skip to main content
10.1145/1753326.1753687acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Measuring the user experience on a large scale: user-centered metrics for web applications

Published:10 April 2010Publication History

ABSTRACT

More and more products and services are being deployed on the web, and this presents new challenges and opportunities for measurement of user experience on a large scale. There is a strong need for user-centered metrics for web applications, which can be used to measure progress towards key goals, and drive product decisions. In this note, we describe the HEART framework for user-centered metrics, as well as a process for mapping product goals to metrics. We include practical examples of how HEART metrics have helped product teams make decisions that are both data-driven and user-centered. The framework and process have generalized to enough of our company's own products that we are confident that teams in other organizations will be able to reuse or adapt them. We also hope to encourage more research into metrics based on large-scale behavioral data.

References

  1. Akers, D. et al. (2009). Undo and Erase Events as Indicators of Usability Problems. Proc of CHI 2009, ACM Press, pp. 659--668. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Burby, J. & Atchison, S. (2007). Actionable Web Analytics. Indianapolis: Wiley Publishing, Inc.Google ScholarGoogle Scholar
  3. Chi, E. et al. (2002). LumberJack: Intelligent Discovery and Analysis of Web User Traffic Composition. Proc of WebKDD 2002, ACM Press, pp. 1--15.Google ScholarGoogle Scholar
  4. Dean, J. & Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51 (1), pp. 107--113. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Google Analytics: http://www.google.com/analyticsGoogle ScholarGoogle Scholar
  6. Grimes, C. et al. (2007). Query Logs Alone are not Enough. Proc of WWW 07 Workshop on Query Log Analysis: http://querylogs2007.webir.orgGoogle ScholarGoogle Scholar
  7. Gwizdka, J. & Spence, I. (2007). Implicit Measures of Lostness and Success in Web Navigation. Interacting with Computers 19(3), pp. 357--369. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Hadoop: http://hadoop.apache.org/coreGoogle ScholarGoogle Scholar
  9. Kaushik, A. (2007). Web Analytics: An Hour a Day. Indianapolis: Wiley Publishing, Inc. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Kohavi, R. et al. (2007). Practical Guide to Controlled Experiments on the Web. Proc of KDD 07, ACM Press, pp. 959--967. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Omniture: http://www.omniture.comGoogle ScholarGoogle Scholar
  12. Pike, R. et al. (2005). Interpreting the Data: Parallel Analysis with Sawzall. Scientific Programming (13), pp. 277--298. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Tullis, T. & Albert, W. (2008). Measuring the User Experience. Burlington: Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. UserZoom: http://www.userzoom.comGoogle ScholarGoogle Scholar
  15. Weischedel, B. & Huizingh, E. (2006). Website Optimization with Web Metrics: A Case Study. Proc of ICEC 06, ACM Press, pp. 463--470. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Measuring the user experience on a large scale: user-centered metrics for web applications

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
      April 2010
      2690 pages
      ISBN:9781605589299
      DOI:10.1145/1753326

      Copyright © 2010 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 10 April 2010

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate6,199of26,314submissions,24%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader