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Sometimes things seems so obvious that we tend not to act upon it. One of the reasons are that we think that if something seems that simple someone else would already have done it. Another reason is that most people are afraid to look silly by beeing naíve dilettantes in several fields of business and study in order to solve a problem in their own field. By doing what I´ve done for two years I might end up both run over by some one else and looking really silly in front of a lot of people. Or maybe that won´t be the case. And I´ve read somewhere that doing something for fun is actually getting more accepted nowadays! ;-)

The PRfekt idea – help media and marketers understand audience motivation
In short: while working as a media analyst I thought that it would be better to let computers do the coding. What you lose in accuracy, you gain in the amount of data possible to analyze. And I thought that the rise of the Internet and consumer generated media would finally put an end to the folly that is concentrating the measurement on the channel rather than on the audience when trying to measure and improve the effectiveness of mediated communication.

There are two main reasons for the longevity of this wrong focus and they are all based on methodological problems related to the traditional media landscape. When the media landscape changed the methodologies got stuck because they made money. And will continue to do so for a while.

The PRfekt summary of the media analysis business
The first methodological problem is data gathering, the second is content analysis and the third is audience research. In the old media landscape you had to send truck loads of dead trees (data gathering) to the location where people could read them (content analysis) and you had to ask people if they had read the content and what they thought about it (audience research), often by sending them questionnaires. Expensive, many things that can go wrong and dependent on that people bother to do cumbersome tasks (manual text analysis and answering questionnaires for instance).

In the new media landscape the media content is digital. That makes data gathering a lot easier, the content analysis possible to address with computers and, by chance, and thanks to blogs, the audience reactions to media content possible to research non-intrusively and by the same general computer-aided methodology.

The missing link is the segmentation model. In the old media landscape demographics ruled the business. In the new media landscape psychographics will rule the business. So there is a need for new ways of segmenting media audiences.

The PRfekt approach to psychographic segmentation

The PRfekt approach to audience segmentation is to determine Myers-Briggs personality type of different personas of bloggers when they link to certain media content. By building a large database of segmented blogs and links to media content, general observations can be made of what media different psychographic segments are interested in. That way you get the basis for the next step – analyzing what goes on in the heads of different audience segments.

It´s cumbersome to gues what it means for an advertiser that the main audience of, e.g. a section in an online news paper are driven by a will for power. Power can mean so many things and you have to be something of a little philospher to think about, something that few people in the busy marketing business have the time to. Planners are a useful bunch of people, but expensive and require expensive research data to be able to perform their best.

the PRfekt approach to description of psychographic segments
So, the PRfekt solution would be to track in real-time what brands, online channels, people, concepts and ideas that the different psychographic segments are interested in. That way you can skip the more theoretical stuff – kind of like Google does when they let peoples actual clicking behaviour determine what rank a page in a result list should have the next time some else is searching for the same thing.

The PRfekt methodological approach
However, I strongly believe that the best solutions to any problem is a combination of the extremes when possible. Mixing qualitative with quantitative is more effective than mixing different quantitative or qualitative methodologies, I believe. The funny thing is that most people tend to be in favour of one, but not the other. For forseeable type-related reasons, by the way. Thinker tend to prefer quantitative and feelers tend to prefer qualitative studies according to DiTiberio and Jensen in Personality and the teaching of composition. Usually that fact ends up in a lot of foolish war of positions instead of frutiful inter-disciplinary thinking and acting. Beeing the dilettant of dllettants I do not fear threading that path, though. Beeing neither a technician nor a psychologist my dream is to merge AI with type theory for the purpose of aiding my actual field of studies, work and interest; media and communication research.

I´ve always liked quantitative content analysis (the name of the game in media analysis for almost a century) for it´s merging of quantitative and qualitative. When constructing a compiter system I also like the merging of the two when combining subjectively built up training data (using the power of the human brain for interpretation of textual meaning) and naíve bayesian classification (using the power of the computer for analysis of textual content).

White paper coming up – and could use some wisdom of the crowds

So that´s how it´s done, mainly. I´m writing on a white paper with some academic name-dropping aswell for all the rest of you geeks out there. If you would like to help me by some early phase peer-reviewing (I´ve never written a paper before) – send me an e-mail and I would be happy to hear how I can improve it so it´s good enough for some serious peer-reviewing when the time comes.