Click tracking

Click tracking is when user click behavior or user navigational behavior is collected in order to derive insights.[1][2] Click tracking is closely related to the terms click analytics, click data, and click-through rate (CTR).[1] [3] Currently, click behavior is commonly tracked using server logs which encompass click paths and clicked URLs.[2] This log is often presented in a standard format including information like the hostname, date, and username.[2] However, as technology develops, new software allows for in depth analysis of user click behavior using hypervideo tools.[1] Given that the internet can be considered a risky environment, research strives to understand why users click certain links and not others.[4] Research has also been conducted to explore the user experience of privacy with making user personal identification information personally anonymized and improving how data collection consent forms are written.[5][6]

Click tracking is relevant in several industries including Human-Computer Interaction (HCI), software engineering, and advertising.[1] [7] Email tracking, link tracking, web analytics, and user research are also related concepts and applications of click tracking.[8] A common utilization of click data from click tracking is to improve results from search engines to make them more relevant to users' needs. Click tracking employs many modern techniques such as machine learning and data mining.[9]

References
  1. Leiva, Luis (November 2013). "Web browsing behavior analysis and interactive hypervideo". ACM Transactions on the Web. 7: 1–28 – via ACM.
  2. Eirinaki, Magdalini (2003). "Web mining for web personalization". ACM Transactions on Internet Technology. 3: 1–27 – via ACM.
  3. Moon, Taesup (2012). "An Online Learning Framework for Refining Recency Search Results with User Click Feedback". ACM Transactions on Information Systems. 30: 1–28 – via ACM.
  4. Ogbanufe, Obi (2018). ""Just how risky is it anyway?" The role of risk perception and trust on click-through intention". Information Systems Management. 35: 182–200.
  5. Karegar, Farzaneh (2020). "The Dilemma of User Engagement in Privacy Notices". ACM Transactions on Privacy and Security. 23: 1–38 – via ACM.
  6. Romero-Tris, Cristina (2018). "Protecting Privacy in Trajectories with a User-Centric Approach". ACM Transactions on Knowledge Discovery from Data. 12: 1–27 – via ACM.
  7. Oentaryo, Richard (2014). "Detecting click fraud in online advertising: A data mining approach". The Journal of Machine Learning Research. 15: 99–140 – via ACM.
  8. Wu, ChienHsing (2018). "Emotion Induction in Click Intention of Picture Advertisement: A Field Examination". Journal of Internet Commerce. 17: 356–382.
  9. Jung, Seikyung (2007). "Click data as implicit relevance feedback in web search". Information Processing & Management. 43: 791–807.


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