Your next firm should be an IT firm.

I’ve interviewed with them all. The big firms, the small firms. Both in Brussels and in New York.

After working in the European Commission and Parliament, I wanted to go to the private side of Communications and Policy. The problem? I had been running data and insights. Using terms like NLPSentiment analysis, cognitive science and connotation mapping to describe what I did not work well. On the other hand dumbing down would leave me looking like another 27 year old who does “the social media” and or “the internets”. Most firms doing the interview for analytics positions wanted to hear “pivot table”, “engagement”, “influence” and maybe SPSS. Upping the hierarchy on those terms to explain why “something was” appeared unnecessary and impractical since explaining this to a clients communications director is another task with in itself.

So to hell with the PA/PR firms, I joined an IT firm that also does communications – Intrasoft International, and could not be happier.  The people’s skill sets are well defined and paid, which gives them a certain confidence in contrast. They like things such as analytics and completely understand them. Their only fault is the soft side of Communications – which is now my job to merge.

IT is going to take over communications sooner than later. Current  PR firms will be left scrambling. The lack of investment in the deeper meaning (abstract knowledge such as transference, retention and pragmatics) will start to show as data/connotation mining becomes a standard practice. Most IT firms already have this infrastructure in place via their AI departments.

There will be a point where dropping shit jargon is irrelevant and companies Comm Director, who should be more of a CIO/CTO in the nearer future, will see right though it.

My 2 cents. Also check out my presentation at chandlerthomas.com

CT

Framing

In politics controlling language is vital. George Lakeoff (Linguist -Cal Berkley) confirms brain will generate a physical cognitive bias if you win the language war. Because of the frame being repeated, the attitude towards the subject changes.

For example the party who introduced the term wins solely on sheer mass. This can be determined by the total count of the terms.

For example: ”Death Tax” or “Estate Tax”. We also know the terms that we introduced to the discussion. Hence the campaign works when the Language has been adopted. It might not seem like much but these are long term goals and or policy matters to keep or change a status quo.

Response to Conversation Impact

CI can be found here 

In looking at Conversation Impact I think – it’s simple and clear which is important for creating a KPI. More importantly CI can be applied

easily across numerous campaigns. I think the main thing that I would add to CI model is accounting for the message framing and looking at mediums as separate instruments. Much of my political work involves this since it can shape perception and offer insight to what type of conversation is going to have the best ROI per medium and influencer. Each tends to have a different pattern that can make it possible to measure CI more accurately. In short there’s a lot of information in each medium interaction and this needs to be clearly separated.

For Figure 1:

The survey instrument – do  surveys correlate to an accurate representation of sales and behavioural change? I’m always sceptical of asking for an opinion i.e. “would you now do x?” or “purchase intent”.

To gain insight I’ll look at online and offline (attending a conference or event) actions. The more complex the task the constituent is willing to go through the more important I’ll view the issue/policy (in a political context). Also If someone posts a comment – I value this more than a “like” or “Vote” or mentions put forth by media as it shows more effort. The question for us all is if this translates into a vote or purchase.

I tend not to rely on automated sentiment as much – in political post (generally negative) it makes it very hard. To counter this specifically in Radian 6 I’ll Boolean for negative terms. As long as it is consistent across everything (like in CI) it can none the less be a good measuring stick and I have done this before.

Figure 2

The graph which illustrates Influencers led to a 6.3:1 rise in network influence. The study that I read was about a 5:1 ratio – so in your case it was a bit higher. Was this sustainable past March 09? Also was the campaign coordinated on traditional mediums and perhaps cross referenced with something like Cision Point which accounts for traditional news? The data from this would be interesting. On and offline merging is what I tend to measure/compare the most.

Also in my work I tend to see exponential gains in sentiment/mentioned but it’s usually built off an off-line instance like a political speech/conference or elections (shown in my Political Intelligence presentation pg 20).
If they were measured what was the sentiment/mentions before after a main event (if there was) for “X” to happen – like a conference, election or product releases? What was the ratio in the gains?

I think the figuring out the market shares or volume is right on. I’ve looked at this extensively with political party mentions by share of seats in parliament across all mediums to measure under/over performance.

Some Thoughts:
I’ve always wanted to “gamify” a campaign to measure what options people choose with in a situation. As we funnel down the process/engagement from a click of an online advertisement, to downloads, to checking in to a place via foursquare etc. and or using an off/on line app like Layar; This could provide a good way to measure campaigns to see what way behaviour can be modified from the digital realm and how best to do it. This is why I love the study of Captology.

Ultimately while it’s easy to make things more accurate, but it also gets complex and becomes hard measure for a multitude of brands/products. I think Conversation impact does a good job of measuring specifically what it’s supposed to – conversation. My only suggestion would be to find a way to account for framing techniques on a per medium basis and what technique generates the most engagement (comments, likes, votes). If we as influence marketers can crack the cognitive and linguistic code would be the Holy Grail and I think looking at data is the first step.

Please feel free to ask any questions.