Behavioural Design (BD) is a systematic approach for applying behavioural insights to solve design challenges that centre on human behaviour.
We humans are rational animals. We are the creatures of habit. We run in a auto pilot mode, instinctively rather than rationally. This nature of our behaviour is what makes changing our behaviour complex. In past, for changing our behaviour we educated people but it took lot of time and money. So, we choose a new powerful approach. We design an intervention right before the desired action, this is how we can change behaviours of individuals with BD.
Getting people to change and take action can be tough. That’s why we rely on behavioural science. BD is the application of behavioural sciences — cognitive neuroscience, behavioural economics and proven experiments conducted by thousands of behavioural scientists around the world — to change human behaviour.
Why do people do what they do? And how can we change/guide/reshape them?
These questions can be answered by implementing BD in our product. Before that lets drill down into better definition of what Behavioural Design much clearer.
Behavioural design is a framework for intensionally and systematically changing human behaviour through persuasive modification from physical and digital world.
We can break down the definition above into major key-points:
It’s a Framework:
BD is a framework for set of ideas that helps us to describe and predict, how and why people predict the way that they do. Its also about changing their behaviour. Lets say our team is
It’s Intensional:
BD is a framework for our design teams can use ti deliberately to achieve specific goals (behavioural results). This is a really helpful tool when we have any specific behavioural outcomes we are intended in achieve people goals.
It’s Systematic:
BD is rooted in observations and experiments from the fields of psychology, neuroscience, and behavioural economics. It is backed by explanations of why people do what they do, which our team mates will lead to certain predictable behavioural change.
It’s About Change:
BD at its core is about changing behaviour. Behaviour that you want people to start doing or stop doing. BD offers us tools we can use for helping people to achieve that kind of change.
It’s About Persuasion:
BD uses insights how our brain operates and strategies for persuading people to change their behaviours. It is not about creating coercion. It’s not about forcing people to change. Instead it powers us to use to increase the chance that someone changes their behaviour without forcing them or violating their autonomy or dignity.
It’s About The “Environment”:
BD helps how people behave by changing their environment. When we say the word environment: the immediate reality in which someone is performing his/her behaviour. For example: If someone is operating an mobile app, the environment would be the app interface (UI).
Behavioural Design works best when the designers and the user want the same thing. — Boundless.ai
Ethics of Behavioural Design:
When used properly BD takes advantage of cognitive biases to make particular behaviours more likely to occur. It does not force certain actions neither it violates or threatens people to change behaviour. It merely provides testable, provable way to increase the chances that behaviours change : its a technology of changing behaviours not about forcing anyone to choose decision.
How we use determines whether or not it’s ethically-aligned. Weather a pattern is used as a Dark Pattern or Light Pattern is a product of its intention and its use. There are BD techniques used to intentionally subvert, manipulate and deceive users. We can find these techniques in the site Darkpatterns.org to have a look how companies have used to undermine user intentions.
Behavioural Design Toolbox:
BD offers ideas and techniques for persuading user behaviour. Some of these techniques for persuading user behaviour. Some of these techniques encourage behaviour to occur while others discourage it. For each one there is a purpose -and for each purpose a tool.
Reinforcement Learning:
Its also called Reward learning or RL increases the frequency that someone performs a behaviour. RL focuses on how a habit can be induced by carefully controlling the rewarding consequences of an action. It predicts how often people engage with an app or a product, and for how long they’ll retain using that app. It works on behaviours that people already do, as well as behaviours they do not yet do.
CUES:
Cue also known as trigger is a prompt to perform an action. When used together with reinforcement learning, they increase the frequency of the behaviour (its C in CAR Model). Cues work because often users automatically perform certain behaviours in response to signals from their internal and external environments.
Optimal Challenge:
Optimal challenge prescribes that at a given movement and for a given person, there exists an optimal amount to challenge or push someone to perform an action. Challenge less, and we may not help the person to achieve their goals. Challenge more and you may induce fatigue and user burnout. Optimal challenge proposes that not only does this “sweet-spot” exist, but that it is detectable, predictable, different between people, and changes with respect to time. Optimal challenge can be used to induce both a behavioural increase or a behavioural decrease.
Soft Incentives:
Soft Incentives describes how your app may use the future promise of emotional or community based value to encourage a single, challenging, one-off behaviour today. It largely rely on our desire for personal accomplishment, congruence with our narratives of identity, or the approval of our friends.
Sunk Cost:
This technique if sunk cost leverages the cognitive bias of sunk cost fallacy for which people inaccurately over-value experiences, relationships or products because they’ve already given them time, money, information or opportunity.
Optimal Group Structure:
Users exhibit different preferences for their interactions with each other: some feel most motivated alone, others in small groups of peers, and some in the full gaze of public. Optimal Group Structure proposes that there exist predictable, optimal scopes and nature of interactivity between app users that will best motivate them to change their behaviour.
Choice Architecture:
As a Behavioural designer we can design someones environment as to create specific, intentional default actions they will take. Its best to guide users towards towards a particular desired choice, when faced with several possible actions they could take, and it can be used to both increase and decrease behaviours.
Ambient Communication:
Often you’ll need to present complex or dense information to someone. Using non-text communication, such as colour, size, texture, pattern, motion, sound, vibration, or time can help you communicate complex content to user quickly and intuitively.
It relies on the brain other non-verbal information processing streams to understand the environment much faster then reading alone might afford.
Stimulus Devaluation:
Stimulus devaluation (SD) provides a techniques for destroying a learned Cue-Action association by introducing delay and friction between an Action and its associated Reward. The resulting experience still allows a user to perform their desired action while destroying the habit forming nature of the experience. Stimulus Devaluation works best to decrease behaviours that people already perform, especially those for which they’ve been positively reinforced using the CAR Model.
Stopping Rules:
Stopping Rules (SR) prescribes that stopping cues can be only controlled to increase or decrease certain behaviours. One commonly seen example of stopping rules is to remove all of the normal stopping cues from an environment. Without stopping cues, users consume much more. Ubiquitous infinite scroll social feeds leverage this effect: the ore users view content and more content is loaded for their consumption.
Optimal Information Flow:
People perceive information differently from one another. For example, steps in a signup workflow that might be intuitive to one user might be widely disoriented to another user. The purpose that there exists, for each person, an optimal ordering of steps for a process and an optimal destiny of information they can be presented with before they struggle to perform tour desired action. This technique works best on actions that we need people to perform once or infrequently, such as a signup form or completing a purchase.
Personalisation:
Everyone does not thinks the same or wants the same thing, Many design teams use personas to help design to different user groups, skills levels, or user needs. New machine learning techniques are making it possible for designers to produce even more adaptive products.
Cognitive Load Balance:
The human mind is capable of juggling only few task at once, and capable of remembering up to about 10 things at once.
If our App requires the user to simultaneously interact with many different events, people, concepts, or ideas cognitive Load Balancing prescribes techniques for how long you might best display only fragments of the whole information set to a user at once and effectively switch between those fragments. Limiting how much mental load the user must do at any time can improve their quality of experience and increase their ability to properly perform the target action we need them to do.
Conclusion
Here we are in the present movement — Today we seek not a technology of the body but a technology of behaviour. BD is a way in which we can rigorously, empirically way to understand — and intervene — in how we think, feel and behave. To design ourselves. To reshape our behaviour to match our aspirations. To turn the computing and scientific advances we’ve made in the past few decades inward to explore and reshape ourselves. BD is a technology of behaviour we see it drain our attention.
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