In many ways, media companies are in need of just a metric: one that effortlessly communicates value and drives decision-making. However, at the present moment, online metrics are too focused on decontextualized outcomes. But by incorporating the influence of promotion on an article’s performance, we can create a set of baselines that would enable more meaningful comparisons across a wide range of content. We might call such a metric ‘Pageviews above replacement’ or PAR for short, as it would allow us to determine how well a certain article performs in comparison to a similar article that received the same level of promotion.
In an attempt to build a prototype of PAR, I collected as much data as I could on the promotional activities of the New York Times. Every 10 minutes or so, I pulled in the posts from 20 Times Facebook accounts, 200 Twitter accounts, and the contents of the homepage and ~ 25 sections fronts. At the same time, I also collected metadata on articles and information on their performance. By cross-referencing these two sources by URL and time, I was able to construct a detailed database of 21,000 articles published on nytimes.com between July and August.
Early results from Abelson’s analysis show that it is possible to predict with a good degree of accuracy how many pageviews a given NYT article will get.
Felix Salmon at Reuters did a further analysis of Abelson’s work, looking specifically at the impact of the disproportionate social sharing of wire services content in Abelson’s database. While 73% of the sampled stories came from wire services, Salmon reports those stories had only a 0.6% chance of being tweeted by @nytimes, resulting in viewer pageviews.