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Predicting website audience demographics for web advertising targeting using multi-website clickstream data

Koen De Bock UGent and Dirk Van den Poel UGent (2010) FUNDAMENTA INFORMATICAE. 98(1). p.49-70
abstract
Several recent studies have explored the virtues of behavioral targeting and personalization for online advertising. In this paper, we add to this literature by proposing a cost-effective methodology for the prediction of demographic website visitor profiles that can be used for web advertising targeting purposes. The methodology involves the transformation of website visitors' clickstream patterns to a set of features and the training of Random Forest classifiers that generate predictions for gender, age, level of education and occupation category. These demographic predictions can support online advertisement targeting (i) as an additional input in personalized advertising or behavioral targeting, or (ii) as an input for aggregated demographic website visitor profiles that support marketing managers in selecting websites and achieving an optimal correspondence between target groups and website audience composition. The proposed methodology is validated using data from a Belgian web metrics company. The results reveal that Random Forests demonstrate superior classification performance over a set of benchmark algorithms. Further, the ability of the model set to generate representative demographic website audience profiles is assessed. The stability of the models over time is demonstrated using out-of-period data.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
out-of-period validation, Random Forests, ensemble classification, clickstream analysis, web advertising, web user profiling, demographic targeting, demographic prediction, AREA, SITES, EXPOSURE, BEHAVIOR, SELECTION, CLASSIFICATION, ONLINE, ROC CURVE, RANDOM FORESTS, BANNER ADVERTISEMENTS
journal title
FUNDAMENTA INFORMATICAE
Fundam. Inform.
volume
98
issue
1
pages
49 - 70
Web of Science type
Article
Web of Science id
000276350100005
JCR category
MATHEMATICS, APPLIED
JCR impact factor
0.522 (2010)
JCR rank
172/235 (2010)
JCR quartile
3 (2010)
ISSN
0169-2968
DOI
10.3233/FI-2010-216
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
967442
handle
http://hdl.handle.net/1854/LU-967442
date created
2010-06-01 13:33:06
date last changed
2016-12-19 15:40:14
@article{967442,
  abstract     = {Several recent studies have explored the virtues of behavioral targeting and personalization for online advertising. In this paper, we add to this literature by proposing a cost-effective methodology for the prediction of demographic website visitor profiles that can be used for web advertising targeting purposes. The methodology involves the transformation of website visitors' clickstream patterns to a set of features and the training of Random Forest classifiers that generate predictions for gender, age, level of education and occupation category. These demographic predictions can support online advertisement targeting (i) as an additional input in personalized advertising or behavioral targeting, or (ii) as an input for aggregated demographic website visitor profiles that support marketing managers in selecting websites and achieving an optimal correspondence between target groups and website audience composition. The proposed methodology is validated using data from a Belgian web metrics company. The results reveal that Random Forests demonstrate superior classification performance over a set of benchmark algorithms. Further, the ability of the model set to generate representative demographic website audience profiles is assessed. The stability of the models over time is demonstrated using out-of-period data.},
  author       = {De Bock, Koen and Van den Poel, Dirk},
  issn         = {0169-2968},
  journal      = {FUNDAMENTA INFORMATICAE},
  keyword      = {out-of-period validation,Random Forests,ensemble classification,clickstream analysis,web advertising,web user profiling,demographic targeting,demographic prediction,AREA,SITES,EXPOSURE,BEHAVIOR,SELECTION,CLASSIFICATION,ONLINE,ROC CURVE,RANDOM FORESTS,BANNER ADVERTISEMENTS},
  language     = {eng},
  number       = {1},
  pages        = {49--70},
  title        = {Predicting website audience demographics for web advertising targeting using multi-website clickstream data},
  url          = {http://dx.doi.org/10.3233/FI-2010-216},
  volume       = {98},
  year         = {2010},
}

Chicago
De Bock, Koen, and Dirk Van den Poel. 2010. “Predicting Website Audience Demographics for Web Advertising Targeting Using Multi-website Clickstream Data.” Fundamenta Informaticae 98 (1): 49–70.
APA
De Bock, K., & Van den Poel, D. (2010). Predicting website audience demographics for web advertising targeting using multi-website clickstream data. FUNDAMENTA INFORMATICAE, 98(1), 49–70.
Vancouver
1.
De Bock K, Van den Poel D. Predicting website audience demographics for web advertising targeting using multi-website clickstream data. FUNDAMENTA INFORMATICAE. 2010;98(1):49–70.
MLA
De Bock, Koen, and Dirk Van den Poel. “Predicting Website Audience Demographics for Web Advertising Targeting Using Multi-website Clickstream Data.” FUNDAMENTA INFORMATICAE 98.1 (2010): 49–70. Print.