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. Discriminative clustering for market segmentation
  2. Turning Good Intentions Into Actions by Using the Health Action Process Approach to Predict Adherence to Internet-Based Depression Prevention
  3. A common European asylum system? How variation in Member States’ administrative capacity undermines EU asylum harmonisation
  4. Optimal control strategies for PMSM with a decoupling super twisting SMC and inductance estimation in the presence of saturation
  5. Timing matters: Distinct effects of nitrogen and phosphorus fertilizer application timing on root system architecture responses
  6. Promoting physical activity in worksite settings
  7. Intelligent software system for replacing a force sensor in the case of clearance measurement
  8. Effectiveness of Web- and Mobile-Based Treatment of Subthreshold Depression With Adherence-Focused Guidance
  9. Vocational identity as a mediator of the relationship between core self-evaluations and life and job satisfaction
  10. The explanatory power of Carnegie Classification in predicting engagement indicators
  11. Support from the Internet for Individuals with Mental Disorders
  12. Vibration analysis based on the spectrum kurtosis for adjustment and monitoring of ball bearing radial clearance
  13. Mechanisms of teleological change
  14. Energy-aware system design for autonomous wireless sensor nodes
  15. Ein echter Gedanke reicht weit
  16. Give and take frames in shared-resource negotiations
  17. Dimension theoretical properties of generalized Baker's transformations
  18. EU decision-making in asylum policy
  19. At what price? IP-related thoughts on new business models for space information
  20. Assessing the costs and cost-effectiveness of ICare internet-based interventions (protocol)
  21. Facing the heat
  22. Crowdsourcing Hypothesis Tests
  23. Salivary cues
  24. Control of Permanent Magnet Synchronous Motors for Track Applications
  25. Interactivity, Interpassivity and Possibilities Beyond Dichotomy
  26. CoLab
  27. Dynamische Bestandsdimensionierung
  28. Leverage points for sustainability transformation
  29. Antidepressants
  30. A klímavédelem alapvető feladatai
  31. Editorial
  32. Rechtschreiben unterrichten
  33. So macht man Karriere
  34. Integrating highly diverse invertebrates into broad-scale analyses of cross-taxon congruence across the Palaearctic
  35. The depositional environments of Schöningen 13 II-4 and their archaeological implications
  36. Kooperation mit Migranteneltern
  37. I will probably fail
  38. 'YouTubers unite': collective action by YouTube content creators