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

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Third European Conference on Information Systems
Number of pages13
PublisherAIS eLibrary
Publication date29.05.2015
ISBN (print)978-3-00-050284-2
Publication statusPublished - 29.05.2015
Event23rd European Conference on Information Systems - ECIS 2015 - Münster, Germany
Duration: 26.05.201529.05.2015
Conference number: 23