Story Analytics
We have all worked with web analytics tools at one time or another to understand how well the site is performing. Google Analytics, Omniture, WebTrends – all of them are great tools – but we are not convinced we use them as well as we could.
The media properties that we have been associated with tend to look at total site traffic in aggregate over long periods or, if they ever get into the details, they tend to look at the site section by section. Often, they will even look at the performance of an individual article. In fact, it is at the most granular level that analytics become the most helpful and drive the most insight. Sadly, we have rarely seen much insight drawn from these numbers.
We propose that media companies need to turn web analytics on its ear. They need to take a bit of a different cut on the data to make it really meaningful. Also, no one tool can currently deliver the type of data that good curators will need to manage their portfolio of topics. We haven’t seen a perfect combination of tools but, looking across sources, here is what we should measure:
- leading indicators of interest in a topic: even before the first article gets written, use data to help identify what we should be writing about. Demand Media, arguably THE biggest media success story of the last few years, bases their business on this practice.
- story type and investment profile: patterns in data related to a story or topic can suggest a certain trajectory or story type; e.g. evergreen with growth potential vs explosive but short-lived types. Different story types deserve different levels of investment in time and resources and, likely, create different monetization opportunities.
- performance of story elements and multi-variate tests: determine what story elements work the best; this will likely differ by the type of story. Use A/B or multi-variate testing if you have access to the required tools. This will tell you if you are assembling your stories in a compelling and usable way.
- depth of visit/engagement in the topic: at a glance, measure how many pages deep your users go in a specific story. Sure, topics can be linked and the lines between stories can be blurred, but do your best as this is a critical measure. While depth of visit on the overall site is an important traditional measure, engagement in a single story more directly correlates to the performance of contextual ad units – which will become a critical measure in its own right.
- contextual ad unit performance: while not all ads can be measured this way, those with a clear call to action and a conversion objective ought to be measured within each individual story to see if the rich context of a well-curated story makes a difference. This is a critical selling point of story curation. For this to be meaningful, the story topic must have some contextual link to the ad itself, i.e. the ad should relate to the story. We expect that some types of stories will perform better than others, but, overall, good curation should yield the kind of ad unit performance numbers that you will want to take to media buyers and advertisers.
These new categories of measurement, plus all of the other traditional analytics data, are crucial to effective curation. In fact, it goes beyond just helping tune the story, it also helps prove the model. Supportive data across these areas of measurement is ultimately critical to proving the benefits of story curation – including the monetization of properly curated content.
As far as publishing goes, gone are the days of post it, forget it, move on.
Measurement, Monetization, Technologies, Topic Exploration
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Google, Google Analytics, Omniture, Web analytics
Themocracy
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