Catalog
concept#Product#Analytics#Data

Behavioral Science

Evidence-based study of human decision-making used to design interventions, experiments and choice architectures for better products and policies.

Behavioral Science studies how people make decisions and how cognitive biases, social influences, and incentives shape behaviour.
Established
Medium

Classification

  • Medium
  • Business
  • Design
  • Intermediate

Technical context

Analytics platforms (e.g., Google Analytics, Mixpanel)A/B test frameworks and experiment infrastructureProduct and CRM systems to activate interventions

Principles & goals

Evidence over intuition: base decisions on data and experimentsContext sensitivity: interventions depend on context and must be validatedEthics and transparency: respect autonomy and apply informed practices
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Unethical manipulation or violation of autonomy
  • Misinterpretation of causal relationships
  • Focus on short-term metrics over long-term impact
  • Start with clear hypotheses and measurable KPIs
  • Combine qualitative and quantitative methods
  • Observe ethical guidelines and user transparency

I/O & resources

  • User research data (qualitative)
  • Quantitative behavioral metrics
  • Hypotheses and theories from psychology/social science
  • Designs for interventions and experiments
  • Evaluation reports and decision recommendations
  • Implementation and monitoring plans

Description

Behavioral Science studies how people make decisions and how cognitive biases, social influences, and incentives shape behaviour. It provides evidence-based methods to design interventions, experiments and choice architectures that improve product outcomes and policy decisions. Applicable across product design, analytics and organizational change.

✔

  • Better user-centered decisions through empirical evidence
  • Greater efficiency in product optimization via targeted interventions
  • Improved policy and business decisions by understanding biases

✖

  • Context dependence limits generalizability
  • Measuring small effects can be difficult
  • Requires interdisciplinary expertise and data access

  • Conversion rate

    Measures the share of users performing desired actions.

  • Retention

    Tracks how many users remain active over time.

  • Effect size (Cohen's d)

    Quantifies the practical significance of an observed effect.

E-commerce A/B test with default option

A shop used default options to reduce decision friction; conversions increased measurably.

Public information campaign using social proof

A campaign displayed peer behaviour and significantly raised program participation.

Product optimization via nudging in onboarding

Onboarding changes based on behavioral data improved retention rates.

1

Define problem and formulate hypotheses

2

Collect data and measure baseline

3

Iterative tests (A/B), evaluation and scaling of successful approaches

⚠️ Technical debt & bottlenecks

  • Unstructured data storage hinders reproducibility
  • Lack of testing infrastructure for robust A/B analyses
  • Outdated measurement and tracking implementations
Data qualityInterdisciplinary collaborationMeasurability of long-term effects
  • Misleading defaults that coerce users into undesired actions
  • Selective publication of only positive test results
  • Unauthorised data use for behaviour steering
  • Overestimating study transferability
  • Lack of control for external confounders
  • Insufficient stakeholder involvement before implementation
Knowledge of behavioral psychology and experimental designData analysis and statisticsProduct and design literacy for implementation
Variability of human behaviour and context dependenceAvailability and quality of behavioral dataEthical and legal constraints
  • • Privacy and consent requirements
  • • Limited scalability of highly individualized interventions
  • • Time and resource demands for robust evaluations