The Last Word On The Podshow / Pickle Alexa Traffic Question
Posted on 4:25 pm by Paul Colligan
This Blog Post re Podcast Pickle got a lot of attention - and not for the reasons I thought it would. I simply was writing a piece about how Podcast Pickle has caught my attention (by among other things, passing Podshow in Alexa Traffic the day I posted the note).
It was picked up at the Pickle (yeah, sure, surprise!), Podcast Brothers, School of Podcasting, and even (Podshow’s) Curry made a comment or two.
And then the numbers appeared to “change” (read Curry’s post). I pointed out my “mistake” - as did plenty of others.
I’m big enough to comment when a mistake has been made.
Funny thing is - it wasn’t my mistake at all. Turns out Alexa “smooths” the chart points after a day or so to get you a better idea of trends. If you look at the screenshot above, you’ll notice that the “Smoothing” is set to “0″ and low and behold, Podcast Pickle did pass Podshow (in Alexa traffic only) twice last month.
So, my reporting was good - and Alexa didn’t burp.
Now, as per Curry’s comments - traffic isn’t their model. And as noted here, they did 52 million “download requests” in December of 2006. That’s a very impressive number set and … he is right, the comparison between Podshow and Pickle in this arena is about as valid as comparing Pickle to 1PlaceForEverything.com.
For those (interested and still) following, the chart below maintains the last month of stats:
Technorati Tags: podshow.com, podcastpickle.com, alexa, podcast downloads




4 Comments »
March 6, 2007
Getting Pickled | Paul Colligan’s Profitable Podcasting (Pingback)
[…] Getting PickledPosted on 4:15 pm by Paul Colligan UPDATE: Turns out I was right. Details here. […]
March 7, 2007
Nation said:
Alexa = Crap
That is all.
March 7, 2007
Paul Colligan said:
What logical discourse! You’ve convinced me nation.
Paul
March 7, 2007
Erik said:
We’re not impressed by downloads as metrics. I never listen to half the stuff my RSS reader downloads for me.
At Foneshow we track:
The number of listeners for each individual second of programming.
Where, and for how long, listeners pause.
Where they scan forward with high speed play.
Where they skip back 5 seconds.
If they listen mulltiple times.
Where they forward a show to their friends.
We wrap all of that with demographics.