Here's a presentation by Alan Mitchell which has been put on the IDM website. Its relevance to Cloud of Knowing is that the MRS paper I have now submitted concluded that CRM may be a more fertile place for content analysis to reside if research proves too finicky and regulatory.  After all all these territories feed into decision support and clients may be less worried about where their information comes from.  So I was intrigued about how CRM might be self policed by customers holding onto their own data and sharing and updating it. Permission and privacy being themes which need sorting out. Even if the regulatory cops tend to shout no and ask questions afterwards. I was amused that after posting about some of the issues that went into my paper last week on my blog I was sent a press release by the PR agency of the MRS.. about privacy and data protection and impending EC legislation. For my information..  And I didn't even have to ask!! 

First of all grovelling apologies not to have involved you all more regularly - we should have had the opportunity for at least one face to face meeting since the last despite the onset of Christmas. I have been rather pressured into providing outputs feeling I should have worked harder to get inputs from all of you. I had a great conversation with Surinder Siamat this afternoon which was really helpful - thank you Surinder.

The deadline for the Market Research Society Conference written paper is Monday (gasp). I will do my very best to circulate a draft of the paper end of play tomorrow to give you the chance to comment on structure and content. Which leaves me Monday to knock the document on the head.

You will have caught the drift if you've seen either the webinar I gave for the IE business school or the powerpoint (if you hadn't got an hour to listen to me burble through it) the links are in a powerpoint in the scriptorium.

I plan to set up a face to face meeting in February when we can talk about which ideas to feature in the conference presentation - which is only 15 minutes after all. And also to talk about one of the ideas which is Demographic replicators - search for the term and you can learn more about these as social media bots and their potential use in research. There's a blog dedicated to them.

So.. wait for the paper - your inputs please finishing line on Monday and a meeting in the offing. Speak soon.

Today I ran a webinar on my current thinking about Cloud of Knowing for the IE business school where I am a visiting professor. I teach the online research component for their digital marketing masters. The deck is I'm afraid too big to upload to the Cloud of Knowing site but I was able to put it up on slideshare. And there is also a link to the recording of my webinar. You can find both in the document called Cloud of Knowing Webinar Links in the Scriptorium. The deadline for doing the written MRS paper is due at the end of February so I am being forced to progress my thinking without the input I was hoping for. If you do have time to go through the deck and have questions, comments or suggestions then I would love to hear them. I have suggested 2 prototype hybrids of research and content one using the Purefold jetengine model and the other using Demographic Replicators.

Research has a strange hangup with sampling. The selection of the right people is kept separate from the content you want to get from them.Which is understanable but has become a limitation. Because it has conventionally been kept physically separate. In this post I want to suggest that sampling could become more dynamic. Which would move research a lot closer to the gathering of analytical content which has almost no sampling discipline applied to it at all. One of the reasons research is so different is because of this sampling discipline which is part of the bedrock.

Quantitative research surveys tend to collect the information about who you are at the end of the survey because its dull and being factual you may be too wearied from the survey to bother to embroider. It is very clear which are the classification questions to be used for weighting and cross tabulation. And which is the content which will be carved up.Qualitative research takes a lot of trouble to make sure you are the right person before you start the research at all. And in analysis much is made of the distinction between process data and content data. The bits of the research interview (or group) that are identified as being linked to the dynamics of the research process itself - these are discarded as contaminated. It could be the warm up for the group or something a respondent agrees with which is reckoned to be a part of the group dynamics than a sign that person B agrees with person A.

want to suggest that using two parallel tagging systems that we could make sampling dynamic and apply it to web content. Sample tags would be used to identify who is the author of the content. Content tags would be used to identify the content. Although a lot of data might carry both tags the same data item should not be used for sampling and content because that would be bootstrapping. This approach would have to be probablistic - because it is unlikely that we would ever have enough information from sources of internet content to identify them  (apart from asking them to complete a sample frame questionnaire of course) - but I''m trying to avoid this because it would turn content analytics into a different way of recruiting online respondents - great content - can you fill in this survey? Similar to scoring models used in direct mail which use detailed information about a sample to make intelligent guesses about the propensities of people living in similar types of houses or buying similar branded goods.

The sampling bottleneck which we need to get past is analogous to the Van Neuman bottleneck which was a major step forward in computing. Before Van Neuman computer designers used two different types of data - control/instruction data and content which the instructions worked on. . The old knitting machines programed with cards are a hangover from that period. Even though Alan Turing's original concept paper showed that a symbol processing machine could handle both in the same medium. What Van Neuman suggested was that data be stored in memory and be acted upon using a CPU which applied programming instructions using the same medium. No more knitting cards. And computers have been designed the same way every since. The instructions and the data have been in the same format stored in the memory and processed in the CPU. I am suggesting that sampling and content needs to go the same way. Drawn from the same source and handled as part of the same medium

This is a follow up to the topographical oceans chart which suggests that it would be an interesting experiment to set up candle charts (you can find these in Excel called stock charts. For keywords.to see what range they move across in terms of how often they are used on the internet. And how often they are used.  Would the volatility or kinetic potential of a word be worth collecting as a predictor of how well a word of mouth campaign was likely to be. Or are there other measures of word popularity we should also consider?

This is by way of slamming down a reference to Avinash Kaushik's blog so you can keep track of it. He is a guru of web analytics. What makes him interesting is that he is good enough to step away from clicks and to propose integrating a trinity of data sources - clickstream, business outcomes and surprise surprise customer research. I want you to take a look at his blog post on measuring social media which is notoriously easy to do and difficult to do well. He is working off piste but his lucidity is really helpful. What I got particular excited by was tweetpsych. A tool which aggregates a ton of tweets and then generates a psychological profile. Kaushik dismisses sentiment analysis pos/neg tweet classification as twaddle. He's looking for analytical tools which can pick up irony, emotion, making authoritative statements - content of that kind. Which tweetpsych appears to do. I commend it to you.

Sorry - I've come over all Roger Dean. I'll b dusting down my old vinyl Yes Albums in a minute. But its just that I suddenly had a thought that if we have all these measurement tools grinding away.

Then for every word in the English language we have a score extending back several years on how how that word has trended. Some words trend a lot more dramatically than others. But I would have thought that the vector that a word travels through is reasonably stable. There are levels it can go up and down to. But there are other levels it will never reach. This becomes a kind of magnetic resonance map of the language. And if we artificially reduced it to a 5000 word vocabulary - (simple option) or a volatiliy index (the most exciting 5000 words) then wouldn't we have  a brilliant way to predict the viability of social media campaigns because we would know the tolerance parameters of the words they were drawing on.

Its a bit like Fourrier analysis which basically deconstructs any curve into the component curves of which it is an aggregate. Which is how statistical modellers work out which factor is contributing which bit.  At present we are looking at campaigns as discreet events when really we should be building a map of the entire linguistic field. ocean thingummy. Does this make sense or am I raving?

Here's the headline on the Research Live site. Just to remind you that Google Analytics is a tool which allows you to monitor your own site or anybody else's for a fee. And to gather behavioural data which might allow you to identify web users who don't have the option to opt out.

Now the Germans are notoriously sensitive to data issues- they have the toughest direct mail regulations in Europe (opt in is standard for example). It matters because on the wild frontier of analytics, data doesn't care who finds it. Having to filter data by regulatory authority based on where the web user or website so we can give them an opt out. is a potential nightmare. And now that Google has announced that it will limit the number of daily free news items for the news sites in an effort to stop the newspapers opting out of Google altogether there are worrying signs that walls are starting to be built. The value of analytics largely consists of how easy it is to get hold of. Analysing it is where the hard work is. If getting hold of it becomes twice as difficult then a lot less clients are going to want to use it.

Just found Tweetcloud via Tim Wright known as moongolfer on twitter - thanks Tim. Its an application that turns your tweets into an idea cloud going back a day, week, quarter, 6 months, year. And at 3 levels of resolution. Very very simple. Now as long as you can set up a twitter account or a list then it would seem to me that this is a very easy way to be able to monitor the keywords that it generates. Or you could set up twitter accounts for people for a particular project and ask them to use it. Then use tweetcloud to do the first pass on the headlines. Is anybody aware of other tools like ideaclouds that could be used in this way. Apparently the developer has done the same thing for Facebook. The graphic is my first as @johngriffiths7covering a year at medium resolution - which I don't find particularly illuminating - but that probably demonstrates that eclectic posting strategies don't tell you very much!!

is a wrap - a select group met on Thursday 20th thanks to all who came. You can find my notes and the slides on the Cloud1 page