Advertising.com pre-install app campaign

Michael Carter, Jerry Fiala, Oved Hernandez, M. Mighell, J. Sacks, C. Tucker, Quanquan Gu, W. Scherer
{"title":"Advertising.com pre-install app campaign","authors":"Michael Carter, Jerry Fiala, Oved Hernandez, M. Mighell, J. Sacks, C. Tucker, Quanquan Gu, W. Scherer","doi":"10.1109/SIEDS.2016.7489332","DOIUrl":null,"url":null,"abstract":"The research detailed in this paper seeks to develop an algorithm to optimally select apps to be pre-installed on newly purchased Verizon smartphones. The research focuses on using consumer data sets provided by Advertising.com, a subsidiary of Verizon Communications, to identify and target consumers that are more likely to use pre-installed apps on their smartphones. Advertsing.com hopes to use these targeted campaigns as a means to raise overall app engagement rates. The paper begins with a background on the mobile advertising industry and Advertising.com's motivation for the project. Following the background, the paper discusses data collection and data management practices, detailing the method for granular attribute selection. The selected attributes for this research include but are not limited to a smartphone user's age, metro code, gender, and income level. Information on the time at which an app is opened and the time distance between the events of preloading and opening an app is also used when available. The paper then details the iterative methodology the team used to identify the highest performing user groups for each application campaign and overviews the predictive models used in forecasting app engagement rates. Finally, the paper concludes with a discussion of the preliminary results, which show an increase in app engagement rates for the app Retale following the team's initial recommendation.","PeriodicalId":426864,"journal":{"name":"2016 IEEE Systems and Information Engineering Design Symposium (SIEDS)","volume":"263 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Systems and Information Engineering Design Symposium (SIEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIEDS.2016.7489332","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The research detailed in this paper seeks to develop an algorithm to optimally select apps to be pre-installed on newly purchased Verizon smartphones. The research focuses on using consumer data sets provided by Advertising.com, a subsidiary of Verizon Communications, to identify and target consumers that are more likely to use pre-installed apps on their smartphones. Advertsing.com hopes to use these targeted campaigns as a means to raise overall app engagement rates. The paper begins with a background on the mobile advertising industry and Advertising.com's motivation for the project. Following the background, the paper discusses data collection and data management practices, detailing the method for granular attribute selection. The selected attributes for this research include but are not limited to a smartphone user's age, metro code, gender, and income level. Information on the time at which an app is opened and the time distance between the events of preloading and opening an app is also used when available. The paper then details the iterative methodology the team used to identify the highest performing user groups for each application campaign and overviews the predictive models used in forecasting app engagement rates. Finally, the paper concludes with a discussion of the preliminary results, which show an increase in app engagement rates for the app Retale following the team's initial recommendation.
Advertising.com预装应用活动
论文中详细介绍的研究旨在开发一种算法,以最佳方式选择预装在新购买的Verizon智能手机上的应用程序。这项研究的重点是使用Verizon Communications子公司Advertising.com提供的消费者数据集,以识别和瞄准更有可能在智能手机上使用预装应用程序的消费者。Advertsing.com希望通过这些有针对性的活动来提高应用的整体粘性。本文首先介绍了移动广告行业的背景和Advertising.com开展该项目的动机。在此背景下,本文讨论了数据收集和数据管理实践,详细介绍了粒度属性选择的方法。本研究选择的属性包括但不限于智能手机用户的年龄、城市代码、性别和收入水平。打开应用程序的时间信息以及预加载和打开应用程序事件之间的时间距离也会在可用时使用。然后,本文详细介绍了团队用于确定每个应用活动中表现最好的用户群体的迭代方法,并概述了用于预测应用粘性的预测模型。最后,本文以对初步结果的讨论作为结论,该结果显示,在团队最初的推荐之后,应用Retale的应用粘性有所增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信