Another long post, no doubt, but an interesting one that I have been thinking about a lot, over the last two weeks or so. For one thing, Google Reader got messed up recently due to Google Plus. I tried a few workarounds but they don’t work for my iPhone RSS applications, and it appears nothing seems to replace the functionality that “notes” in Google Reader had till recently.
Enter Alterian SM2, which does seem to be catching most of my notes and the urls that Google Reader no longer does. My guess is Sysomos MAP would also catch my Google Reader selections, though what Sysomos shows is a very small selection to what Alterian appears to be picking up (I, and many others are angry at Google for dropping this feature) with provides me with a viable, if indirect workaround. Accepting that no 2 analytics platforms seem to agree on any results, for anything – I’ll move past that and just focus on what Alterian SM2 offers.
Google Plus Shared Notes Results that used to show up in Google Reader (and no longer do).
This chart confirms the data from my Google Reader Notes and Selections are present still, and being collected, it’s just not displayed any longer. I believe there are more notes I made that aren’t be shown by the chart above, but I’ll take what I can, assuming that if my search query were more expansive, yet exact, I’d capture everything (and the only reason I’m not seeing all my notes, is I haven’t taken the time to write the best query I could write, that, in fact, covers everything).
But it also occurred to me, as I started reading Zite (Zite application for my iPad) that maybe I didn’t need Google Reader anymore, period (as long as I was willing to use my iPad for all my RSS reading). Zite seems to have gotten down how to automate and select the best content for an individual’s reading consumption, without asking me or anyone else to select RSS feeds I’m interested in – and by just looking at my followers on Facebook and Twitter, etc.
Overall, I think Zite has done a much better job than Google Reader and selecting and presenting information in a social format, and it proves to me Google is, more vulnerable to lose subscribers and users, than I had believed to be the case.
TechCrunch has a post on “What if This is No Accident? What if This is The Future?” that …..
“… we have hit an inflection point at which technology destroys jobs faster than it creates them. Kling writes (at length, but it’s worth reading): “The new jobs that emerge may not produce a middle class … gains in well-being that come from productivity improvements [may] accrue to an economic elite … we could be headed into an era of highly unequal economic classes. People at the bottom will have access to food, healthcare, and electronic entertainment, but the rich will live in an exclusive world of exotic homes and extravagant personal services.”
Which sounds eerily like what we would get if we extrapolated from today, no? While millions of long-term unemployed fight desperately to tread water, technology’s handmaidens — software engineers — are minting money like bailed-out bankers. That Stanford survey mentioned above seems to undercut Peter Thiel’s take that “We’re in a bubble and it’s not the Internet. It’s higher education” — but guess again:
It’s beginning to look like we might have entered a two-track economy, in which a small minority reaps most of the benefits of technology that destroys more jobs than it creates. As my friend Simon Lawsays, “First we automated menial jobs, now we’re automating middle-class jobs. Unfortunately, we still demand that people have a job soon after becoming adults. This trend is going to be a big problem…”
I think this is all true, unfortunately, and it’s logical conclusion, looks like a dismal future for the vast majority – perhaps one thing that people are reacting to (ie: Occupy Wall Street).
Gary Angel has a collection of post over the last week, that are all excellent, and gave me much room for thought, one of his posts appealed to my artistic sensibilities (machine learning), which I’ll focus on more, shortly. However the real clincher was a post on Social Media and the Sampling Problem that Gary shared the other day (the sampling issue was covered in an earlier post – almost no platform can, by it’s nature, provide a comprehensive sample of social data, which introduces bias that are difficult to detect and correct).
“…. it turns out that Wharton’s research has, at least partially, addressed an even larger issue: how representative is social media chatter of word-of-mouth (WOM) conversation. In other words, if you look at social media mentions, are they the same kinds of subjects and attitudes expressed by people in real conversation?”
If social media isn’t representative of word of mouth, then the whole enterprise of brand tracking is doomed, sampling be damned.
The answer, based on the Wharton study is that most social monitoring data does not represent true Word of Mouth conversations – a real shocker!
“… For at least some (most…a few…many?) industries, there is no valid use of Social Media Measurement as a reflection of brand WOM and sentiment no matter how careful your measurement.”
That’s important.
“…They listened to a social community and they setup a Word-of-Mouth (WOM) experiment and they compared the results for two different industries.”
“… In their study, automotive social chatter correlated reasonably well with WOM. Beauty-Supply chatter had zero correlation with WOM.”
“…In other words, if you’re Ford, then if you can listen very carefully, you have some chance of using Social Media to measure brand sentiment. If you’re ProActiv, no matter how carefully you listen and sample, you’re not tracking actual consumer sentiment when you listen with Social Media.”
“…And the million dollar question, of course, is what if you’re neither automotive or beauty supply? How do you know if social media measurement is correlating to actual customer attitudes?
You don’t.“
In other words, if your monitoring Pharmaceutical conversations around specific drugs and indications (as many do) there’s a good chance that your wasting your time, no matter how careful your keyword groups are, how good your noise filtering is, how accurate your word clouds are. I goes beyond what platform your using – the whole concept of what is being done, and how, may need to rebuilt, from scratch, and most of your previous findings, thrown out.
It very well may be that most of the reasons Social Monitoring is being sold for, are, and their core, unreliable and unrepresentative, if this study holds true against many verticals, as it more than likely does.
Gary goes on to add, and echo my thoughts precisely that …..
“…. Of course, that won’t prevent countless companies from selling you their tools and services on the assumption that it all works perfectly for you. It won’t prevent countless organizations from presenting this data as if they were confident of its meaning.”
“..The research here suggests that for understanding the relationship between Social Conversation and true WOM, the key variables are local – specific to every industry. That’s never the most pleasing answer, but reality is not ours to choose.”
That’s why I wrote Social Media Analytics, because there was so much “snake oil” out there, particularly in the MARCOM space, and the Wharton study that Gary Angel cited, pretty much confirms it.
“…But I actually took encouragement from the study – the correlations in automotive were better than I would have expected. Of course, these were academics designing a careful experiment (not your PR firm slapping together a Keyword Profile) and there are many other factors -such as social campaigns – that might visibly distort such findings over time. On the other hand, the social medium is becoming more reflective of the general population and perhaps conversations are becoming steadily more representative of WOM. It’s certainly possible, and at the very least it suggests that Social Media Measurement for some industries might fruitfully include brand tracking.”
So, there’s hope.
“… It’s not as if Social Media measurement is endangered by this finding. The functions of social media extend far beyond consumer brand-awareness and sentiment tracking. And even for that particular function, there is some merit in understanding “social consumer brand awareness”. If social matters as a channel, then how you’re talked about in that channel matters, even it is not representative of true consumer conversation.
It does mean, though, that if you want to use Social Media Measurement for broad brand awareness and sentiment tracking, then you have some work ahead of you. Work that involves first proving the validity of the relationship and then controlling your samples to the greatest extent possible.
I’ll add, that this “work” is probably of a nature and cost that no one I’ve met, so far, are willing to pay for; and even if they did, they would not know how to undertake it, and do in meaningfully, in the first place. That was another reason I wrote #smabook.
Hey, I guess if people want to eat “cotton candy” there should be the makers of that – so that water finds its own level. On the other hand, the while people are focusing on the wrong things, using inaccurate data, they are being robbed of real insights, much as if they eat cotton candy all day, they’ll die of malnutrition, or choke on it.
By the way, in finishing this long post – I’m speaking at SOCIAL MEDIA MONITORING & ROI METRICS FORUM 2012, MARCH in Sidney, Australia, at the end of March 2012.
29 March – 30 March 2012 Grace Hotel, 77 York Street, Sydney, NSW, Australia
The conference will be fully interactive with presentations, case studies, Q & A sessions and panel discussions. There will also be a ‘bootcamp’ with demonstration of many of the leading social media monitoring and measurement tools, services and ROI generating initiatives
“Accurately measure your social media impact and ROI”
Cheers, Mate….
