Four metrics will be looked at and weighed against analyses of page design.*/**

• Bounce%
  - “A bounce occurs when a web site visitor only views a single page on a website, that is, the visitor leaves a site without visiting any other pages.” 
  - Avinash Kaushik, self-described ‘author, digital marketing evangelist - Google co-founder’, suggests B% is one of the most useful statistics to determine whether or not the audience of a ‘content site’ are engaging with the content.
• Pages per Visit
  -Self explanatorily enough, PpV shows the number of pages visitors see per visit to a news site. High PpV typically means low B% - though not necessarily. PpV is generally another good indicator of reader interest in content of news and other content-driven sites, according to Kaushik (and infinitely linkable other sources)/
• Time on Page 
  -Again self-explanatory, ToP allows us to see how much time readers spend on any given page. When weighed against what design features appear frequently in home and news pages with high ToP, design features that ‘hold attention’ might be identifiable. 
• Click-Stream
  -CS is in some ways a back-up metric for the project. CS reveals the websites users visit before they arrive at the news site in question as well as the sites they visit once they leave. It can be useful for observing a news site’s network (who sends them traffic, who they send traffic). CS can also support or check referral statistics. 
  

As well as being useful metrics for content sites generally, there is an issue of practicality with these four standard metrics as well. Since part of the methodology[«<hyperlink] used for this paper relies on news site administrators sharing traffic data - and because that data will likely be accrued through various analytics services - it is important to define a finite set of data, generally available in disparate analytics platforms and commonly known to administrators.

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*This dissertation will only look at news sites’ homepage and news page designs and layouts.

**Important: Kaushik says “averages hide the truth effectively.” This is why all metrics for this paper (except click-stream) need to be looked at as page averages as well in distributed data form. 
Below: example of the same statistic (from a real site) averaged and distributed