Colourful Numbers

Last week, we served pastries, coffee and crisp thoughts on how to get culturally attuned through data. Charlotte Burt, consultant in the quant and analytics team summarises the key take-outs...

Most people have at some point thought about what superpower they would most like to have. Invisibility? The ability to fly? How about the power to read people’s minds? What if we told you, we could grant you the power to know what people are thinking just from the use of numbers? That’s exactly what Syann and Claire, director and associate director respectively in our quant and analytics team, shared with some early risers at Crowd’s most recent breakfast session.

Using data to tell stories with context: whilst qual techniques are often the go-to method for providing a deep understanding of behaviour, the beauty of quant research is that it can place findings in a more representative context and really broaden our understanding of a particular topic. How do we do this?

1. Think about the outputs – it’s no use thinking about what outputs you want once the research is completed. This needs to happen in the early stages and it’s important to ask yourself: what will the outputs look like? Where can numbers give me the most value? And what data will really help bring the story to life?

2. Use methods tactically – qual and quant often go hand in hand so think about the best ways the different methods can complement each other and give you the best insights.

3. Layer your questions – asking the same question in a number of different ways will really tap into the nuances in people’s opinions. Be implicit. Explicit. And everything in between.

Getting beyond claimed behaviour: there is often a misconception that you can’t truly find out what makes someone do the things they do just by asking them. Wrong. There are ways of gathering and interrogating data that allow us to explore the thought process people go through, and statistics are at the heart of Crowd’s approach. So how can this be done?

1. Trade-offs – presenting people with ‘trade-off’ scenarios (or choice based conjoint analysis, if you’re feeling fancy) can help you analyse the paths people take in their decision making and isolate what factors are the most important to them.

2. Segmentation – grouping people on their attitudes and behaviours (rather than their demographics) is a great way to understand how people differ in their opinions.

3. Key drivers – if you want to find out the relationship between different attributes and what is really making people do the things they do then key drivers analysis is the way to go!

Questionnaire design to gain cultural understanding: everyone has biases. Some we know and some are hidden deep in the depths of our subconscious. The key to great questionnaire design is to recognise what biases might be at play – both in our own minds and in the minds of others when they are responding. This can be tricky, but the following tips can help:

1. Know your audience – think about the type of language that your desired audience are familiar with. Set the right tone and make sure that the look and feel of the survey is relevant.

2. Use frameworks – being mindful of behavioural science and cultural strategy is crucial. Every question you ask should have a purpose and it’s important to consider how each question will be interpreted.

3. Lay the foundations – never approach quant research cold. Whether it’s undertaking up front qual, desk research, speaking to experts or simply reflecting on your own thoughts and opinions, always come armed with information before designing a questionnaire.

As the morning drew to a close and the final croissants and coffees were snapped up, the real question now is… what to do with these new found super powers?

Rise: Colourful Numbers

Come along to our next breakfast session for thoughts and ideas on bringing culture to quantitative research...

Date: February 25, 2016

Time: 8.15-9am

Location: Crowd DNA, 5 The Lux Building, 2-4 Hoxton Square, London N1 6NU

Roll up for our second Rise breakfast session. Titled ‘Colourful Numbers’, this time we’ll be focusing on ways to ensure quantitative data provides a culturally relevant understanding of consumer behaviours and motivations, with Syann Cox and Claire Moon from our quant and analytics team discussing how a more sophisticated approach to outputs helps data to tell stories with more context, how statistical methods can get us beyond claimed behaviours, and how an informed, trends-aware approach to questionnaire design enables a richer exploration of audiences and occasions.

This is perfect for those who are looking for new ways to understand consumer behaviours, those with an interest in decision making processes… and those who want more from quant work than bar charts. It’ll be quick, to the point – and there’s pastries, coffee and stuff too.

Contact Jason Wolfe if you and/or colleagues would like to attend.

Big + Beautiful Data

Here's a data oriented double act, with associate director Claire Moon on author/broadcaster Tim Harford's Google Firestarters presentation, and Eric Shapiro, our creative delivery knowledge leader, reviewing David McCandless' talk at a Guardian Live event. Let's go...

In the first of our two reports, author, broadcaster and FT columnist Tim Harford gave two TED-style talks – one titled ‘Big Mistakes With Big Data’ and the second on ‘How To Tell The Future’. Here’s four relevant insights from his presentations.

  1. Data can’t always speak for itself

At first glance, big data promises to render traditional methods of sampling obsolete (because we now have the data for ‘n=all’), and does away with the need for theories and hypotheses because we can simply ‘listen’ to the data by running algorithms to analyse it.

However, the rise and fall of Google Flu Trends – the poster child for big data – highlights the importance of ‘old-fashioned, boring lessons around how we behave with data’ and the enduring importance of human intelligence at all stages of analysis.

Despite working well at the start, the success rate of the predictions made by Google Flu Trends began to fall spectacularly – and because Google didn’t have a theory for why it worked in the first place, it was impossible to work out why it had gone wrong.

  1. The importance of being human

Despite calling himself a huge fan of big data, Tim advocated human intuition over computer learning and algorithms, and explained why speaking to ‘n=all that matter’ is still a far better approach than attempting to listen to ‘n=all’.

As the volume of ‘found data’ increases, big data is becoming increasingly good at telling us what is happening and identifying correlations, but it can’t tell you why it’s happening and if a correlation actually represents causation – you still need to speak to real humans for that!

  1. Be self-critical

Tim’s final lesson was around prediction, and the importance of being open minded. He spoke at length about a research programme set up by psychologist Philip Tetlock that aggregated a large number (20,000) of quantifiable forecasts made by a broad variety of people. Through this experiment, Tetlock found that the success of predictions lie in correcting biases, working in teams, and in practicing ‘actively open-minded thinking’.

In short, the best way to ensure accuracy when carrying out research and looking to the future is to continually challenge what you find and be prepared to change your mind when new information arises.

  1. Research isn’t always about finding answers

During the Q&A session after Tim’s talks, he was asked about his work for the Scenario Planning division at Shell. Tim’s description of it as ‘science fiction’ got a few laughs, but his point was a serious one – research shouldn’t always be about finding answers. Instead, research should be about stimulating thinking.

(If you want a more detailed account of the event and Tim’s talks, check out Neil Perkin’s great write-up here)


In the second of our reports, we heard Mr Information Is Beautiful (more commonly known as David McCandless) discuss his new book Knowledge Is Beautiful, where he spoke not only of the art of data visualisation, but more deeply on the dividing line between ‘data’ and ‘knowledge’.

Psychology tells us seven pieces of knowledge is about the most information a person can hold, so here’s three things to remember from David’s speech to add to the four from Tim’s.

Knowledge is joined up data

Bored with drawing up immaculate and fascinating data representations, McCandless sought to understand and illustrate knowledge in his new book. He came to the realisation that single data sets only tell you so much. If you want to find something new and genuinely interesting, you need to join up different banks of data to paint a clearer representation. For example, if you want to know who’s top dog, you need to look at a huge range of factors, including vet records, dog genealogies and popularity to reach your goal. It’s the same with insights. To find something new, you need to join up different data types and studies, and view them as one.

3/4 of our brain is vision

Astonishingly, three quarters of our neurons are dedicated to the visual system. We’re incredibly sensitive to beautiful things, but we’re equally aware of ugly things. Even more fascinatingly, we have trust in the former, and are suspicious of the latter. It’s why we describe companies with older or more simple websites as ‘dodgy’, and equally why we forgive glamorous celebrities for just about anything (nice corn rows, Justin…). This means no matter how great, relevant, or life changing a piece of knowledge is, we won’t trust it unless it’s packaged in something beautiful that earns our trust. Equally, we need to be conscious of not presenting something incorrect beautifully, encouraging the wrong sort of knowledge – which means data integrity still matters.

Up wide, crash zoom, to the side

Finally, we learned how in order to extract the best information from data, you need to examine it from all angles. That means looking at the whole picture, exploring the tiny details within, and changing the angle of approach. Take the world of cash crops. From afar, wheat is the most planted, sugar cane the most fecund and most popular, and cannabis yields the highest revenue. That last one’s interesting, no? Well, if we zoom in, you can see that cannabis generates £47,660,000 per square kilometer. And if we look at it from another angle, we see in a state where cannabis is now legal, Colorado, that it reels in more tax revenue than Alcohol. The insight? Cannabis is more lucrative than you might have thought.

Rather than point you towards the illegal drug trade, we reckon this is a lesson in analysis: specifically the importance of using frameworks to view data through different lenses and extract the best and most interesting bits.

(You can see more of David’s beautiful works here, and he’d probably want this blog to link to the Amazon page for his new book – we’ll acquiesce and do this here.)


We resurrected this one from our old blog (it's just too intriguing a notion to discard). Data cuisine: bringing research to life in a manner that's humorous and revealing in equal measure, explains Crowd DNA managing director Andy Crysell...

From hacking events to music innovation seminars (and, of course, the odd DJ), the Sonar festival in Barcelona is not short of stuff to get you thinking differently. But we weren’t necessarily expecting to encounter a session on data visualisation – particularly one that involved food.

Susanne Jaschko and Moritz Stefaner’s data cuisine project for Sonar saw them work with a group of 15 people at the Center Of Contemporary Culture Of Barcelona, collecting statistics about the city and expressing them via new recipes, prepared with the assistance of professional chefs. It’s an interesting and radical realisation of the oft discussed challenge of bringing research to life – humorous and revealing in equal measure (though obviously not so easy to knock up when a client deadline is looming).

You can find out more about their work here. Visualisations cooked up to date include a fried dorada, with sections prepared in different ways to represent emigration from Spain (battered fish for the UK, with a wine sauce for France, cooked in beer and parsley for Germany etc); the sex lives of folk in Barcelona depicted through noodles; a cocktail made with measures to represent suicide trends; and an unemployment Pan Con Tomate.

Crowd DNA’s toaster and microwave will soon be put to the test.