Catalog
concept#Product#Delivery#Governance#Software Engineering

Behavior Change

Concepts and methods for systematically influencing human behavior in products, communication and organizations.

Behavior Change describes principles and methods to systematically understand and influence human behavior.
Established
Medium

Classification

  • Medium
  • Organizational
  • Design
  • Intermediate

Technical context

Analytics platforms (e.g. Google Analytics, Mixpanel)Experiment and feature-flag tools (e.g. Optimizely)User research tools (surveys, session replay)

Principles & goals

Hypothesis-driven approach: Formulate clear assumptions before interventions.Measurability: Define KPIs and behavior metrics in advance.Iterative testing: Small experiments instead of large theoretical changes.
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Unintended behavior shifts into other negative areas.
  • Loss of user trust due to overly aggressive nudges.
  • Lack of generalizability of experiment results.
  • Use small, well-defined experiments
  • Prioritize ethical transparency with users
  • Combine qualitative and quantitative insights

I/O & resources

  • Quantitative usage data (events, funnels)
  • Qualitative user feedback and interviews
  • Hypotheses and success criteria
  • Tested interventions and results
  • Recommendations for product changes
  • Monitoring and metric set

Description

Behavior Change describes principles and methods to systematically understand and influence human behavior. It combines psychology, behavioral economics and design to plan, measure and iteratively improve interventions. It informs product, communication and organizational choices and guides selection of metrics, hypotheses and experimental designs.

✔

  • Targeted increase of desired user actions and adoption.
  • Improved product decisions through empirical insights.
  • More effective communication and higher relevance for target groups.

✖

  • Context dependence: Intervention effects can vary widely.
  • Measurement issues: Causal effects are not always clearly demonstrable.
  • Ethics and acceptance: Manipulative measures may trigger rejection.

  • Conversion rate

    Share of users performing a desired action.

  • Retention

    Share of users who remain active over time.

  • Behavior frequency

    How often a target action occurs within a period.

EAST intervention for energy saving

Use of the EAST principle (Make it Easy, Attractive, Social, Timely) to reduce household energy consumption.

SaaS product onboarding experiment

A/B test with simplified checklist and social proof increased activation of new users.

Behavior workshop for safety culture

Workshops and visible reminders improved adherence to safety processes in a production unit.

1

Define problem and objectives

2

Form hypotheses and measurement plan

3

Experiment, evaluate and iterate

⚠️ Technical debt & bottlenecks

  • Missing event instrumentation for behavior measurement
  • Fragmented data sources hamper analysis
  • No versioning of experiment setups and hypotheses
Data availabilityCultural resistanceMeasurability
  • Aggressive dark patterns that boost KPIs short-term
  • Rewarding rather than changing behavior leads to dependency
  • Interventions without privacy review
  • Confusing correlation with causation
  • Measuring wrong metrics (vanity metrics)
  • Making large changes without stepwise tests
Foundations of behavioral scienceExperiment design and statisticsProduct and UX skills
Measurability of behaviorPrivacy and ethicsScalability of interventions
  • • Legal constraints (privacy, advertising)
  • • Limited resources for experiments
  • • Accessibility and inclusion