Failure Analysis
Evidence note Examples below are illustrative unless a row in the Evidence Ledger is linked next to the claim. We report target behaviors in percentage points (Δ‑B), with denominators and windows specified.
Learning from what doesn’t work is as important as studying successes. This section provides deep analysis of common behavioral intervention failures, their root causes, and actionable lessons to improve your success rate.
Why Failure Analysis is Critical
In behavioral strategy, failures are particularly instructive because they reveal the hidden assumptions and contextual factors that determine success. Unlike product failures that might be obvious (it doesn’t work), behavioral failures are often subtle - the intervention works, just not the way intended, or works initially but fails to sustain.
The Cost of Behavioral Failures
Many widely cited program categories underperform when measured on target behaviors rather than participation or logins. We document effect sizes and windows in the Evidence Ledger and link each claim to its sources.
The Behavioral Failure Taxonomy
1. Conceptual Failures (Root Cause: Wrong Problem/Behavior)
These failures occur when we solve the wrong problem or target the wrong behavior.
Common Patterns:
- Addressing symptoms instead of root behaviors
- Assuming rational decision-making
- Misunderstanding user motivations
- Solving for edge cases instead of core behaviors
Example: A financial app that gamifies saving (symptom) when users’ real problem is not having money left to save (root cause).
Prevention: Always validate Problem Market Fit before designing interventions.
2. Design Failures (Root Cause: Poor Intervention Design)
The right behavior is targeted, but the intervention doesn’t enable it effectively.
Common Patterns:
- Too much friction in desired behavior
- Rewards misaligned with motivations
- Triggers absent or mistimed
- Cognitive overload in behavior path
Example: A health app requiring 15 minutes of daily data entry - correct behavior (tracking), wrong design (too burdensome).
Prevention: Test interventions with <5 minute time-to-behavior metric.
3. Implementation Failures (Root Cause: Execution Gaps)
Good behavioral design poorly executed.
Common Patterns:
- Technical bugs interrupt behavior flow
- Inconsistent experience across touchpoints
- Poor onboarding leaves users confused
- Measurement systems not capturing behaviors
Example: Exercise app with perfect behavioral design but crashes during workouts, breaking habit formation.
Prevention: Behavioral QA testing focusing on behavior completion, not just functionality.
4. Context Failures (Root Cause: Environmental Mismatch)
Interventions that work in controlled settings but fail in real-world contexts.
Common Patterns:
- Lab testing ≠ real world performance
- Cultural factors not considered
- Physical environment prevents behavior
- Social pressures override intervention
Example: Workplace wellness program designed for office workers fails for remote employees due to different environmental cues.
Prevention: Always test in natural user environments, not controlled settings.
5. Scaling Failures (Root Cause: Emergent Complexity)
What works for 100 users fails for 10,000.
Common Patterns:
- Personal touch doesn’t scale
- Early adopters ≠ mainstream users
- Network effects work in reverse
- Support costs explode
Example: Peer coaching app that works brilliantly with motivated early users but fails when mainstream users don’t want to coach others.
Prevention: Test with representative user segments, not just enthusiasts.
Deep Dive: Why Behavioral Interventions Fail
The Intention-Action Gap
The biggest predictor of behavioral intervention failure is relying on intention rather than enabling action. Research shows:
- Only 20-30% of intentions translate to behavior
- This gap widens over time (decay effect)
- Environmental factors override intentions
Key Insight: Design for action enablement, not intention creation.
The Complexity Trap
As behavioral interventions evolve, they often accumulate features that increase friction:
- Version 1.0: Simple behavior, clear trigger, immediate reward
- Version 2.0: Multiple behaviors, complex rules, delayed rewards
- Version 3.0: Behavioral overload, user abandonment
Key Insight: Resist feature creep that adds behavioral complexity.
The Motivation Misconception
Many failures stem from misunderstanding user motivation:
- Mistake: Assuming extrinsic rewards drive long-term behavior
- Reality: Intrinsic motivation sustains behavior
- Result: Initial spike followed by rapid decline
Key Insight: Align with existing motivations rather than trying to create new ones.
Case Studies in Failure
1. Corporate Wellness Programs (illustrative)
What Happens: Companies spend billions on wellness programs with minimal behavior change.
Why They Fail:
- One-size-fits-all approach ignores individual differences
- Focus on education rather than behavior enablement
- Lack of integration with daily workflows
- Metrics focus on participation, not behavior change
Lessons Learned:
- Segment users by behavioral readiness
- Embed wellness behaviors into existing routines
- Measure actual behavior change, not program enrollment
2. Digital Health Apps (illustrative)
What Happens: Health apps see massive downloads but minimal sustained use.
Why They Fail:
- Require new behaviors instead of enhancing existing ones
- High friction daily requirements
- Generic interventions ignore personal context
- No social or environmental support
Lessons Learned:
- Piggyback on existing behaviors
- Reduce to smallest viable behavior
- Personalize based on user context
- Build in environmental triggers
3. Financial Behavior Apps: The Mint Problem
What Happens: Budgeting apps get millions of users but don’t change spending behavior.
Why They Fail:
- Tracking ≠ behavior change
- Showing problems without enabling solutions
- Requiring sustained attention to mundane tasks
- Working against instant gratification bias
Lessons Learned:
- Enable behavior at point of decision
- Automate rather than requiring manual work
- Provide immediate feedback on actions
- Design for System 1 (automatic) thinking
Available Failure Analyses
📊 Gamification Failures
Why 80% of gamification initiatives fail and how to avoid the common pitfalls. Deep dive into the “PBL fallacy” (points, badges, leaderboards) and why they often backfire.
🧭 Google+ (Behavior Selection Failure)
Selected symmetric social networking behavior for a user base oriented to asymmetric discovery and subscriptions; coordination effects ignored.
🎬 Quibi (Behavior Selection Failure)
Selected passive, TV-style short-form consumption for a mobile context dominated by interactive, lean‑in behaviors.
More Analyses Coming:
- Nudge Limitations - When choice architecture isn’t enough
- Habit App Failures - Why habit trackers don’t create habits
- Enterprise Behavioral Failures - B2B specific failure patterns
- Cultural Mismatch Failures - When behavioral science ignores culture
The Failure Analysis Framework
Use this framework to analyze any behavioral intervention failure:
1. Failure Detection
- What behavior was supposed to change?
- What actually happened?
- When did the failure become apparent?
2. Root Cause Analysis
- Which category of failure? (Conceptual, Design, Implementation, Context, Scaling)
- What assumptions were violated?
- What wasn’t validated?
3. Lesson Extraction
- What can be learned?
- How could it have been prevented?
- What would you do differently?
4. Pattern Recognition
- Have you seen this failure before?
- Is this a systemic issue?
- What other interventions might fail similarly?
Common Anti-Patterns to Avoid
1. The Rational Actor Fallacy
Designing for how people “should” behave rather than how they do behave.
2. The More-is-Better Trap
Adding features/behaviors thinking it increases value when it actually increases friction.
3. The Early Success Bias
Mistaking early adopter enthusiasm for mainstream viability.
4. The Metric Gaming Problem
Optimizing for measurable behaviors that don’t connect to real outcomes.
5. The Context Blindness Error
Ignoring environmental and social factors that override individual interventions.
Learning from Failure: Best Practices
- Document Everything: Create failure post-mortems
- Share Openly: Normalize failure discussion
- Extract Patterns: Look for systematic issues
- Update Processes: Embed lessons into validation
- Measure Better: Track behavior quality, not just quantity
Contributing Your Failures
Have a behavioral intervention failure to share? We’d love to analyze it. Send details to jason@thebehavioralscientist.com with:
- Context and goals
- What was supposed to happen
- What actually happened
- Your hypothesis for why
- Lessons learned
The best way to advance Behavioral Strategy is learning collectively from what doesn’t work.
“Success is going from failure to failure without losing enthusiasm.” - Winston Churchill
In Behavioral Strategy, each failure brings us closer to understanding what actually drives human behavior.