How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis. / Stange, Martin; Funk, Burkhardt.
Proceedings of the Twenty-Third European Conference on Information Systems. AIS eLibrary, 2015.

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Stange, M & Funk, B 2015, How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis. in Proceedings of the Twenty-Third European Conference on Information Systems. AIS eLibrary, 23rd European Conference on Information Systems - ECIS 2015, Münster, Deutschland, 26.05.15. https://doi.org/10.18151/7217484

APA

Stange, M., & Funk, B. (2015). How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis. In Proceedings of the Twenty-Third European Conference on Information Systems AIS eLibrary. https://doi.org/10.18151/7217484

Vancouver

Stange M, Funk B. How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis. in Proceedings of the Twenty-Third European Conference on Information Systems. AIS eLibrary. 2015 doi: 10.18151/7217484

Bibtex

@inbook{02f02f601bbf4d3c855ca7f8227751ad,
title = "How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis",
abstract = "Extracting value from big data is one of today{\textquoteright}s business challenges. In online marketing, for instance, advertisers use high volume clickstream data to increase the efficiency of their campaigns. To prevent collecting, storing, and processing of irrelevant data, it is crucial to determine how much data to analyze to achieve acceptable model performance. We propose a general procedure that employs the learning curve sampling method to determine the optimal sample size with respect to cost/benefit considerations. Applied in two case studies, we model the users' click behavior based on clickstream data and offline channel data. We observe saturation effects of the predictive accuracy when the sample size is increased and, thus, demonstrate that advertisers only have to analyze a very small subset of the full dataset to obtain an acceptable predictive accuracy and to optimize profits from advertising activities. In both case studies we observe that a random intercept logistic model outperforms a non-hierarchical model in terms of predictive accuracy. Given the high infrastructure costs and the users' growing awareness for tracking activities, our results have managerial implications for companies in the online marketing field. ",
keywords = "Business informatics, Big Data, Online Marketing, User Journey Analysis, Learning Curve, Bayesian Models",
author = "Martin Stange and Burkhardt Funk",
year = "2015",
month = may,
day = "29",
doi = "10.18151/7217484",
language = "English",
isbn = "978-3-00-050284-2",
booktitle = "Proceedings of the Twenty-Third European Conference on Information Systems",
publisher = "AIS eLibrary",
address = "United States",
note = "23rd European Conference on Information Systems - ECIS 2015, ECIS conference 2015 ; Conference date: 26-05-2015 Through 29-05-2015",
url = "https://www.ercis.org/, http://www.ecis2015.eu/",

}

RIS

TY - CHAP

T1 - How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis

AU - Stange, Martin

AU - Funk, Burkhardt

N1 - Conference code: 23

PY - 2015/5/29

Y1 - 2015/5/29

N2 - Extracting value from big data is one of today’s business challenges. In online marketing, for instance, advertisers use high volume clickstream data to increase the efficiency of their campaigns. To prevent collecting, storing, and processing of irrelevant data, it is crucial to determine how much data to analyze to achieve acceptable model performance. We propose a general procedure that employs the learning curve sampling method to determine the optimal sample size with respect to cost/benefit considerations. Applied in two case studies, we model the users' click behavior based on clickstream data and offline channel data. We observe saturation effects of the predictive accuracy when the sample size is increased and, thus, demonstrate that advertisers only have to analyze a very small subset of the full dataset to obtain an acceptable predictive accuracy and to optimize profits from advertising activities. In both case studies we observe that a random intercept logistic model outperforms a non-hierarchical model in terms of predictive accuracy. Given the high infrastructure costs and the users' growing awareness for tracking activities, our results have managerial implications for companies in the online marketing field.

AB - Extracting value from big data is one of today’s business challenges. In online marketing, for instance, advertisers use high volume clickstream data to increase the efficiency of their campaigns. To prevent collecting, storing, and processing of irrelevant data, it is crucial to determine how much data to analyze to achieve acceptable model performance. We propose a general procedure that employs the learning curve sampling method to determine the optimal sample size with respect to cost/benefit considerations. Applied in two case studies, we model the users' click behavior based on clickstream data and offline channel data. We observe saturation effects of the predictive accuracy when the sample size is increased and, thus, demonstrate that advertisers only have to analyze a very small subset of the full dataset to obtain an acceptable predictive accuracy and to optimize profits from advertising activities. In both case studies we observe that a random intercept logistic model outperforms a non-hierarchical model in terms of predictive accuracy. Given the high infrastructure costs and the users' growing awareness for tracking activities, our results have managerial implications for companies in the online marketing field.

KW - Business informatics

KW - Big Data

KW - Online Marketing

KW - User Journey Analysis

KW - Learning Curve

KW - Bayesian Models

U2 - 10.18151/7217484

DO - 10.18151/7217484

M3 - Article in conference proceedings

SN - 978-3-00-050284-2

BT - Proceedings of the Twenty-Third European Conference on Information Systems

PB - AIS eLibrary

T2 - 23rd European Conference on Information Systems - ECIS 2015

Y2 - 26 May 2015 through 29 May 2015

ER -

Links

DOI

Zuletzt angesehen

Publikationen

  1. Studying properties of water data using manifold-aware anomaly detectors
  2. HAWK - hybrid question answering using linked data
  3. Denoising and harmonic detection using nonorthogonal wavelet packets in industrial applications
  4. Life satisfaction in Germany after reunification: Additional insights on the pattern of convergence
  5. Integrating inductive and deductive analysis to identify and characterize archetypical social-ecological systems and their changes
  6. A Genetic Algorithm for the Dynamic Management of Cellular Reconfigurable Manufacturing Systems
  7. A robust model predictive control using a feedforward structure for a hybrid hydraulic piezo actuator in camless internal combustion engines
  8. Octanol-Water Partition Coefficient Measurement by a Simple 1H NMR Method
  9. Challenging the status quo of accelerator research: Concluding remarks
  10. Behavior in the context of control
  11. Quality Assurance of Specification - The Users Point of View
  12. Does online-delivered Cognitive Behavioural Therapy for Insomnia improve insomnia severity in nurses working shifts? Protocol for a randomised-controlled trial
  13. Contested future-making in containment: temporalities, infrastructures and agency
  14. The relation of flow-experience and physiological arousal under stress - can u shape it?
  15. Maintaining the Reputation of Reputation
  16. From Values to Emotions
  17. Grand theories and mid-range theories
  18. Recognizing Guarantees and Assurances of Non-Repetition
  19. Functional trait similarity of native and invasive herb species in subtropical China-Environment-specific differences are the key
  20. Principled Interpolation in Normalizing Flows
  21. Cross-Fertilizing Qualitative Perspectives on Effects of a Mindfulness-Based Intervention: An Empirical Comparison of Four Methodical Approaches
  22. Collaboration and Open Science Initiatives in Primate Research
  23. Working time flexibility and work-life balance
  24. Oxygen dependence in the photoreaction of the pesticide metamitron
  25. Cyberpunk
  26. Gutes Leben vor Ort
  27. Dispute and morality in the perception of societal risks: extending the psychometric model
  28. Will participation foster the successful implementation of water framework directive?
  29. Deformation and Anchoring of AA 2024-T3 rivets within thin printed circuit boards
  30. Species richness stabilizes productivity via asynchrony and drought-tolerance diversity in a large-scale tree biodiversity experiment
  31. Das Problem der Unbestimmtheit des Rechts
  32. Pesticide and metabolite fate, release and transport modelling at catchment scale
  33. Forest Ecosystems: A functional and biodiversity perspective