From Big to Friendly Data

Guest Post by Richard Shaw, VP JuiceGen Labs

There’s a slightly dorky game show inspired digital assistant that lives on my PlayStation called Max. This Siri-like character guides you through choosing what to watch next on Netflix and comes with his very own game where you rate movies and TV shows on a five-star scale. Netflix

I would be willing to hazard a guess that the algorithms powering Max’s content suggestions are the same ones Netflix uses for its regular content recommendations, it’s just that Max’s suggestions feel so precise.

Last weekend I was in the house on my own, eating beige-coloured food and looking for something dramatic. Max offered up Zodiac, a Jake Gyllenhaal movie I had previously overlooked, but it was so right for a quiet Sunday night. This feeling of precision seems to come from two places.

Continue reading

Do We Really Need A System 3?

Rory Sutherland, founder of Ogilvy Change, is as much behavioural raconteur as behavioural scientist – but he’s a brilliant writer who’s done more than anyone else in the UK to popularise behavioural economics and decision science among marketers. So we tend to pay attention to his ideas – even (especially!) the cheeky ones.

The Mad Thinker – a System 3 pioneer?

In his latest “Wiki Man” column, Rory gives a quick overview of System 1 (fast) and System 2 (slow) thinking, then suggests that maybe we need to consider a third system of thinking. System 3, says Rory, is what happens when human beings augment their brains with computer-assisted decision making. He gives so-called freestyle chess as an example, where chess masters use chess computers to play at a far higher level. Continue reading

Our Best Blog Posts Of 2013

It’s been a fine year for the Brian Juicer Blog – we more than doubled our readership and views from 2012 – and it feels like a good time to spotlight the posts you liked most this year. A countdown, no less, of our most popular content this year!

10. Packaging – The Plain Truth? The Australian government has introduced Plain Packaging laws on cigarettes – this post took a look at the intitial results. It’s ambiguous whether the packs are reducing tobacco consumption – but they do seem to be making it less enjoyable, which raises a whole different set of questions…

9. Doctor Who And The Anchoring Effect Back in November, the BBC announced it had recovered some of the “missing episodes” of long-running sci-fi show Doctor Who – but not quite as many as had been rumoured. The reactions were a perfect encapsulation of psychology’s “anchoring effect”….

8. Research And Big Data: Seven Roads To Enlightenment In February, BrainJuicer partnered with the AMA to organise the first ever Analytics With Purpose conference. (The second one is coming up!) Here, conference chair Tom Ewing summarised the themes of the conference and of research’s relationship to big data.

7. Moving The Elephant If you’ve seen one of our Webinars this year (and if you haven’t, check them out!) you’ve probably seen “the elephant and the rider” – our favourite metaphor for decision making. This is the post which explains why we like it and where it comes from.

6. When Stories Suck The more specific a story is, the more likely it is to be believed… but the less likely it is to be true. This post looks at the dark side of “storytelling” and its dire implications for common research techniques like segmentation.

Any excuse, frankly.

Any excuse, frankly.

5. Taming The Panda “Brand Trackers are the Giant Pandas of research”. ‘Nuff said.

4. Eight Days Of Emotion Emotions matter – so back in March we took the opportunity to throw a spotlight on each of our basic emotions in turn. This master post collected the series – find out which ads use sadness best, the difference between contempt and anger for a brand, and the uses of happiness (and lots more).

And now, the top 3…. Continue reading

Research Is Doomed! (Part 759)

Every few months one of the business sites publishes a piece having a go at market research. Usually these articles are a heady mix of dramatic predictions, weirdly dated assumptions about what researchers do, and – let’s be honest – a few sharp truths.

This one, in Forbes, is no exception. The gist is that “Big Research” is going to get kerb-stomped by “Big Data”, so the piece combines its downer on research with a bouncy optimism about the infinite powers of big data. I’m not going to go through it point-by-point, and anyhow plenty of other commentators have outlined where big data and research fit with each other. But this line stood out.

“What possible similarity is there between a person in 1972 and today in terms of how they respond to an ad?”

1972 fashion

1972 – too remote to matter?

What indeed? Let’s start with biology, psychology, emotions, how they make decisions, the fundamental needs they might be trying to fulfil… but no – in a world transformed by technology none of those things matter. Continue reading

Familiarity Breeds Content

What makes a hit? Understanding why things become popular is one of the foundational questions of marketing, but a surprisingly difficult one. It’s easy – too easy – to come up with reasons for success which are satisfying from a narrative and a commonsense perspective, but which are ultimately completely untestable.

This is where large-scale data analysis can really help. In Brian Uzzi, Satyam Mukherjee, Michael Stringer and Ben Jones’ new paper, Atypical Combinations And Scientific Impact, they turn the lens of analysis onto scientific papers themselves. They look at 1.7 million papers to find out what characterises “hit” – i.e. widely cited – works of science across a variety of disciplines, rating them for novelty and conventionality. (Abstract here, PDF presentation here)

Common sense might suggest that novelty is the main factor – a paper that discovers something completely new will surely have greater impact than one which relies on older and more common knowledge. But this turns out not quite to be the case: hit papers actually do both. A hit paper scores high on new knowledge, but also scores unusually high on “conventionality”. As the authors put it, “papers with an injection of novelty into an otherwise exceptionally familiar mass of prior work are unusually likely to have high impact”. It seems Isaac Newton wasn’t just being humble when he said “If I have seen farther than others, it is by standing on the shoulders of giants.” Continue reading

Big Data And The Power Of N=1

Since the birth of the teenager, what teens do has been a source of shock, worry and voyeuristic fascination for adults. Every adult was once a teen, which means the focus of their concern isn’t, deep down, the behaviour, but the specific cultural clothes it’s dressed up in. Fashion, politics, music, videogames, and nightlife have all fallen under the judgemental spotlight, assessed with a mix of disdain, bafflement, and barely disguised envy.

Nowadays, of course, the point of interest is social media. “How teens use social media” is the subject that launched a thousand posts. Infographics scream about a handful of percentiles difference in teen activity across sites. Newspaper thinkpieces peer, smelling salts in hand, at teenage hookup apps. Marketers oil up and wrestle each other in pits for the right to name the Generation after Y and map its digital life.

teens_on_phones_copy

Most research reports feel as authentic as this photo.

And pieces like this one get read, a lot. “I’m 17 And It’s All About Brand Me”, writes Carmin Chappell about her highly mediated, presentational, digitally-enabled life. It’s a very good piece – self-aware and full of good examples. It deserves its 1400 tweets and 800 Facebook mentions – a whole lot more than any blog post by me, or by any other researcher I can think of. (Yes, it’s on Mashable, so it automatically has a bigger audience – but Mashable knows what its readers want.)

But as a researcher, it made me feel bad. The thing is, making information exciting and shareable is our job as researchers – or ought to be. Continue reading

Return To A World Without Questions

Tom Ewing’s World Without Questions paper recently won its second ESOMAR award. 18 months on, he looks at the arguments in the paper and what’s changed since writing it.

At the ESOMAR Congress in Istanbul last week I was delighted and honoured to get another award for the paper I co-wrote with Bob Pankauskas of Allstate Insurance, Research In A World Without Questions. The paper won the Excellence Award for the best paper at any ESOMAR event this year – a flattering claim!

(If you want a copy yourself, let me know – I’ll get it to you ASAP.)

But the paper was published a year ago, and written almost 18 months ago. So I thought I should take a fresh look at it, and see if there’s anything I should add or update.

Too many questions!!

Here are five extra thoughts about the World Without Questions.

Context Is King. The paper’s central theme was that market research can and must move away from a reliance on direct questioning, and think more about the context of decisions and behaviour. This focus on context seems more important than ever. Not only that you need to know deeper context to fully understand behaviour – and you can’t always get at it by asking questions – but also that the immediate context of behaviour can often only be accessed by observation. For instance, the actions of other people, or the way choices are framed, can be critical influences at the moment of decision.

System One Has Won. Since I wrote the paper, Daniel Kahneman’s Thinking Fast And Slow has become a surprise global bestseller, and Kahneman’s ideas about decision-making have become common boardroom currency. Obviously we had nothing to do with Kahneman’s rise! But our instincts about his importance were right. 18 months ago we used to get standard research briefs and talk about System 1 and 2 in our proposals. Now more and more briefs are specifically asking about implicit and unconscious decision-making, and there’s less expectation that direct questioning is the way to go.

Brains Are In Fashion. If I was writing the paper now, I’d give more space to location analytics and especially to neuroscience, which got slightly short shrift. Scalable neuroscience – from biometrics to facial coding – has emerged as a big part of the “System 1” toolkit, though it shouldn’t be the only part: it’s not great at taking social or environmental influences into account, for starters. But its promise of direct access to the subconscious mind is finally starting to be fulfilled after a decade-plus of hype.

The Battle For Big Data: I also didn’t spend a lot of time on “big data” – not because it isn’t important but because I felt that most researchers wouldn’t have the skills to deal with it and that the research industry didn’t carry a lot of weight in the conversation anyhow. For all the noise about big data, I haven’t seen a lot since then to suggest I was wrong. A lot of the big shifts seem to be in infrastructure – moving from discrete packets of data (generated by projects) to continuous flows, which inevitably reshapes how analysis and “insights” work. Researchers need to adapt to this – the subject of many other fine papers given at recent conferences – but have little control over it, and it seemed important in the paper to examine the practical changes we could make.

Never Mind The Insights: One thing that struck me reading the paper again is how much it assumes the role of the researcher is essentially advisory, not proactive. I made the case for “research without questions” based on coming up with better insights and recommendations. While these are important, what we’ve found in the 18 months since is a real hunger for actually implementing, experimenting and iterating in research. Don’t just recommend a behavioural intervention – set up a control and a test sample and experiment with it. Think an ad might be better in a different edit or with different music? Make and test it yourself. If there’s one thing which has changed in my everyday working life since writing the paper, it’s this greater emphasis on moving beyond recommendation into testing. Our appetite for questions may be diminishing – the desire for evidence isn’t.