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Modeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Modeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals. / Nottorf, Florian.

In: Electronic Commerce Research and Applications, Vol. 13, No. 1, 01.2014, p. 45-55.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Nottorf, F 2014, 'Modeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals', Electronic Commerce Research and Applications, vol. 13, no. 1, pp. 45-55. https://doi.org/10.1016/j.elerap.2013.07.004

APA

Nottorf, F. (2014). Modeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals. Electronic Commerce Research and Applications, 13(1), 45-55. https://doi.org/10.1016/j.elerap.2013.07.004

Vancouver

Nottorf F. Modeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals. Electronic Commerce Research and Applications. 2014 Jan;13(1):45-55. doi: 10.1016/j.elerap.2013.07.004

Bibtex

@article{d5cbc64a5c734d23b69c8938e9aed56e,
title = "Modeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals",
abstract = "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.",
keywords = "Informatics, Baysian mixture, Consumer behavior, Display advertising, Paid search advertising, Retargeting",
author = "Florian Nottorf",
year = "2014",
month = jan,
doi = "10.1016/j.elerap.2013.07.004",
language = "English",
volume = "13",
pages = "45--55",
journal = "Electronic Commerce Research and Applications",
issn = "1567-4223",
publisher = "Elsevier B.V.",
number = "1",

}

RIS

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 -

DOI