There are so many articles about audience metrics and so little time. Here is an overview of the most recent articles by innovative thinkers on audience metrics.
Frederic Filloux of the Monday Note has made the best proposal I have ever seen on measuring quality news. The basic idea is you can score content on different signals. In his project at Stanford, he uses these inputs.
Publication quality score (PQS) includes awards and newsroom staffing. An input of the publication is the authors quality score (AQS) with, again, awards and social footprint: activity on various platforms, number of followers, retweets, and so on. It also considers a person’s resumé on LinkedIn.
Another contributor to the PQS is the lifespan of a story and its staying power. Last but not least, you get more points if the story originated with you.
Interestingly enough, social propagation gets a low consideration weight in the model because it is more of a popularity indicator at any given moment than a long-lasting quality clue.
The engagement metric combines the most critical values in assessing quality: actual reading time and propensity of the reader to comment, annotate, or even e-mail the piece.
Public interest level must be evaluated by a human. And the level of data contained in a piece usually indicates the depth of research.
Best practices at Die Welt and Financial Times
German news organisation Die Welt has a tool that aggregates data from multiple sources and metrics such as engagement time and video views to score articles from 0 to 30.
I really like the engagement metric by FT described in this article. First, because it is company-wide. And second, because it focuses on loyalty and frequency, which is a great predicter of real loyalty. The model is called RFV, short for recency, frequency, and volume. It looks over the last 90 days to see how recently a reader visited FT, how many times, and how much he read over the period.
If audience development is your job, make sure to read this piece by the audience guru at the Guardian. He warns that data can also lead to horrible decisions and pointless delays if it isn’t used judiciously. Throwing data at any problem is seductive, especially in a world where big tech companies often succeed because of their data.