TargetingVis: visual exploration and analysis of targeted advertising data
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R E G UL A R P A P E R
Di Peng • Wei Tian • Min Zhu
•
Yukun Ren • Xiaojian Lin • Mingzhao Li
TargetingVis: visual exploration and analysis of targeted advertising data
Received: 28 October 2019 / Revised: 11 May 2020 / Accepted: 22 May 2020 The Visualization Society of Japan 2020
Abstract Targeted advertising is a dominant form of online advertising. It considers advertisers’ major concern of their customers, including the consumers’ certain traits, interests and individual preferences. To promote the effectiveness of advertisement delivery, advertising analysts need to understand advertiser delivery behavior and problems in targeting structure. However, statistical methods cannot meet analytical requirements completely, and analysts have to spend a lot of time reading countless data reports. Concretely, there is no efficient tool accomplishing analysis tasks such as exploring targeting usage at different levels, discovering useful or abnormal targeting combination patterns, finding competition from user behavior. In this paper, we design and implement an interactive visual analytics system named TargetingVis to visualize targeted advertising delivery data to face the challenges. After conducting a detailed requirements analysis with the domain experts from Tencent Inc., we design TargetingVis with four linked views: a novel chord diagram for cross-level exploration of targeting relations, a view for delving into the analysis of targeting combination patterns, an auxiliary view for displaying data indicators and a view to help gain insights into the behavior of advertisers. Finally, we evaluate the usability and efficiency through experiments based on real-world datasets. Keywords Targeted advertising Visual analytics Relations in hierarchical data User behavior analysis
D. Peng W. Tian M. Zhu (&) College of Computer Science, Sichuan University, Chengdu, China E-mail: [email protected] D. Peng E-mail: [email protected] W. Tian E-mail: [email protected] Y. Ren X. Lin Tencent Inc., Shenzhen, China E-mail: [email protected] X. Lin E-mail: [email protected] M. Li RMIT University, Melbourne, Australia E-mail: [email protected]
D. Peng et al.
1 Introduction With the rapid development of the Internet, online social networking platforms have become the gateway to the Internet for billions of users. These platforms accumulate rich user data which enable themselves to deliver online advertising efficiently. Nowadays, targeted advertising, which is based on products’ or people’s certain traits, accounts for the largest share of the online advertising market. This phenomenon indicates that advertisers are willing to spend more money on targeted advertising. However, some advertisers fail to use targeted advertising effectively due to incorrect audience identification. Hojjat et al. (2017) suggested that online advertising companies wish to know about their audiences (e.g., how many unique individuals were exposed to the advertisement) and understand how well their targeted strategies
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