six takeaways from nudgestock

Nudgestock, an event curated by Ogilvy Consulting, took place in the UK, on June 7th. Nudgestock combines creativity and behavioral science, tied together with revelry and ice-cream.

While Nudgestock’s music has died down and the taste of Eton Mess ice cream has faded from attendee’s palettes, the learnings of Nudgestock still remain.

everyone should be a behavioral scientist

Nudgestock attracts an incredibly diverse audience and speaker line-up – academics, creatives, digital experts, public and private sector professionals. This gave a clear message. We’re all in the business of human behavior. And (to quote Rory Sutherland) given that “the greatest gains to be made in business and society today are psychological in nature, not technological”, if you don’t think you’re in the human behavior business, you should think again.

With podcasts like O-Behave from Ogilvy, to books like Choice Factory by Richard Shotton, introducing yourself to behavioral science is easy. Well worth doing when the discipline is so relevant to market research.  

we’re all part of a highly competitive attention puzzle

Spotify’s Will Page proclaimed that “music is just one part of the attention puzzle”. The attention puzzle, one assumes, is vast and competitive (Forbes estimate Americans see 4’000-10’000 adverts daily, indicating the scale of the said puzzle).

But how do we put the pieces of this puzzle together?

Rory Sutherland stated that everything we do should have an ‘excitement attribute’ and we need to better appeal to people’s selfish side – i.e. what will we do for them. In short, be exciting and relevant. So whether you’re discussing a methodology with a client or recruiting a participant, apply these two pieces of advice into all communications and win attention for research and increase its perceived value in the process.

the sciences can live in effectiveness harmony…

Data science and behavioral science are rarely mentioned together. Data science is seen as ‘tech focussed’. Behavioral science perhaps ‘academic’. However, the two can exist in effectiveness harmony.

Behavioral science often relies on experiments to prove a hypothesis. Data science has access to vast amounts of data to experiment with. Uber demonstrated the discipline’s combined value by showing how combining behavioral science concepts, such as observational transparency, with analytics, such as mediation modeling, can generate positive business outcomes.

Cross-discipline collaboration and diverse thinking can unlock more impactful insights. We should acknowledge where external thinking, whether it be academic, design or sector expertise, can add be used effectively.

…but can technology and creative be as harmonious?

Tricia Wang identified that friction between marketing technology and creative mindsets exist. This is often due to quantification bias (the belief of valuing the measurable over the immeasurable). Frequently ‘a metric’ is needed to defend decisions. The unquantifiable ‘gut’ – often the source of the creative magic behavioral science spawns – isn’t regarded as a metric. Gerd Gigerenzer identifies the rejection of ‘gut’ being seen as acceptable evidence to empower action as preventing innovation. Boots marketing director Helen Normoyle supports, saying:“The art of great marketing and great insights and research is a combination of really deep customer insights and data with instinct and intuition.”

Always think outside of the data. Data is often rational. People, less so. Therefore, the answer to understanding irrational human behavior may be in our guts, not our crosstabs.

forget data and technology and focus on people

Artificial intelligence, machine learning, text analytics – the list of computerized ways to understand people is consistently evolving. However, let’s not forget that we need human intelligence to understand human action. Humans are irrational. Algorithms are structured. An algorithm may reject an outlier, but outliers can be irrational acts of intelligence. Therefore, how can irrationality be truly understood by computerized, rigid structures?

The list of emerging methods in the GRIT Report contains many technology platforms. However, to truly understand people, we need time with them. Whether it be via in-person interviews or store observations, ‘real’ time with ‘real’ people is invaluable. Yes, there are cost implications to this. However, ‘real’ interactions with people are the difference between getting overly rational voice-of-the-data and valuable voice-of-the-customer.

let’s simplify how we do it

Gerd Gigerenzer made the case for simple heuristics over complexity. He did so demonstrating the effectiveness of simple heuristics over complex equations across several areas, ranging from how to understand catching a ball, to predicting the weather. However, to provide simpler ways to answering questions we must identify which singular variable matters most when answering the question. Often easier said than done.

Too often surveys are guilty of measuring too many brand attributes or interviews guides guilty of discussing overly minute details. We’d be better spending time in advance of designing research documents identifying what ‘really’ matters vs. measuring/asking everything. Consequently, we’ll make sure we only collect relevant information.