Modeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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in: Electronic Commerce Research and Applications, Jahrgang 13, Nr. 1, 01.2014, S. 45-55.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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TY - JOUR
T1 - Modeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals
AU - Nottorf, Florian
PY - 2014/1
Y1 - 2014/1
N2 - The evaluation of online marketing activities using standalone metrics does not explain the development of consumer behavior over time, although it is of primary importance to allocate and optimize financial resources among multiple advertising channels. We develop a binary logit model with a Bayesian mixture approach to demonstrate consumer clickstreams across multiple online advertising channels. Therefore, a detailed user-level dataset from a large financial service provider is analyzed. We find both differences in the effects of repeated advertisement exposure across multiple types of display advertising as well as positive effects of interaction between display and paid search advertising influencing consumer click probabilities. We identify two consumer types with different levels of susceptibility to online advertising (resistant vs. susceptible consumers) and show that the knowledge of consumers individual click probabilities can support companies in managing display advertising campaigns.
AB - The evaluation of online marketing activities using standalone metrics does not explain the development of consumer behavior over time, although it is of primary importance to allocate and optimize financial resources among multiple advertising channels. We develop a binary logit model with a Bayesian mixture approach to demonstrate consumer clickstreams across multiple online advertising channels. Therefore, a detailed user-level dataset from a large financial service provider is analyzed. We find both differences in the effects of repeated advertisement exposure across multiple types of display advertising as well as positive effects of interaction between display and paid search advertising influencing consumer click probabilities. We identify two consumer types with different levels of susceptibility to online advertising (resistant vs. susceptible consumers) and show that the knowledge of consumers individual click probabilities can support companies in managing display advertising campaigns.
KW - Informatics
KW - Baysian mixture
KW - Consumer behavior
KW - Display advertising
KW - Paid search advertising
KW - Retargeting
UR - http://www.scopus.com/inward/record.url?scp=84893774172&partnerID=8YFLogxK
U2 - 10.1016/j.elerap.2013.07.004
DO - 10.1016/j.elerap.2013.07.004
M3 - Journal articles
VL - 13
SP - 45
EP - 55
JO - Electronic Commerce Research and Applications
JF - Electronic Commerce Research and Applications
SN - 1567-4223
IS - 1
ER -