Capturing “Human Bandwidth”: A Multidimensional Model for Measuring Attention on Web Sites

The diffusion and evolution of the Internet has been mirrored by an evolving science of how to measure traffic online. However, after many years of development, Web publishers and online advertisers still find the current metrics are not adequately integrated to provide a holistic picture of audience attention on the Web. This study proposes a conceptual model for measuring attention on the Web on 5 dimensions at 5 different levels of analysis: visibility (share per market), popularity (unique audience per site), loyalty (visits per person), depth (pages per visit), and stickiness (time per page). An empirical analysis of major news and information sites' traffic data identifies distinct attention patterns characterizing different types of Web sites. For example, news portals and television news sites enjoyed the most popularity. Portals and weather sites scored the highest on loyalty. Magazine sites spurred deeper visits (more pages), and weather sites outperformed other sites in terms of stickiness on a per-page basis. This model promises to be commodiously valuable for assessing a Web site's performance on all distinct dimensions of audience attention, selecting the right metrics by which to gauge performance against competitors, and by establishing benchmarks by which that performance can be quantified.

[1]  Louisa Ha,et al.  Audience Evolution: New Technologies and the Transformation of Media Audiences , 2011 .

[2]  Avinash Kaushik,et al.  Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity , 2009 .

[3]  Thomas H. Davenport,et al.  The Attention economy , 2001, UBIQ.

[4]  Ming-Hui Huang,et al.  Web performance scale , 2005, Inf. Manag..

[5]  Bernard J. Jansen,et al.  The Methodology of Search Log Analysis , 2009 .

[6]  M. Strand.  The Mere Exposure Effect and In-Game Advertising , 2013 .

[7]  Bernard J. Jansen,et al.  A Review of Methodologies for Analyzing Websites , 2010 .

[8]  C. Sunstein.  Republic.com 2.0 , 2007 .

[9]  Christopher E. Beaudoin.  The internet's impact on international knowledge , 2008, New Media Soc..

[10]  Phil M. MacGregor,et al.  TRACKING THE ONLINE AUDIENCE , 2007 .

[11]  Beatriz Plaza,et al.  Monitoring web traffic source effectiveness with Google Analytics: An experiment with time series , 2009, Aslib Proc..

[12]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1996, SIGMETRICS '96.

[13]  J. Zaller,et al.  WHO GETS THE NEWS? ALTERNATIVE MEASURES OF NEWS RECEPTION AND THEIR IMPLICATIONS FOR RESEARCH , 1993 .

[14]  Philip M. Napoli.  Audience Economics: Media Institutions and the Audience Marketplace , 2003 .

[15]  Dietmar Janetzko,et al.  NET Objectivity , Reliability , and Validity of Search Engine Count Estimates , 2008 .

[16]  Brian Clifton,et al.  Advanced Web Metrics with Google Analytics , 2008 .

[17]  David Tewksbury.  The Seeds of Audience Fragmentation: Specialization in the Use of Online News Sites , 2005 .

[18]  James G. Webster,et al.  Ratings analysis: The theory and practice of audience research , 2006 .

[19]  Deborah Kania,et al.  Internet World Guide to One-To-One Web Marketing , 1998 .

[20]  Michael H. Goldhaber,et al.  The Attention Economy and the Net , 1997, First Monday.

[21]  Merrill Morris.  The Internet as Mass Medium , 1996, J. Comput. Mediat. Commun..

[22]  Nick Koudas,et al.  Early online identification of attention gathering items in social media , 2010, WSDM '10.