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Bridging the Analytics Gap: Optimizing Content Performance using Actionable Knowledge Discovery

Published: 10 September 2024 Publication History

Abstract

Web analytics tools like Google Analytics are widely available, but website owners outside the eCommerce sector struggle to extract actionable insights from their data to curate and optimize content. This difficulty often arises from challenges in identifying and aligning objectives with standard website performance metrics provided by these tools, compounded by a lack of expertise in tool configuration. This study focuses on automated approaches that generate actionable insights for owners of content-driven websites, analyzing visitor attention at the most granular level by focusing on segments of web pages. It considers both the length of the page and different device types used to access these pages.
Existing research is augmented with four major contributions: First, a robust regression model to predict user behaviour based on the scroll behaviour of 850,000 visitors and more than 9 million data points from five diverse websites. Second, a dataset of measurements of web page lengths from a random sample of one million websites, for a better understanding of the relation between scroll behaviour and web page lengths. Third, an actionable knowledge discovery method for web analytics data of non-transactional websites that allows to identify deviations from expected visitor behaviour, enabling content optimization for those web analytics users who find it difficult to leverage their data today. Finally, an indicator for page performance that allows to compare page performance based on in-page visitor engagement. This research exemplifies the intersection of web analytics and intelligent content curation, showcasing a methodological framework that facilitates the generation of automated suggestions for digital content optimization, rooted in comprehensive behavioral data analysis.

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cover image ACM Conferences
HT '24: Proceedings of the 35th ACM Conference on Hypertext and Social Media
September 2024
415 pages
ISBN:9798400705953
DOI:10.1145/3648188
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 10 September 2024

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Author Tags

  1. actionable knowledge discovery
  2. content curation
  3. data mining
  4. digital analytics
  5. web analytics

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