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In the last post on podcast metrics, I talked about measuring the number of listeners who are subscribed to your feed. In this installment of the Podcasting Metrics Series, I’d like to address an issue that you might have wondered about when looking at your subscriber stats.

If you look at a graph of feed subscribers mapped out by each day, you’ll notice that it usually resembles a rollercoaster–dipping lowest over the weekend. Don’t worry. This doesn’t mean that your joke at the end of your last episode bombed and listeners promptly unsubscribed. This up and down movement is a natural phenomenon. Why is this?

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So far we’ve discussed how to measure the size of your audience by tracking downloads and bandwidth.

Another important podcasting metric to track is the number of subscribers that you have. Even though subscribers only represent a portion of your total listenership, it’s worthwhile knowing how many of your listeners have taken the step to automatically receive new content from you. It can be argued that this is typically a more engaged listener.

So how do we identify a subscriber and distinguish him/her from other listeners? A subscriber is someone who has entered your feed address into a podcatcher. A subscriber can be identified as a unique user (probably identified by unique IP address) who is checking your feed on a regular basis.

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A little while back MasterMaq of the Podcast Spot podcast host added some interesting and relevant points about my podcasting metrics series. Specifically, he disagreed with the idea that podcasters should not have to provide complete download stats because this held us to a higher standard than newspaper and other such media. He states:

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To get a summary of the podcasting metrics series thus far, check out this guest article I wrote for the Web Anlaytics World blog. I’m working on part 6 which will be on measuring subscriber stats. I’m sending some questions to FeedBurner and will post when I hear back from them.

In Part 4 of the metrics series I talked about how you can measure complete downloads and whether it was necessary. Moving on to Part 5, I’ll take a look at how to keep your site from being shutdown by your host due to excessive bandwidth use and how to measure complete downloads using bandwidth.

Measuring Bandwidth to Avoid Hosting Problems

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Before I move on to the discussion on bandwidth, I want to add a couple more things to the topic of complete downloads that I started in Part 4. 

Scott Bourne and Tim Bourquin
provided interesting and relevant responses. They both emphasize that podcasters
have a responsibility not to let advertisers hold podcasting to a
higher standard than other media (i.e. magazines and newspaper) that can’t measure complete content/ad consumption. If they don’t have those numbers, then why should you be required to. In other words, using complete downloads to represent the "reach" of your podcast undersells the value you offer.

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In part 3 of this series on metrics, I stated that measuring unique downloads, while still an imperfect podcast metric, was one of the most popular ways to measure a podcast’s audience size. While filtering your web logs by unique IP cuts out multiple hits from one user, you still don’t know if that user received all or just part of the file. Measuring downloads that are not only unique but also complete results in a more conservative metric than measuring just unique downloads.

This raises two questions:

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In Podcasting Metrics Series Part 2, I pointed out the problem with looking at the # of hits to a file for estimating the # of downloads. A single download can result in many hits. The hits stat will always be bigger than the actual number of downloads.

So how can we get a more accurate idea of the number of downloads? Services that offer podcast metrics (e.g. Libsyn, Podpress, Feedburner, Podtrac, RadioTail Ripple etc.) track things such as the IP address of each request to a file to determine how many unique users have downloaded (or at least attempted to download) the podcast file. This leads to a more accurate, although still imperfect, measurement.

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How do you measure the size of your audience? Is it the # of people who download your episode? Is it the # of people who consume each episode? Is it the number of feed subscribers? What about the people who download the file directly from your site?

Can you see where we run into problems with the semantics? We’re going to take a look at some words and stats used to measure the size of a podcast’s audience and what each term really means. In this post I’m going to discuss the "hit" myth.

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Recent conversations that I’ve observed or participated in on- and offline have given me a desire to write about podcasting metrics. I’ve decided to create a multi-part series on the subject. I’m not sure how many parts it will consist of, but it will continue over the next couple weeks and be interspersed with posts on other topics.

First let me share a couple reasons why this has been on my mind…

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