Generative Media Networks: Fueling growth through action: Practical examples

Any good news organization knows how to use its brand and the media vehicles it owns and operates to both inform and influence.  We’ve seen this up close on several occasions at the Guardian. #

“Twitter’s detractors are used to sneering that nothing of value can be said in 140 characters. My 104 characters did just fine. #

The combination of our work and what other people do creates a very powerful bi-directional relationship. #

  1. Things that we make.
  2. Things that people use.
  3. Ideas that people share.
  4. Ideas that we evaluate.
Here are some things that we are doing now at the Guardian in each area. #

  • We make things quickly and cheaply
  • The things we make perform well, have acceptable errors
  • We make interesting, creative, groundbreaking things
  • Our work is of a high standard, considered better than most competitors
  • The amount of what we produce is sufficient for demand
We can also measure our success in terms of the work our partners are doing when we co-create. #

“If content is king, then this is service is a hundred of the king’s best horses, and thousands of his best messengers, sending the Guardian far and wide.” #

We’ve also seen some incredible work by people using the data we’re publishing as part of the news cycle in the Data Store.  There are hundreds of people posting images of ways they are using that data on a group on Flickr. #

  • People buy our paid-for products, and we make a profit on those products
  • People see our free products, and we receive a high value subsidy for that
  • People dive into our products and spend time with them
  • Partners are using our stuff, and they are making money as a result
  • Our partners offer successful paid and free things by using our stuff
  • People buy access to our people, processes, platforms, partners
  • Our market share in all the things we offer is strong
SHARE
It used to be that once our work made it into our customers’ hands we had very little idea what happened to it. #

  • People both implicitly and explicitly indicate interests in things
  • They spread our work through their social nets.  Their social actions result in more actions from those connections.
  • They participate in conversations we trigger and add to them with their ideas, both within and away from our products.
  • They actively contribute by giving or selling us material to evaluate and then make things
  • Things change in the world as a result of our work and the impact of our readers, users, and partners acting on it
EVALUATE
Research and investigation got much easier as the Internet increased the speed, access and volume of information available to all.  Among the many things it did, the web made it easier to locate details and contacts. #

  • We see important trends early, generally before the competition
  • People with important information share it with us directly
  • We are good at verifying information, recognizing it’s value and knowing what to do with it
  • We are honest and fair in our assessments, and the market validates that view
  • We are accurate and truthful by most objective standards
Measuring success with metrics #

  • Time to develop, number of people involved
  • Real cost of development
  • Ease-of-use, performance and errors
  • Aesthetic appeal
  • Strengths against competition
  • Weaknesses against competition
USE #

  • Number or amount of things produced
  • Number of people using each thing
  • Number of repeat uses
  • Amount of time spent
  • Breadth and depth of usage
  • Supply vs capacity ratio
  • Number of things purchased
  • Amount received from buyers
  • End-user response to promotions
  • End-user conversion rate on promotions
  • Amount received from advertising promotions
  • Number of partners using our stuff in their stuff
  • Revenue partners are earning from using our stuff
  • Amount partners are spending to use our stuff
  • Market share: end-users
  • Market share: partners
SHARE #

  • Implicit interests collected from end-users
  • Explicit interests collected from end-users
  • Number of shares (tweets, RT’s, likes, mailto’s, etc.)
  • Number of referral URLs posted
  • Number of clicks from shares and referrals
  • Number of comments within our stuff
  • Number of comments elsewhere as a result of our stuff
  • Quality of insights from comments
EVALUATE #

  • Number of articles/posts/pictures/video pitched to us
  • Cost of acquiring articles/posts/pictures/video, etc.
  • Amount of information intake
  • Cost of data analysis on external inputs
  • Success rate in surfacing strong signals in the data
  • Low failure rate: verifying information
  • Low failure rate: accuracy
When tracking performance indicators across all these areas, it becomes very easy to then understand what is going well and what isn’t.  Different metrics have different values in different contexts, but one could roll everything up into a framework that helps with decision-making. #


This series is an attempt to assemble some ideas I’ve been exploring for a while.  Most of it is new, and some of it is from previous blog posts and recent-ish presentations. I’ve split the document up into a series of posts on the blog here, but it can also be downloaded in full as a PDF or viewed as a sort of ebook via Scribd: #

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