Behavioural Research

What is Behavioural Research?

Behavioural Research in UX is the systematic study of what users actually do when interacting with products or services, as opposed to what they say they do. It focuses on observing and measuring users' actions, interactions, and performance to understand their natural behaviors in realistic contexts. This approach provides objective data about how people use interfaces in practice.

Behavioural research methods include usability testing, A/B testing, analytics analysis, eye-tracking studies, field studies, and contextual inquiry. These methods capture what users actually do rather than relying on self-reported data, which can be subject to various biases and inaccuracies. Behavioural data is particularly valuable because it reveals patterns that users themselves may not be aware of or able to articulate.

Why is Behavioural Research Important?

Behavioural Research is important because it provides objective evidence of how users actually interact with products, which often differs from how they report their behavior. This "say-do gap" exists because users may not accurately remember their actions, might want to please researchers, or may not be aware of their own habits and behaviors. Behavioural data helps bridge this gap by showing what really happens during user interactions.

This approach reveals usability issues, interaction patterns, and user journeys that might not be captured through attitudinal methods alone. It helps identify where users struggle, what features they actually use (versus what they say they value), and how they navigate through interfaces. Behavioural insights lead to more effective design decisions based on evidence rather than assumptions.

How to Conduct Behavioural Research?

To conduct effective behavioural research, choose appropriate methods based on your research questions (usability testing for interface issues, analytics for usage patterns, field studies for contextual insights), define clear metrics and behaviors to observe, create realistic scenarios that reflect actual use cases, and use tools that capture behaviour with minimal interference or observer effect.

Best practices include combining multiple behavioural methods for a more complete picture, triangulating behavioural data with attitudinal insights, observing users in their natural environments when possible, analyzing both quantitative metrics (time on task, success rates) and qualitative observations (hesitations, workarounds), and maintaining ethical standards by obtaining informed consent and protecting user privacy. Remember that the goal is to understand what users actually do, not what they think they do or what you want them to do.