This document discusses the concept of "fluent innovation" and how behavioral science can help make new ideas successful. It explains that people are more accepting of ideas that combine novelty with familiarity. Several examples are given of innovations that succeeded or failed based on how well they balanced the familiar and unfamiliar. The document advocates framing new ideas by explaining what makes them work and what makes them genius in a way that builds on the familiar. It describes how predictive markets can be used to evaluate how fluently an idea is presented and likely to be received. Overall, the key message is that innovations are more likely to succeed if they are presented in a way that feels instinctively right and familiar to potential users or customers.
Most Influential HR Leaders Leading the Corporate World, 2024 (Final file).pdf
Fluent Innovation: Using Behavioural Science to Make Your Next Big Idea a Success
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Fluent Innovation
Using Behavioural Science to Make Your Next Big Idea a Success
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Traditional polling doesn’t work so well (just ask Ed)
Rather than focusing on sample considerations, we wondered if we could foretell outcomes another way – using behavioural science.
A US election seemed like the ideal way to try it out.
But then….
Traditional polling doesn’t work so well (just ask Ed)
Rather than focusing on sample considerations, we wondered if we could foretell outcomes another way – using behavioural science.
A US election seemed like the ideal way to try it out.
But then….
Traditional polling doesn’t work so well (just ask Ed)
Rather than focusing on sample considerations, we wondered if we could foretell outcomes another way – using behavioural science.
A US election seemed like the ideal way to try it out.
But then….
Automotive sales forecasts traditionally focus on predictors such as advertising, brand preference, life cycle position, retail price, and technological sophistication. The quality of the cars' design is, however, an often-neglected variable in such models. We show that incorporating objective measures of design prototypicality and design complexity in sales forecasting models improves their prediction by up to 19%. To this end, we professionally photographed the frontal designs of 28 popular models, morphed the images, and created objective prototypicality (car-to-morph Euclidian proximity) and complexity (size of a compressed image file) scores for each car. Results show that prototypical but complex car designs feel surprisingly fluent to process, and that this form of surprising fluency evokes positive gut reactions that become associated with the design and positively impact car sales. It is important to note that the effect holds for both economy (functionality oriented) and premium (identity oriented) cars, as well as when the above-mentioned traditional forecasting variables are considered. These findings are counter to a common intuition that consumers like unusual–complex designs that reflect their individuality or prototypical–simple designs that are functional.
Traditional polling doesn’t work so well.
Rather than focusing on sample considerations, we wondered if we could foretell outcomes another way – using behavioural science.
A US election seemed like the ideal way to try it out.
But then….
Traditional polling doesn’t work so well.
Rather than focusing on sample considerations, we wondered if we could foretell outcomes another way – using behavioural science.
A US election seemed like the ideal way to try it out.
But then….
Traditional polling doesn’t work so well.
Rather than focusing on sample considerations, we wondered if we could foretell outcomes another way – using behavioural science.
A US election seemed like the ideal way to try it out.
But then….
Traditional polling doesn’t work so well.
Rather than focusing on sample considerations, we wondered if we could foretell outcomes another way – using behavioural science.
A US election seemed like the ideal way to try it out.
But then….
Able to turn it into a star ratings
Star ratings=real market success
Traditional polling doesn’t work so well.
Rather than focusing on sample considerations, we wondered if we could foretell outcomes another way – using behavioural science.
A US election seemed like the ideal way to try it out.
But then….