research-article

Visual appearance of display ads and its effect on click through rate

Authors:

Vidhya Navalpakkam,

Xiaoli FernAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 495 - 504

https://doi.org/10.1145/2396761.2396826

Published: 29 October 2012 Publication History

Abstract

One of the most important categories of online advertising is display advertising which provides publishers with significant revenue. Similar to other categories, the main goal in display advertising is to maximize user response rate for advertising campaigns, such as click through rates (CTR) or conversion rates. Previous studies have tried to optimize these parameters using objectives such as behavioral targeting. However, there is no published work so far to address the effect of the visual appearance of ads (creatives) on user response rate via a systematic data-driven approach. In this paper, we quantitatively study the relationship between the visual appearance and performance of creatives using large scale data in the world's largest display ads exchange system, RightMedia. We designed a set of 43 visual features, some of which are novel and others are inspired by related work. We extracted these features from real creatives served on RightMedia. We also designed and conducted a series of experiments to evaluate the effectiveness of visual features for CTR prediction, ranking and performance classification. Based on the evaluation results, we selected a subset of features that have the highest impact on CTR. We believe that the findings presented in this paper will be very useful for the online advertising industry in designing high-performance creatives. It also provides the research community with the first ever data set, initial insights into visual appearance's effect on user response propensity, and evaluation benchmarks for further study.

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Cited By

Miura ROhya HItoh T(2023)Visualization System to Analyze Browsing Trends of Internet Video Advertisements2023 27th International Conference Information Visualisation (IV)10.1109/IV60283.2023.00011(1-6)Online publication date: 25-Jul-2023
https://doi.org/10.1109/IV60283.2023.00011
Zhao K(2022)The Study of User Characteristics Factors that Affect the CTR of ADsProceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)10.2991/978-2-494069-31-2_402(3426-3435)Online publication date: 29-Dec-2022
https://doi.org/10.2991/978-2-494069-31-2_402
Zhang XWang ZDu B(2022)Deep Dynamic Interest Learning With Session Local and Global Consistency for Click-Through Rate PredictionsIEEE Transactions on Industrial Informatics10.1109/TII.2020.303616418:5(3306-3315)Online publication date: May-2022
https://doi.org/10.1109/TII.2020.3036164
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Index Terms

Visual appearance of display ads and its effect on click through rate
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics
    1. Image manipulation

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cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2012

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October 29 - November 2, 2012

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Cited By

Miura ROhya HItoh T(2023)Visualization System to Analyze Browsing Trends of Internet Video Advertisements2023 27th International Conference Information Visualisation (IV)10.1109/IV60283.2023.00011(1-6)Online publication date: 25-Jul-2023
https://doi.org/10.1109/IV60283.2023.00011
Zhao K(2022)The Study of User Characteristics Factors that Affect the CTR of ADsProceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)10.2991/978-2-494069-31-2_402(3426-3435)Online publication date: 29-Dec-2022
https://doi.org/10.2991/978-2-494069-31-2_402
Zhang XWang ZDu B(2022)Deep Dynamic Interest Learning With Session Local and Global Consistency for Click-Through Rate PredictionsIEEE Transactions on Industrial Informatics10.1109/TII.2020.303616418:5(3306-3315)Online publication date: May-2022
https://doi.org/10.1109/TII.2020.3036164
Gani MFaroque A(2021)Digital MarketingCross-Border E-Commerce Marketing and Management10.4018/978-1-7998-5823-2.ch008(172-202)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5823-2.ch008
Ye KNazari NHahn JHussain ZZhang MKovashka A(2021)Interpreting the Rhetoric of Visual AdvertisementsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.294744043:4(1308-1323)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TPAMI.2019.2947440
Song XXu BJiang Y(2021)Predicting Content Similarity via Multimodal Modeling for Video-In-Video AdvertisingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.297992831:2(569-581)Online publication date: Feb-2021
https://doi.org/10.1109/TCSVT.2020.2979928
Bron MZhou KHaines ALalmas M(2019)Uncovering Bias in Ad Feedback Data Analyses & Applications✱Companion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3317304(614-623)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3317304
Kitada SIyatomi HSeki YTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad CreativesProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330789(2069-2077)Online publication date: 25-Jul-2019
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Hong YWang KGe WQiu YZhou G(2019)Cursor momentum for fascination measurementFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6607-613:2(396-412)Online publication date: 17-May-2019
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Somandepalli KMartinez VKumar NNarayanan SD'Mello SGeorgiou PScherer SProvost ESoleymani MWorsley M(2018)Multimodal Representation of Advertisements Using Segment-level AutoencodersProceedings of the 20th ACM International Conference on Multimodal Interaction10.1145/3242969.3243026(418-422)Online publication date: 2-Oct-2018
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