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    Nobody pays me to write any of the copy on my blog, and should I ever have the good fortune that they do, I will declare it. My main employment is as the owner and principal of The Future Place consultancy. The Future Place provides two key services 1) training and services to industry and academic bodies and 2) consultancy services to companies. The details of the companies I work with are a private matter, but if I blog about any company who has paid The Future Place more than expenses recently (approx. two years) I will mention that they are a client. I hold equity in Virtual Surveys and provide consulting services to them from time to time. I am paid to run courses for a number of trade bodies and over the last few years clients have included ESOMAR, AMSRS, MRS, and MRIA.

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Interesting Ray, although I'm not sure I entirely agree with your definitions. You can listen to online conversations in qualitative and quantitative ways, and I would say that rather the difference between active and passing listening is the type of information you can gather; second-hand data in the case of passive listening, and first-hand data in the case of creating research communities.
I certainly agree that there needs to be a balance between qualitative and quantitative research when listening online, but the degree of quantification of the qualitative, if you will, has always been a debate among anthropologists. There needs to be some form of coding in order to make sense of the data (whether we call it memes or key topics, etc) but then a qualitative analysis to make sense of it and enter deeper into detail.
So we can use competitor names or product names, for example, as search queries for a company that wants to know what's being said about these topics in order to detect the comments online and automation to count how many times each topic was mentioned, on which sites, when etc., and then human analysts to pull out key trends, how they've changed over time and why, etc.

PS I like the "battle for the soul" comment ;D

Michelle @Synthesio (Int'l web monitoring & research)


Hi Ray

It occurred to me that some (most?) of the people leading social media monitoring/research agencies are not researchers. Radian 6 CEO has a background in tech start ups. Companies such as WaveMetrix and Visible Technologies have CEO's with a finance/tech background. So, I wouldn't say it was only quant researchers that were slow to grasp the value of online conversations - it was all researchers!

Re: quant vs qual, I agree with Teresa that to get the most from online conversations you need a marriage of the two. The quant element gives you an overview of the topics and sentiment of conversation, which allows you to focus on what's most important for further analysis. So, for (a simple) example, if Nokia analyse the buzz on their latest phone and 25% are talking about video and it's strongly negative (the quant bit), that's likely to be the issue to focus on. Delving into the specific conversations around video quality (the qual bit) should reveal exactly what it is about the video quality that is a problem. Of course, this assumes enough buzz to create a quant overview. If there isn't enough then you proceed directly to the qual bit.

Btw, to produce reliable quant and qual buzz research data you need human analysts at both stages i.e. to code the data (quant) and to delve deeper (qual) . Automation isn't anywhere near good enough (yet).


Jon Beaumont
Virtual Surveys


Maybe I'm being idealistic, but I'd certainly hope that researchers, quantitative and qualitative, would see the value of a balance between the two. One can't really stand without the other, and that's especially apparent in the social media space. While everyone seeks the holy ROI grail, that ROI is still impacted by qualitative information. I can't see how there's ever been a true delineation between these two types of research, and I hope that kind of extremism doesn't continue for long in social media.

I'd like to clarify that Radian6 isn't all about automation. While our platform aggregates and visualizes information from the social web, we will advocate to the ends of the earth the importance of a human eye in making the most sense of that data. Only those in the trenches of an organization can truly understand what data matters and why; it's up to them to make the final analyses. We truly believe in the value of our platform, but in this day and age, technology can't replace the analytical mind of a competent human being.

Thought-provoking post, Mr. Poynter. Thank you. :)



Teresa Basich
Community Manager, Radian6

Ray Poynter

Ah Annie, but that goes to the ontological heart of the issue. Those with a constructionist point of view would look at terms like 'validity' and 'reliability' and dismiss them as the illusions of the positivist descendants of the Vienna Circle.

One of the reasons, IMHO, that online qual did not take off ten years ago (amongst most qualitative researchers) was the nature of the narrative. If you look back at the old papers which conducted side-by-side studies into online and offline, they often talked about the quantity of the verbatims, the numbers of words, the accuracy and speed of the transcripts. These 'fact' bases arguments are all artefacts with great significance to the positivists, but with little resonance to those with a constructionist epistemology.

If qual and quant are to meet and understand each other, then each will need to understand the framework of the other's thinking better. Telling an artist that a bigger brush will let him paint faster will not convince the artist. Similarly, telling Ford that hand painted cars would be more personal is not likely to turn the business around.


Regardless of who got there first, because it wasn't quant researchers but rather IT professionals who got there first, there is room for every method and style of research as long as validity and reliability are at the root. Some people feel more comfortable with quant than qual, and vice versa. Others feel more comfortable with automated than manual, and vice versa. In the end, the method we choose to address a problem will be the one that is best suited for the job and best suited to the user. When we approach problems from all angles, we'll get the best dang results ever imagined.

So, Conversition is approaching this problem from the quant side. Can't wait to meet the qual folks in the middle!

Annie Pettit, Quantitative

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