There’s nothing like a dashboard full of data and graphs and trend lines to make us feel like grown ups.


You’re supposed to put these dashboards up on a wall, on a huge plasma screen. Because of course numbers are twice as persuasive if you make them twice as big.

Stijn DeBrouwere, Cargo cult analytics

Go read this post, it’s awesome.

h/t David Churbuck, who added:

The admonition that, “You can’t manage what you don’t measure,” has built a corporate culture more concerned with looking buttoned-up, on the ball, and obsessively accurate than being intuitive, empathetic and innovative.

I was the guy who built these dashboards, peered at them for magical insights, puked them at my bosses, and over time I started to get really cynical and put tired old quotes pissing on measurement into my PowerPoint presentations:

Einstein: “Not everything that can be counted counts, and not everything that counts can be counted.”

Warren Buffett:”They studied what was measurable, rather than what was meaningful” 

I know Debrouwere’s post appealed to me because he was specifically addressing metrics in the newsroom — a place I spent most of my career. But it also struck a current chord with me because of  my work for clients, all of whom cite Big Data incessantly as a force for disruption and transformation, yet haven’t the faintest clue of how to harness it or whom the Oracle will be in their organization who will study the digital tea leaves and come up with the single “AHA!” that will make them Measurement Legends.

Union Metrics For Tumblr

I want this very much.

Union Metrics for Tumblr provides detailed Tumblr analytics for brands and marketers, helping provide insight into engagement with Tumblr campaigns and conversations.

Union Metrics for Tumblr is Tumblr’s preferred analytics provider.

For the first time ever, get analytics on any Tumblr post or topic! Search for a blog, a keyword, a tag, even content source. We have full-fidelity access to the entire firehose of Tumblr data, so your analytics are built on the highest quality data.

Union Metrics for Tumblr reporting includes:

  • Post and note volume to show overall engagement levels and trends over time
  • Top contributors and curators to help identify key influencers
  • Analysis of posts and tags to surface most popular posts
  • Post engagement details, including the full reblog tree and interactions over time

Tumblr has more-or-less endorsed the tool as a ‘preferred analytics partner’: the first of these preferred partner rleationships.

Images do better than this unspirited prose:

Note that this third image represents a Twitter cascade from a specific Tumblr post. That data is accessed from Gnip’s Twitter firehose.

Twitter drives 4 times as much traffic as you think it does | awe.sm: the blog


Because we are moving from a web of pages to a web of flow because of the rise of liquid media, Twitter’s impact is beinf significantly underreported by referral-based analysis tools:

Jonathan via

Referrers are a poor way to attribute traffic from social sharing.

Referrer analysis is based on the outdated metaphor of the web as a network of links between static pages that could only be navigated by browsers. Today’s web is built around social streams and other APIs that are consumed via dynamic web applications, desktop clients, mobile apps, and even other web services, all of which render referrers obsolete as an attribution mechanism.

awe.sm was built for the modern web — a network of people, not pages — to track the results of Tweets, Likes, emails, and other sharing activities no matter what path they follow. So our system knows with certainty where each link was originally shared in addition to all the places where it was ultimately clicked (i.e. referrers). This approach gives us a unique set of data that demonstrates just how misleading referrer information can be.

And in the case of links shared on Twitter, it’s very misleading: the referral traffic one sees from Twitter.com is less than 25% of the traffic actually driven by Twitter.

Twitter is the perfect storm for referral traffic

We looked at awe.sm data from the first 6 months of 2011 spanning links to over 33,000 sites, and the numbers were astounding:

  • only 24.4% of clicks on links shared on Twitter had twitter.com in the referrer;
  • 62.6% of clicks on links shared on Twitter had no referrer information at all (i.e. they would show up as ‘Direct Traffic’ in Google Analytics);
  • and 13.0% of clicks on links shared on Twitter had another site as the referrer (e.g. facebook.com, linkedin.com).

We need better social plumbing for the social, liquid web. We need to measure the ripples spreading — a measure of flow through social networks — not the number of links in pages referencing other pages.

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