big data
feature selection
computational advertising
machine learning
cloud computing
Business Administration, Management, and Operations
Databases and Information Systems
Operations and Supply Chain Management
spellingShingle mobile advertising
logistic regression
big data
feature selection
computational advertising
machine learning
cloud computing
Business Administration, Management, and Operations
Databases and Information Systems
Operations and Supply Chain Management
DE REYCK, Bert
FRAGKOS, Ioannis
GRUKSHA-COCKAYNE, Yael
LICHTENDAHL, Casey
GUERIN, Hammond
KRITZER, Andre
Vungle Inc. improves monetization using big-data analytics
description 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.
format text
author DE REYCK, Bert
FRAGKOS, Ioannis
GRUKSHA-COCKAYNE, Yael
LICHTENDAHL, Casey
GUERIN, Hammond
KRITZER, Andre
author_facet DE REYCK, Bert
FRAGKOS, Ioannis
GRUKSHA-COCKAYNE, Yael
LICHTENDAHL, Casey
GUERIN, Hammond
KRITZER, Andre
author_sort DE REYCK, Bert
title Vungle Inc. improves monetization using big-data analytics
title_short Vungle Inc. improves monetization using big-data analytics
title_full Vungle Inc. improves monetization using big-data analytics
title_fullStr Vungle Inc. improves monetization using big-data analytics
title_full_unstemmed Vungle Inc. improves monetization using big-data analytics
title_sort vungle inc. improves monetization using big-data analytics
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/lkcsb_research/6765
https://ink.library.smu.edu.sg/context/lkcsb_research/article/7739/viewcontent/Vungle.pdf
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