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As newspapers and other media outlets struggle to develop new online business models, understanding the behavior of different readership segments is critical to success.

Multiple custom variables, then, may be the one new feature everyone from Web producers to publishers is going to want to learn more about. In brief, variables can now be assigned to visitors at three different levels: individual visitor, session or page.

At the visitor level, a tag such as “registered visitor” may be applied to track the behavior of all of your readers in that cohort. At the session level, a tag such as “commenter” could be applied to any visitor who interacts with your commenting system. At the page level, a tag of “sports” could be assigned to any visitor who visits your sports section.

The system will allow up to 50,000 different variables to be defined, each of which can be specified via JavaScript code embedded in your page templates.

via Poynter

While no where near as granular as Newstogram, the new multiple custom variable aspect of Google Analytics seems interesting (although also sounds like a lot of set-up work).

Eric T. Peterson of Web Analytics Demystified released a white paper today foreshadowing a ‘coming revolution in web analytics’.

Peterson believes we are on the verge of seeing “third-generation digital analytics tools” that will provide greater insights and opportunities by bridging the gap between offline data (such as market research) and online data.

This white paper describes the impending revolution in digital analytics, one that has the potential to change both the web analytics and business intelligence fields forever.  We make the case for a new approach towards customer intelligence that leverages all available data, not just that data which is most convenient given the available tools.  We make this case not because we believe there is anything wrong with today’s tools when used appropriately, but because we believe digital analytics should take a greater role in business decision making in the future.

Peterson also believes that first- and second-generation digital analytics tools have failed to live up to expectations in part because companies thought that the tools themselves would lead to meaningful and actionable insights, and as a consequence failed to invest in the resources (aka analysts) that actually create insights out of the data collected by the tools. This is consistent with web analytics evangelist Avinash Kaushik whose 10 / 90 Rule advocates spending 9 times more on resources to extract value from web analytics data than on the tools that collect the data.

This raises the question of whether paying for web analytics tools that collect web data will be a casualty of Peterson’s ‘revolution’. Companies who pay for their web analytics solutions are already the minority (this is true even for large enterprises: study finds only 33% of large enterprises pay for web analytics technologies), and of those who do pay for web analytics tools, most are considering displacing them with a free alternative. A primary motivation? Freeing up resources so they can invest more in the people necessary to drive insight rather than the technology used to collect and analyze data.

Seems like this ‘coming revolution’ will be good news for people with the skills to turn data into insights, and bad news for companies who have built their businesses on charging for collecting data.