Tom's Analytics

I am a panelist at this year’s Politics Online Conference next week.

The title of my session is “Social Media Analytics: Monitor, Measure and Manage Your Reputation on the Wild Wild Web of Social Media.”  I will be sitting on a panel with the following people:

Kate Kaye, Clickz (Moderator)
Peter Corbett, iStrategyLabs
Stan Magniant, Linkfluence
Joe Mansour, David All Group

I am extremely excited about our potential discussion since we are all hands-on practitioners with different perspectives and experiences related to Social Media and politics.

My session runs from 2:00pm – 3:00pm on Tuesday, April 21st.  The conference runs from Monday, April 20 through the 21st in the Reagan Building – hopefully I will see you there!

Earl Devaney, chairman of the Recovery Accountability and Transparency Board, has a tough job – bringing transparency to the government outlays mandated by the recently-passed American Recovery and Reinvestment Act.  The main vehicle envisioned to bring this information to the public is Recovery.gov, which is administered by Devaney’s board.

Last week, Devaney was before Congress touting the demand for this information.  His testimony was picked up by several news outlets, including these stories at CNN, Federal Computer Week, and the L.A. Times.

In his testimony, he described traffic levels at Recovery.gov to be about “4,000 hits per second” (emphasis mine).  This means that in a day, the site would get about 350 million hits per day (4,000 * 60 seconds/minute * 60 minutes/hour * 24 hours per day).  To most people, this would imply a traffic level equivalent to every internet user in the United States visiting the site more than one time, every single day.  However, this statistic is misleading.

A “hit”, commony confused with a “page view” or a “visit”, is a single HTTP request from a web server to a browser for a specific resource.  HTML pages, images, flash objects, CSS sheets, external scripts, and other other files requested by a browser to render a page each generate a single hit upon their request.  The net effect is that single pages can generate several hits for the display of a single page to an individual user.

In the case of Recovery.gov, the home page loads 30 files to fully render.  If the 4,000 hits statistic is accurate, then there are really about 11.5 million page views per day.  Assuming that the average visitor views 5 pages on the site (which is a big assumption), the true number of visits to the site is more like 2.3 million per day, or about 27 new visits per second.

27 is a lot less impressive than 4,000, but it may be a much better picture of the true nature of traffic to the site.

In a great article for Marketing Profs, Avinash Kaushik asks: Is Bounce Rate “the Sexiest Web Metric Ever?”

I agree with Avinash in that Bounce Rate is one of the most important metrics for any type of site. A high bounce rate negatively affects Time Spent on Site in ways that may not be immediately apparent; have a look at my article on Life Tables and Time Spent on Site to see how. However, we need to take a closer look at our data collection methodology before we assign too much importance to this metric.

Bounce Rate Defined

The classic definition of a Bounce is a visitor that views a single page and then immediately leaves without viewing any other pages. A site’s Bounce Rate is the ratio of single page visits by the total number of visits. Anil Batra defines it (in his post “Bounce Rate Demystified“) this way as well, but with an added definition adding people that visit a single page, but exceed some predefined time limit on that page.

Problems with Bounce Rate
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This summer, Neilen/NetRatings declared the “Total Minutes” metric as the best measure of website engagement. This is certainly debatable, but the purpose of this post is to illustrate a new method of understanding the Time Spent on Site metric by applying a technique used by people in the Actuarial Sciences and Demography: the Life Table.

Some Background on Life Tables

The Life Table is a tool typically used to analyze and predict patterns of mortality among populations. The most familiar measure that gets generated by a Life Table is Life Expectancy. Life Expectancy is always great fodder for news headlines; at the time of this article’s publication, the Centers for Disease Control and Prevention just announced that Average Life Expectancy for people in the U.S. is nearly 78 years. This statistic was calculated using a Life Table.
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