In preparing for a talk I gave at the Advertising Research Foundation's Audience Measurement 4.0 conference, I prepared a simple model for measuring the effectiveness of your Twitter activity. The topic of the presentation was how to value the pass-along of content.
Quantifying effectiveness requires a simple model using readily available data. Using bit.ly stats, you can get all the data you need. I've attached a spreadsheet to this post that contains our data and the model I used to generate the metrics.
For each Tweet that contains a link, use bit.ly to get the clicks on that link. For each day you've shared links in Twitter, grab the number of your followers. Dividing the clicks on the links you've shared by the number of followers on that day gives you the effective click-through rate (eCTR) for your account. Next, grab the total number of clicks on the same link you've shared (again, this is available through bit.ly). Divide the number of the clicks your tweet received by the total number of clicks and you've got your account's "share" of clicks.
Here are those formulas:
my clicks / my followers = eCTR
my clicks / total clicks = share of clicks
Using these two metrics can provide you with some really valuable insights into the effectiveness and power of your presence on Twitter.
You can also add a lot of power and utility to this model by grouping the Tweets into content categories (eg. tweets on travel, tweets on software, tweets on deals) and do the same analysis within and across specific categories. For example, if you are managing an account for Google (no affiliation), then you could perform this analysis to see what your share of Tweets is among links that have been shared on search or online advertising. What you'd hope to see is that your share is positively correlated with your market share, your share of ad spend, or your reach among sites in those verticals.
What do you think?
Wednesday, June 24, 2009
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2 comments:
This is an incredibly useful concept not often (if ever) effective utilized in the not-for-profit world.
Nice work!
Neat aggregation technique to determine popularity of a tweet across an audience. Great stuff!
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