Greg Linch on “quantifying impact”

Currently, works of journalism (articles, videos, galleries, graphics, etc.) no matter what subject (news, sports, entertainment, business, features, investigations, etc.) are quantitatively measured the same. An investigative piece that might be nowhere near as popular in pageviews across a mass audience (yes, sometimes, they can be) is quantitatively measured the same way a celebrity death story is. Either story could make a sensational splash, truly connect emotionally with readers, or both. Each has value, but there are different kinds of values across different subjects journalists cover.

If we value impactful accountability journalism, why are we quantitatively equating it one-to-one to entertainingly impactful news? For example, when an investigation is published that saves taxpayer money or even human lives, we should instead try to measure these in a more multi-dimensional way — instead of merely the simplistic ones — and measure them differently from journalism works that have different goals. We should do this not just because the quantification would be more accurate (again, still imperfect), but because it would be a better model of the complex real-world response.

Greg Linch — Quantifying impact: A better metric for measuring journalism.

Hello Publishers, Meet Dash

Specifically, editors at separate organizations asked us the same question: Can you share some of that data with us? You know, the topic data and the data on authors?

Begrudgingly, we agreed, and started to send out reports on a monthly basis.

Editors: “Hmm, this is great! Can we get this quicker?”

Parse.ly: “Uh, sure. We can give it to you weekly.”

Editors: “Awesome! Actually, it’d be great if we could get this daily.”

Parse.ly: “OK, what’s up here? Why do you care more about the data than the recommendations?”

Well, as it turns out, nobody had really showed them this data before, and the data was simply eye-opening for the editorial team. They were using it to go beyond monitoring individual articles to understanding what was resonating with their audience.

Queue the second Aha! moment in early 2011. We took a step back and did some research on analytics tools for online publishers. What we found was astounding. Almost no innovation had happened on the analytics side for online publishers. Most tools were one-size-fits-all systems that treated an e-commerce site the same as a content site, and obviously, that’s not the way to do it.

Content-based sites are dramatically different than an e-commerce property from both a data and business perspective.

It’s no wonder these publishers were clamoring for data that provided fresh insights on their property. Publishers need to know how their content breaks out by topic, what causes a post to go viral, why one author does better with search traffic than another, and a bevy of other key insights that are specific to their needs. We knew this was a big opportunity, and decided to dive head-first into the analytics space.

Sachin Kamdar — Hello Publishers, Meet Dash

New York Times releases code to help journalists collaborate on WordPress, other platforms

New York Times releases code to help journalists collaborate on WordPress, other platforms. Track changes within the WordPress editor. Code is available on Github; it would be awesome to see this support realtime collaborative editing too. (via Steve Myers)

How to manage a proper multi-author WordPress blog

How to manage a proper multi-author WordPress blog. Latest version of Edit Flow makes the list of recommended tools. Interestingly, at the top of the list is a team blog, P2 in fact, for authors and editors to discuss ideas, share links, etc. Now, if only that were embedded within the admin too…

Today’s two WordPress.com VIP launches: PandoDaily and Grist

Today, MLK day even, two new sites launched on WordPress.com VIP that I’m personally pretty excited about.

PandoDaily

PandoDaily is a brand new tech site started by Sarah Lacy, former senior editor at TechCrunch. From her announcement post:

We have one goal here at PandoDaily: To be the site-of-record for that startup root-system and everything that springs up from it, cycle-after-cycle. That sounds simple but it’ll be incredibly hard to pull off. It’s not something we accomplish on day one or even day 300. It’s something we accomplish by waking up every single day and writing the best stuff we can, and continually adding like-minded staffers who have the passion, drive and talent to do the same.

So… this sounds like a newer, better, and fresher TechCrunch starting from scratch. And she’s recruited Michael ArringtonMG SieglerPaul Carr and Farhad Manjoo as regular contributors. Props to Sara Cannon for pulling off the design.

Grist

Grist, a non-profit environmental news publication, is near and dear to my heart. It’s why I’m on the technology side of publishing instead of photographing in the third world. In summer 2007, I worked an awesome web production internship where, in exchange for a bit of copy and pasting into the CMS, I had the freedom to explore publishing on the web and to start developing my skills. That was back in the days of Bricolage; Grist has since been on ExpressionEngine. Props to Matt Perry and Nathan Letsinger for making the switch happen (and to the Otto and Nacin show for their support).

Want to help publishers kick ass with WordPress? Come join my team — we’re hiring.

Questions publishers want answered

Short list of questions publishers want answered that I believe could be answered with the right data:

  • Who are my best writers?
  • What topics are my audience most engaged in?
  • Which types of pieces do best over time?
  • What type of stories should I have my writers work on?
  • When is the best time to publish?
  • What’s the best length for a piece?
  • Does including rich media help with engagement?
  • Do my writers actually need to include links? How many?

What am I missing?

Obviously most publishers know most of these by heart, it’s key to running a successful business. What’s more interesting is to use this type of data as a baseline for experimentation.

It’s important to remember the difference between creation and optimization, and how data can be used for each.