Back To The Future
There is no greater magnet for hubris and nonsense than the end of year TREND PREDICTIONS post, so obviously I make one every year. There is a little blackbeard angel on my shoulder, though, who tells me “At least be accountable!”. So before posting this year’s set of stocking-fillers, here’s my yearly look BACK at what I’ve claimed in the past.
The 2009 list - ten predictions, date of fruition unspecified. And the 2010 list - eleven thoughts mostly about “big data”. What was I right about and how much? I’ve sorted the ideas into four categories in roughly descending order of correctness so you can see for yourselves…
RIGHT!
- Play Power: In 2009 I predicted we were going to see a lot more examples of researchers engaging people by learning from game designers. Unless this is the first research blog you’ve read in 2011 you’re probably aware by now that I got this one right. (Though outside research I am no fan of “gamification”.)
- Research robots: When I talked about these in 2009 I wasn’t aware that BrainJuicer were already running them - they’re now a successful and developing product for us (and BrainJuicer is now “us”). Have they reinvented research? Well, no, but they’re here, they work and they’re improving. Next step: Siri style interactive consumer-bots for marketers so you can literally ask them a question!
- Targeted microsurveys: I suggested that the algorithms that serve up Google Ads would be useful for personally targeted micropolls and mini-surveys. This is basically the approach that Facebook’s research business is now taking. The idea of a marketplace of demographic/target group “keywords” a la Google Ads hasn’t materialised, but this is still on the road to rightness.
- Post-rational respondents: It has been in the air for a while, but the idea of the irrational respondent was bigger than ever in 2011. Many now agree with it (though differ as to how irrational we are, and why) but there’s still a lack of solutions at scale.
GETTING RIGHTER!
- Social meteorology: The idea that social media is a weather system - granular readings (it is raining now) are less useful than a general understanding of network effects and patterns. I expressed this so vaguely that it was bound to have been a BIT right but I still think research is waking up to network analysis and effects, thanks partly to agencies like Face and thinkers like Mark Earls.
- Dataholics anonymous: Not a prediction so much as an observed trend - the idea that the response of companies to “big data” seems to be to want EVEN MORE DATA. This certainly hasn’t slowed, partly because the exponential increase in knowledge about consumers is really an exponential increase in measurable variables - i.e. the gaps in datasets are growing at the same rate as the sets themselves.
- Paradata: Not quite the all-conquering buzzword I predicted last year but passive measurement (to use its less nerdy name) is definitely bubbling under as a hot idea - its fortunes intimately connected with mobile research applications, so expect to see it feature more prominently in mobile case studies next year.
- Redefining the representative: In a nutshell, the idea that social media research will become less worried about representative individuals or samples and more about representativity at the network or conversation level. I’ve not seen this talked about much but there are echoes of it in Face’s “mapping the conversation” approach so I feel the green shoots of rightness are poking through.
- Self-research: AKA the “quantified self” - this is getting more mainstream all the time - this article in Research looks at it in terms of economic control of personal data but I still think there are much wider implications: a more data-literate society is one where what researchers can ask, expect and learn is greatly increased.
STILL MIGHT BE RIGHT!
- Data brokers: The idea that consumers will realise they can make money off their own data and sell it to the highest bidder rather than simply passively acquiesce to its use by web services. There are good grounds for suggesting this won’t work but people are starting to try it: start-ups like Allow and Personal offering privacy management services - managing and controlling the data being the first step to exploiting it.
- Post-respondent research: In 2009 I pointed at the emergence of a belief that all questions could be answered by mining the datamass. You still see this in the wilder pronouncements around “Big Data” but I honestly don’t feel it’s MUCH closer.
- Liquid communities: This was my pet idea about spontaneous community-like events being a better model of how the Internet works than stable walled “communities” - I still think this is true (truer than ever, tho there’s an argument for saying social networks are becoming less dynamic) and I still think it’s an opportunity for research but not one that’s been taken yet. Just about in the “still might be right” category.
- Data Bubble: In 2010 I speculated about the bursting of the “data bubble” - not so much that the amount of data available will lessen but the demand for new data, and the perception that data is de facto useful, will decline. This might happen, though more likely I think is that analytics platforms will wire themselves into the corporation like powerpoint or email - something whose problems and flaws become known and cursed but which is too embedded to get rid of.
- Infographic backlash: I was right - to be fair, so was everyone - about infographics being big in 2011. I was wrong to suggest a widespread backlash against them, particularly in the research industry, which is still waking up to the idea. The terms of engagement are shifting from “WHOA AMAZING” to “OK how do we actually DO them”, though.
ERM… NOT VERY RIGHT AT ALL!
- Business class research: The idea is that high-value respondents would be given tailored/bespoke research experiences to maximise the value to them of research experiences (rather than simply maximising the incentives). While there’s been a lot of emphasis on respondent experience, there’s been very little evidence of “two-speed” experiences emerging.
- Peak Crowd: The idea that respondents would get sick of crowdsourcing and communities and their utility in terms of increasing engagement would drop off. In the wider world of marcomms misguided or exploitative “social media” efforts you could argue this has happened, but in research it hasn’t, probably because the baseline of interest was so feeble anyway.
- Social Sabotage: Trolls, astroturfing and other attempts to game social media monitoring would undermine its perceived value. This hasn’t really happened, or if it HAS it hasn’t been exposed. On the other hand it’s become industry received wisdom that sentiment analysis doesn’t work that well anyway!
- People Not Using Facebook As A Proxy For All Social Media: LOL.
