TY - GEN
T1 - Online advertising revenue forecasting
T2 - 2021 IEEE International Conference on Big Data, Big Data 2021
AU - Wurfel, Max
AU - Han, Qiwei
AU - Kaiser, Maximilian
N1 - Funding Information:
This work was funded by Fundac ao para a Ciencia e a Tecnologia (UID/ECO/00124/2019, UIDB/00124/2020
Funding Information:
This work was funded by Fundac¸ão para a Ciência e a Tecnologia (UID/ECO/00124/2019, UIDB/00124/2020 and Social Sciences DataLab, PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences DataLab, PINFRA/22209/2016)
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Online advertising revenues account for an increasing share of publishers' revenue streams, especially for small and medium-sized publishers who depend on the advertisement networks of tech companies such as Google and Facebook. Thus publishers may benefit significantly from accurate online advertising revenue forecasts to better manage their website monetization strategies. However, publishers who only have access to their own revenue data lack a holistic view of the total ad market of publishers, which in turn limits their ability to generate insights into their own future online advertising revenues. To address this business issue, we leverage a proprietary database encompassing Google Adsense revenues from a large collection of publishers in diverse areas. We adopt the Temporal Fusion Transformer (TFT) model, a novel attention-based architecture to predict publishers' advertising revenues. We leverage multiple covariates, including not only the publisher's own characteristics but also other publishers' advertising revenues. Our prediction results outperform several benchmark deep-learning time-series forecast models over multiple time horizons. Moreover, we interpret the results by analyzing variable importance weights to identify significant features and self-attention weights to reveal persistent temporal patterns.
AB - Online advertising revenues account for an increasing share of publishers' revenue streams, especially for small and medium-sized publishers who depend on the advertisement networks of tech companies such as Google and Facebook. Thus publishers may benefit significantly from accurate online advertising revenue forecasts to better manage their website monetization strategies. However, publishers who only have access to their own revenue data lack a holistic view of the total ad market of publishers, which in turn limits their ability to generate insights into their own future online advertising revenues. To address this business issue, we leverage a proprietary database encompassing Google Adsense revenues from a large collection of publishers in diverse areas. We adopt the Temporal Fusion Transformer (TFT) model, a novel attention-based architecture to predict publishers' advertising revenues. We leverage multiple covariates, including not only the publisher's own characteristics but also other publishers' advertising revenues. Our prediction results outperform several benchmark deep-learning time-series forecast models over multiple time horizons. Moreover, we interpret the results by analyzing variable importance weights to identify significant features and self-attention weights to reveal persistent temporal patterns.
KW - Deep Learning
KW - Digital Marketing
KW - Online Advertisement
KW - Time Series Forecasting
UR - http://www.scopus.com/inward/record.url?scp=85125345879&partnerID=8YFLogxK
U2 - 10.1109/BigData52589.2021.9672010
DO - 10.1109/BigData52589.2021.9672010
M3 - Conference contribution
AN - SCOPUS:85125345879
T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
SP - 1980
EP - 1989
BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
A2 - Chen, Yixin
A2 - Ludwig, Heiko
A2 - Tu, Yicheng
A2 - Fayyad, Usama
A2 - Zhu, Xingquan
A2 - Hu, Xiaohua Tony
A2 - Byna, Suren
A2 - Liu, Xiong
A2 - Zhang, Jianping
A2 - Pan, Shirui
A2 - Papalexakis, Vagelis
A2 - Wang, Jianwu
A2 - Cuzzocrea, Alfredo
A2 - Ordonez, Carlos
PB - Institute of Electrical and Electronics Engineers (IEEE)
Y2 - 15 December 2021 through 18 December 2021
ER -