I was hoping that most people would feel like you did

As one of your colleagues, Tom, has pointed out above, it's really important that we (as an industry) start to talk about measurement in a meaningful way rather than trying to make a quick buck – building internal tools that are tested and verified by analysts will be a big part of that in my eyes.

Thanks again for commenting!

Integrating macro monitoring is key to any online reputation measurement – but rarely is!

I hope that there's the level of Darwinism that you're predicting

First of all, let me just say that I completely agree with you – SMM is absolutely littered with problems (if you look over my last post you'll find *another* rant about automated sentiment analysis). Where I would add to your point about understanding within the industry (for industry, read comms) is that very, very few people understand social media, let alone measurement, let alone data reliability, let along regression analysis or support vector machines. You're in a very fortunate position as you *do* know all of this, and much much more by the sounds of things.

I'd also like you, and everyone that reads this post from this point to see this as an opening gambit, a prologue. There are many, many flaws with measurement (on the side of the measurement tools, the agencies that use them and the clients that demand them) and I WILL be writing much more on these issues – this wasn't a dumbed down post, it was an introduction. Yes, the title was on the edge of link bait, but so what? In a sea of comms. bloggers regurgitating the same posts week in week out I wanted to say something fresh (while you'd disagree that this is fresh, to many it is – see the comments pointing this out) that would get people talking.

Open debate will only happen when people realise that there's a problem, and I hope that people like KD Paine and Jason Falls will help to bring these issues to the forefront.

Again, thank you for commenting

Jed

However SMM does have many problems, to my mind a couple of these are with really understanding data, data integrity and over reliance on machines. Very few people in this industry truly understand regression analysis or much beyond basic cause and effect, too few know how to set up full Boolean searches or how SMM tools capture data and SMM tools themselves have a very long way to go, for example: the same terms entered into different tools will often capture very different results and trends due to different coverage strengths of the tools themselves, worse than this: if the person setting up the tool does not have an intimate understanding of it's strengths, weaknesses and mode of data collection you end up with a very messed up dataset from the start. Also, Spam blogs and spam tweets often create huge false positives and problems with getting to really good, relevant info – this is fine if you have a human hand to go through and check datasets, but few people budget properly for this, hence: over reliance on machines.

Too many agencies, and brands, are just “phoning it in” – buying tools they don't really understand and extrapolating results based on flawed data. They all seek context and comparison but when the basics of data integrity, consistency, system setup and statistical methodology do not sit behind that then of course they end up producing crap.

In terms of taking next steps: well I agree more with this one, what use is good info if it's not acted upon?

I guess, to summarise: I don't feel this post really hit at the heart of the many problems and areas of development needed in this industry, I feel it was a little to obvious and simple to really warrant the sensationalist title, I think we all need to openly debate a number of issues if we really want to improve social media monitoring, my rambling comment has covered just a few of these issues, what do you think?

I'm integrating macro monitoring with web analytcis and other relavant metrics.

All this data is new to many clients and I think qualified analysts now need to wade in, separate the wheat from chaff, and create some context.

A lot of the low budget tools seem lack customisation and advanced data mining capabilities. As time goes on we'll start to see a lot of these substanceless tools end up in analytics graveyards.

Great post – the tagline did exactly what it should've done, had me ready to 'defend, defend' while waiting for the page to load, then lowering my heart rate as I read though the post

I cannot support your opinion strongly enough – while the idea of 'real-time' monitoring is an exciting concept, with some value, where listening can really come into it's own is where it is given the time to contextualise itself and be analysed thus.

Our analyst team use our listening tool to gather appropriate data etc. but then are skilled in taking this data and putting in in contaxt to provide actionable insights. Any data read in a silo loses its importance, and a dashboard can only tell you so much. Human analysis adds understanding to listening.

Thanks again for the post!

Jacqui (@JacquiHill)
1000heads