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Poulos, Marios, Korfiatis, Nikolaos 
ORCID: https://orcid.org/0000-0001-6377-4837 and Papavlassopoulos, Sozon
(2020)
Assessing stationarity in web analytics: A study of bounce rates.
Expert Systems, 37 (3).
ISSN 0266-4720
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Abstract
Evidence-based methods for evaluating marketing interventions such as A/B testing have become standard practice. However, the pitfalls associated with the misuse of this decision-making instrument are not well understood by managers and analytics professionals. In this study, we assess the impact of stationarity on the validity of samples from conditioned time series, which are abundant in web metrics. Such a prominent metric is the bounce rate, which is prevalent in assessing engagement with web content as well as the performance of marketing touchpoints. In this study, we show how to control for stationarity using an algorithmic transformation to calculate the optimum sampling period. This distance is based on a novel stationary ergodic process that considers that a stationary series presents reversible symmetric features and is calculated using a dynamic time warping algorithm in a self-correlation procedure. This study contributes to the expert and intelligent systems literature by demonstrating a robust method for sub-sampling time-series data, which are critical in decision making.
| Item Type: | Article |
|---|---|
| Additional Information: | Part of Special Issue: New data envelopment analysis models for assessing sustainability: Part 1: A dynamic data envelopment analysis approach |
| Uncontrolled Keywords: | dtw,series,time,bounce rate,numerical analysis,stationary progress,time series |
| Faculty \ School: | Faculty of Social Sciences > Norwich Business School |
| UEA Research Groups: | Faculty of Social Sciences > Research Groups > Innovation, Technology and Operations Management Faculty of Social Sciences > Research Centres > Centre for Competition Policy |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 13 Nov 2019 09:30 |
| Last Modified: | 21 Apr 2023 00:13 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/72949 |
| DOI: | 10.1111/exsy.12502 |
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