Publication Type

Journal Article

Version

submittedVersion

Publication Date

10-2017

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

Discipline

Business Administration, Management, and Operations | Databases and Information Systems | Operations and Supply Chain Management

Research Areas

Operations Management

Publication

Informs Journal on Applied Analytics

Volume

47

Issue

5

First Page

369

Last Page

471

ISSN

2644-0865

Identifier

10.1287/inte.2017.0903

Embargo Period

8-25-2021

External URL

https://doi.org/10.1287/inte.2017.0903

Download

Share

COinS