Web Stats – June 2009

Chandoo’s Pointy Haired Dilbert blog had its best month ever in June, and he posted some stats to prove it. Congratulations, Chandoo!

June 2009 Stats for Pointy Haired Dilbert

Pointy Haired Dilbert statistics for June 2009

John Walkenbach responded with his own visitor stats.

June 2009 Stats for The Spreadsheet Page

The Spreadsheet Page statistics for June 2009

John asked, so here are my June stats, first for the entire Peltier Tech website (which includes the blog), then for the PTS Blog.

June 2009 Stats for Peltier Tech Web Site

PeltierTech website statistics for June 2009

June 2009 Stats for PTS Blog

PTS Blog statistics for June 2009

This was almost the best month for the web site, but March (below) was slightly better (February and April were in between March and June).

March 2009 Stats for Peltier Tech Web Site

PeltierTech website statistics for March 2009

June was the highest month for visits and visitors on the blog, but April had more pageviews.

April 2009 Stats for PTS Blog

PTS Blog statistics for April 2009

My stats show “typical” daily variation, with high weekday and low weekend, for visits, visitors, and pages, while the other measures are flat. John has the same daily variation in visits, visitors, and pages, while hispages per visit and time per visit are higher on weekends. I think this means that people spend more time on his site on weekends, when they have more time to poke around.

Chandoo’s stats in the second week of June are crazy, higher and less regular than the rest of the month, which may show a regular variation except for the distraction of the second week. The peak in Chandoo’s numbers probably corresponds to the June 12 announcement of the new Chart Busters feature he and I are working together. This announcement led to some controversy, as it at first used the name Chart Doctor, which is also the name of a  feature on Kelly O’Day’s Process Trends web site.

Peltier Tech Chart Utility

Comments

  1. It’s a shame the stats don’t show sparklines for more than a month, so we can see the trends properly. 24 hour and 7 day moving averages would be good; I stopped being interested in the daily and weekly cycles once they established their existence. Now I just want them to step into the background and allow the context of longer term changes to take the foreground.

  2. Okay, here’s a challenge for you. I nominate the information posted above for a chartdoctors consultation. Would be good to include the daily dose of excel and other blogs who posted as well. Game on?

  3. I’d love to see the % new visits for the Data Pig blog, given it’s just a month old. How does Google Analytics handle a divide by zero scerario?

  4. Not bad… Congrats all! I’m expecting to reach 150 000 monthly visits very soon too (around July 5, 2030)…

  5. You can readily change the time scale of the sparkline; this is my site for two months (1 May to 30 June):

    May and June 2009 Stats for Peltier Tech Web Site

    PeltierTech website statistics for May and June 2009

     
    You can show numbers weekly as well; this is my site weekly in 2009 (4 January to 27 June):

    Weekly 2009 Stats for Peltier Tech Web Site

    PeltierTech website weekly statistics for 2009

     
    And of course, you can do your own charts. These are for 1 January through 30 June, 1009.

    2009 Pageview Stats for Peltier Tech Web Site
    PeltierTech website pageview statistics for 2009

    2009 Pageview Stats with 7-Day Moving Average for Peltier Tech Web Site
    PeltierTech website pageview statistics with 7-day moving average for 2009

    2009 Pageview Stats with Loess Smoothing for Peltier Tech Web Site
    PeltierTech website pageview statistics with loess smoothing for 2009

  6. Cool. I hope you didn’t take my comment as a critisism of the way the information is presented above.

    Rather, I just thought it would be a good challenge to design a graph or series of graphs from first principles that shows how the different blogs relate to each other.

    Some small multiples, crossed with some dynamic graphs with selectable series…that kind of thing.

    I’d have a go, if there was some downloadable data from a few different blogs..

  7. Hey Jon.. pretty cool post this. I have always been skeptical to show or talk about stats. I think they measure reader engagement incorrectly. But they are very good for a host of other things like measuring bounce rates, understanding search engine, referral performances, how users navigate et al.

    I have uploaded the entire stats screenshot for June and for the last three months here.

    Pointy Haired Dilbert statistics for June 2009
    Pointy Haired Dilbert statistics for June 2009

    Pointy Haired Dilbert statistics for Q2 2009
    Pointy Haired Dilbert statistics for Q2 2009

    Note, June numbers have slightly gone up, may be because google analytics counted few more visits after taking the screencap. I was on 1st july in India, but few parts of the world must be still in June at that time… (or there is something funny with the way google measures these numbers)

    Also, one correction: the june 12 spike is due to mention in Lifehacker. While chartbuster announcement did have a local spike, the lifehacker mention alone fetched 25k new visits in a frenzy.

  8. Chandoo –

    The other thing Google Analytics is good for is analyzing traffic, and knowing for example that your June spike was due to the mention in Lifehacker. I hadn’t really thought the Chart Doctor controversy was enough to account for such a huge spike. What fraction of June’s traffic do you think came from the spike?

    I use GA to see variation in visits to specific pages, to see trends in traffic from different referring sites, to see how visitors move within my site. This is an academic exercise for me so far, since I haven’t really figured out how to use the information.

  9. Another Excel blog heard from:

    Daily Dose of Excel statistics for June 2009
    Daily Dose of Excel statistics for June 2009

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