A Sept. 12 Pew Center study found that “Americans are spending more time following the news than over the past decade.” Great news – or is this yet another misleading key performance indicator, as my previous blog post about time spent on site might suggest?
No – I like the Pew Center study. It’s a study of attitudes and feelings. Good old-fashioned survey research (with all of its mind-numbing statistical sampling), is an essential component of web analytics. Web site traffic data is audience behavior – the “what.” News orgs have to have attitudinal research to understand the “why” so they can attract audiences that aren’t coming to their sites.
The data you get from Google Analytics or Omniture is enticing, isn’t it? (Work with me, here….) Oh wow, we can track every click! Ah, yes, we can track every click – but we can ONLY track clicks on OUR site, not on anyone else’s.
A time-on-site calculation can only be harvested for you if someone clicks on a page in your site and generates a page view that’s counted by your Google Analytics/Omniture account. Time-on-site is the time in between the first page clicked on YOUR site and the last page clicked on YOUR site.
This means:
1. If someone clicks on YOUR site and then immediately goes to another site (a bounce),
it’s not included by Google Analytics/Omniture in the time- on-site calculation. It’s like it never existed, time-on-site-wise.
It IS counted as one visit and as one page view. So that means that all of those people who come to your site regularly (you know, the ones we really like) just to get the latest on a story aren’t counted in time-on-site - and they should be.
2. If someone is on his/her third page in your site and opens another tab and goes to another site for twenty minutes before returning to your site and clicking on another two pages, those twenty minutes are included in time-on-site – and they shouldn’t be.
3. The time a person spends on the last page of your site isn’t counted.
If someone clicks through a few pages on your site and spends 15
minutes utterly absorbed in a story before leaving your site to go pay
bills online, those 15 minutes aren’t included in time-on-site – and they should be.
So, time-spent-on-site is always either over- or under-counted. And you’ll never know which – this makes this metric utterly unreliable as an indicator of success or failure. So you can’t make a decision with this data because you can’t know whether your action – a section added, the number of long-form videos reduced – caused time spent to go up or down.
More importantly, these days it really doesn’t matter how much time people actually spend on a news site. What matters much, much more is whether people are engaged with the news, whether they believe news sites are an essential component to their lives, so much so that they come back repeatedly, rate a story with five stars, leave comments, click on an ad, and otherwise use the site. It doesn’t matter whether they spend three seconds or three hours.
That’s what makes the Pew Center finding so exciting (surely you’re still with me on how great web analytics is?). People actually said they’re spending more time with the news now than they did a decade ago. It doesn’t matter whether they actually are (!) – they believe they are.
I wish every news org could afford its own Pew Center-like attitudinal research study so it could track how engaged its own targeted audiences are (or aren’t), and to understand how to get and retain new audiences. The information wouldn’t always be pretty, but at least it would be data that could make a difference.


Video metrics for everyone!
YouTube‘s become a verb and a household name, but I’ll always see it as an organization that’s brought metrics into the lives of the common people (those who have broadband Internet, anyway). The “Most Popular” and “Featured Videos” are seen worldwide,
sometimes garnering millions of views. “Hey, did you see….” is usually accompanied by something like “…and it has x million views on YouTube!”
Number of views is great for little else other than bragging rights. It’s one of the “famous” metrics (web analytics guru Avinash Kaushik‘s term) that “are staring you in the face when you crack open any analytics tool” but “barely contain any insight.”
Yep, for anyone in the content business, number of views is right up there with hall of famers number of page views and monthly unique visitors.
YouTube has pushed all of its account holders – no matter how amateur – to use meaningful metrics. In March 2008 it launched Insight, its “video analytics tool for all users,”
along with some almost-preachy instructions on how to use metrics to get more people to watch your videos and, of course, come more often to YouTube.
The Insight tool allows you to track “community engagements” (there’s that word again) in terms of ratings, comments, and favorites. YouTube doesn’t want you to settle for people just watching your video. People have to show, in a measurable way, that they not only watched it but also reacted to it.
At the very least people should give a star rating (one is bad, five is good). Rating is easy, quick and anonymous. Tagging a video as a favorite is the next rung. And if they’re really engaged, they’ll leave comments.
But, as anyone who’s ever spent any time at all on YouTube knows, many comments are spam, obscene and irrelevant – just noise. But the value of social media metrics is in looking beyond what James Kobelius in Information Management points out is an “often low and laughable” signal-to-noise ratio.
Kobelius notes that “if you crawl, correlate, categorize, mine, and explore it with the
right tools….[this unstructured information] can yield unexpected insights….The intelligence value of any individual tweet [or comment] in isolation is
negligible….Intelligence emerges from the aggregate.”
If you can stomach a few obscenities, look at this thought in Encyclopaedia Dramatica about YouTube view fraud and how the ratio of VPC, or views per comment, “is the most accurate way to determine if anyone” cares. “A high VPC usually means view fraud has been committed.”
The example in ED shows that a video with 136,097 views and 3,529 comments has a VPC of 38.7, a low number that indicates this is a video “that people actually find funny.” The video with 296,413 views, 541 comments and thus a VPC of 547.9 is probably something nobody really cares about.
I calculated some VPCs from this week’s “Most Popular”
videos and came up with some numbers that I don’t know what to do with yet. To see if VPC can be used as a key performance indicator, I’ll need to calculate VPCs and crawl through the cacophony of a variety of news videos. VPC may never be “famous,” but it might be insightful.
Posted in Comments, Misused metrics, Ratings, Social media, YouTube | Leave a Comment »