Content analysis can be a very simple affair, or quite complex, according to a client’s needs.
It might involve nothing more than counting news pieces or tallying mentions of certain brands to see who has the greatest raw presence in a media space. Or it can describe the complex relationships between the appearance of certain message sets against outcomes like sales activity, share-price fluctuations, improvement in reputation, or regulatory shifts.
At its simplest, all you need to carry out content analysis is a spreadsheet and the ability to count! But as things get a little more complex, the need grows for quite powerful data systems, well qualified and trained people, and rigorous methods and procedures.
However no matter the level of complexity, content analysis must be meaningful to the client organization concerned. It must be auditable, and it must be transparent in terms of the methods used to arrive at the results. Of course, it must also be affordable … but that’s pretty much a given these days.
With all the changes that are occurring in the media space generally (the advent of social media channels being perhaps the most talked-about) it’s not surprising that there is also a shift occurring in regard to the methods that a number of analysts are applying to their work. But this shift is also being driven by evolving client requirements.
Increasingly, savvy clients are demanding broader, more valuable, more insightful research, that’s capable of informing good decision-making in uncertain times. They’re no longer impressed by the questionable dollar-value calculations, or the arcane proprietary performance scoring systems based on "secret formulas", that have been passed off as quality research by some practitioners in the past.
These clients want to understand the complexities of their media landscapes in plain terms. They want research that can inform strategic thinking, good planning, and effective tactical action, and they want to measure their successes in terms of real business outcomes.
The need to change and improve has seen some analysis houses move away from the decades-old, simplistic practice of content “coding” – which has long been considered the only way to accomplish “real” content analysis. However the strong attachment to traditional coding methods, developed back in the 1920s and still taught in many universities around the world today, has blinded many in the analysis industry to the very substantial limitations that these old methods impose on the quality, veracity, flexibility, and cost-effectiveness of their research.
To understand both the strengths and limitations of coding, and of some of the alternative analysis methods in operation today, it’s necessary to understand the mechanics of each method.
And that's coming up next.