Quick Reference Guide
Use this detailed reference when you need a concept table, decision tree, or structured example while preparing a project record. The examples organize practitioner judgment; they are not validated scoring software or evidence that a project will succeed. Use the compact cheat sheet for meeting reminders, Quick Start for an introduction, and the methodology directory to find the full protocol for your next decision.
The Behavior Fit Assessment is a practitioner decision tool for comparing candidate behaviors across Dispositional Fit, Capability Fit, and Context Fit. It is not a validated measurement instrument. Treat the minimum dimension as a bottleneck and prioritization heuristic; it is not a deterministic probability of behavior.
A score of 6 out of 10 on each Behavior Fit Assessment dimension is a starting threshold that must be calibrated by domain, population, context, stakes, and observed behavior.
The Behavioral State Model is a practitioner diagnostic model with six Personal Components: Personality, Perception, Emotions, Abilities, Social Status/Situation, and Motivations. It also includes two Context Components: the Social Environment and Physical Environment. “Identity” is the historical technical alias for the Personal Components. Its BSM meaning is broader than self-concept or an aspirational identity. The components operate on different timescales. The Behavioral State Model is a practitioner model, not a validated psychometric instrument or a universal prediction equation.
Core Concept Decision Tree #
# Master Decision Tree for Behavioral Strategy Application
behavioral_strategy_decision_tree:
start: "What is your strategic challenge?"
new_initiative:
question: "Are you creating something new?"
if_yes:
next: "Have you validated the problem exists?"
if_validated:
action: "Proceed to behavior research"
framework: "Use full DRIVE process"
if_not_validated:
action: "Start with Problem Market Fit validation"
method: "Gather enough direct evidence to test problem-seeking behavior; document the sampling rationale"
existing_initiative:
question: "Is user adoption below expectations?"
if_yes:
diagnostic:
check_1: "Was Problem Market Fit validated?"
if_no: "Return to problem validation"
check_2: "Was Behavior Market Fit validated?"
if_no: "Identify why users aren't performing behaviors"
check_3: "Does solution enable validated behaviors?"
if_no: "Redesign to reduce behavioral friction"
if_no:
question: "Are you optimizing for growth?"
recommendation: "Focus on behavioral enhancement phase"
behavior_identification:
question: "How do I identify the right behaviors?"
process:
1: "List all behaviors that could solve the problem"
2: "Screen candidates with Behavior Fit Assessment (Dispositional/Capability/Context)"
3: "Use the minimum dimension as a prioritization heuristic and calibrate the starting threshold"
4: "Validate in realistic context (observation + prototype testing)"
5: "Document thresholds and proceed to solution integration"
Four-Fit Hierarchy Validation Guide #
# Sequential Validation Framework
# Derive decision thresholds from the domain, population, stakes, baseline,
# and observed behavior. The framework does not supply universal targets.
four_fit_validation:
problem_market_fit:
definition: "Users actively seek solutions to this problem"
validation_criteria:
- problem_evidence: "The problem is specific and consequential in context"
- solution_seeking: "Observed actions show active attempts to solve it"
- commitment: "People invest decision-relevant time, effort, money, or political capital"
- current_workarounds: "Existing attempts and alternatives are documented"
methods:
- user_interviews: "Sample and stop according to the research question and evidence saturation"
- search_analysis: "Growing query volume"
- competitor_growth: "Existing solutions gaining users"
decision_rule: "Pre-commit the evidence required for this initiative"
failure_action: "Pivot problem or audience"
behavior_market_fit:
definition: "Users can and will perform target behaviors"
validation_criteria:
- dispositional_fit: "Matches relatively enduring tendencies and preferences"
- capability_fit: "Users have the actual abilities, skills, and knowledge required"
- context_fit: "Context supports behavior where it occurs"
- frequency: "Can be performed regularly"
methods:
- observation: "Observe a justified sample in realistic contexts"
- prototype_testing: "Measure actual behavior"
- diary_studies: "Track behavior over time"
decision_rule: "Use a calibrated BFA screen, then require observed behavior"
failure_action: "Simplify or change behaviors"
solution_market_fit:
definition: "Solution enables target behaviors effectively"
validation_criteria:
- behavior_completion: "Completion meets the pre-committed domain target"
- time_to_behavior: "Time to first behavior meets the workflow-specific target"
- repeat_performance: "Repetition matches the value-delivery cadence"
- user_satisfaction: "Behaviors feel natural"
methods:
- usability_testing: "Behavior-focused"
- analytics: "Behavioral event tracking"
- cohort_analysis: "Retention by behavior"
decision_rule: "Set targets from baseline, value requirements, and stakes"
failure_action: "Iterate on friction points"
product_market_fit:
definition: "Sustained behavior change in market"
validation_criteria:
- market_adoption: "Adoption is sufficient for the stated outcome and population"
- behavior_retention: "Retention persists over a decision-relevant period"
- organic_growth: "Expansion mechanisms are measured rather than assumed"
- unit_economics: "Products require viable unit economics; for public programs, Program Market Fit requires sustainable operations and funding"
methods:
- market_metrics: "Growth analytics"
- behavioral_cohorts: "Long-term tracking"
- qualitative_research: "Case narratives"
decision_rule: "Require a pre-committed duration appropriate to the behavior"
failure_action: "Return to previous fit"
Behavioral State Model (BSM) Components #
# BSM Component Assessment Framework
class BehavioralStateAssessment:
"""
Organize evidence across the 8 BSM components and identify a research priority.
This illustration does not calculate a probability or confidence score.
"""
def __init__(self):
self.components = {
# Review each component as a distinct diagnostic prompt.
'personality': {
'description': 'Core traits and tendencies',
'assessment': 'Big 5 personality inventory',
'intervention': 'Design for trait preferences',
'scale': (0, 10)
},
'perception': {
'description': 'How user interprets world',
'assessment': 'Mental model mapping',
'intervention': 'Reframe understanding',
'scale': (0, 10)
},
'emotions': {
'description': 'Emotional patterns and triggers',
'assessment': 'Emotion diary study',
'intervention': 'Emotional design elements',
'scale': (0, 10)
},
'abilities': {
'description': 'Skills and capabilities',
'assessment': 'Capability audit',
'intervention': 'Training or simplification',
'scale': (0, 10)
},
'social_status': {
'description': 'Position in social hierarchy',
'assessment': 'Social network analysis',
'intervention': 'Status-appropriate messaging',
'scale': (0, 10)
},
'motivations': {
'description': 'Core drivers and goals',
'assessment': 'Motivation interview',
'intervention': 'Align with intrinsic motivators',
'scale': (0, 10)
},
# Social and physical environments complete the eight components.
'social_environment': {
'description': 'People and culture around user',
'assessment': 'Social context mapping',
'intervention': 'Peer influence design',
'scale': (0, 10)
},
'physical_environment': {
'description': 'Spaces and objects',
'assessment': 'Environmental audit',
'intervention': 'Context modification',
'scale': (0, 10)
}
}
def identify_research_priority(self, component_ratings, evidence_notes):
"""
Identify the lowest provisional rating as a bottleneck hypothesis.
Args:
component_ratings: Comparable ordinal ratings for one behavior,
population, context, and observation window
evidence_notes: Evidence and uncertainty behind each rating
Returns:
A research priority, not a behavior forecast
"""
if not component_ratings:
raise ValueError('component_ratings must not be empty')
lowest = min(component_ratings.items(), key=lambda item: item[1])
return {
'bottleneck_hypothesis': lowest[0],
'provisional_rating': lowest[1],
'supporting_evidence': evidence_notes.get(lowest[0], []),
'next_step': 'Test this hypothesis against observed behavior'
}
# Example usage
assessor = BehavioralStateAssessment()
user_scores = {
'personality': 7,
'perception': 6,
'emotions': 8,
'abilities': 4, # Limiting factor
'social_status': 7,
'motivations': 9,
'social_environment': 6,
'physical_environment': 7
}
evidence = {
'abilities': ['Several participants could not complete the task unaided']
}
result = assessor.identify_research_priority(user_scores, evidence)
print(f"Bottleneck hypothesis: {result['bottleneck_hypothesis']}")
print(f"Next step: {result['next_step']}")
Common Patterns and Anti-Patterns #
# Behavioral Strategy Patterns Reference
patterns:
successful_patterns:
validate_before_build:
when: "Always"
how: "Problem Market Fit → Behavior Market Fit → Solution Market Fit → Verify Product Market Fit"
outcome: "Reduces wasted effort by catching poor fit early"
behavior_first_design:
when: "Designing any feature"
how: "Map feature to specific validated behavior"
outcome: "Higher adoption through behavior alignment"
measure_behaviors_not_satisfaction:
when: "Setting KPIs"
how: "Track behavior completion, not NPS"
outcome: "Real impact visibility"
start_simple:
when: "Selecting target behaviors"
how: "Choose easiest high-impact behavior first"
outcome: "Faster initial wins"
anti_patterns:
assumption_driven_development:
symptom: "We think users will..."
consequence: "High failure rate from unvalidated assumptions"
fix: "Validate with behavioral research"
feature_factory:
symptom: "Building requested features"
consequence: "Features unused"
fix: "Validate behaviors, not features"
nudge_theater:
symptom: "Adding behavioral elements post-hoc"
consequence: "Minimal impact"
fix: "Integrate from inception"
one_size_fits_all:
symptom: "Same solution for all users"
consequence: "Low adoption from poor segment fit"
fix: "Segment by behavioral profiles"
DRIVE Framework Quick Implementation #
# DRIVE Framework Checklist
drive_implementation:
define_phase:
duration: "Set from the research question, access, and decision stakes"
deliverables:
- validated_problem: "Evidence users seek solutions"
- target_segments: "Specific user groups defined"
- success_metrics: "Behavioral KPIs identified"
key_activities:
- problem_interviews: "Use a justified sample and document the stopping rule"
- market_analysis: "Search trends, competitors"
- stakeholder_alignment: "Agreement on goals"
research_phase:
duration: "Set from the behaviors, contexts, and evidence needed"
deliverables:
- behavior_inventory: "All possible behaviors mapped"
- validated_behaviors: "Top 3 users will perform"
- barrier_analysis: "Why users don't act now"
key_activities:
- ethnographic_observation: "Use a justified sample across relevant contexts"
- behavior_testing: "Prototype key behaviors"
- diary_studies: "Track current behaviors"
integrate_phase:
duration: "Iterate until the solution meets its pre-committed behavior criteria"
deliverables:
- behavior_enabled_design: "Solution makes behaviors easy"
- friction_reduction: "Barriers removed"
- motivation_alignment: "Intrinsic drivers leveraged"
key_activities:
- iterative_prototyping: "Test with users weekly"
- behavior_mapping: "Feature to behavior matrix"
- usability_testing: "Focus on behavior completion"
verify_phase:
duration: "Ongoing"
deliverables:
- behavioral_analytics: "Real-time tracking"
- cohort_analysis: "Behavior retention curves"
- success_validation: "KPIs achieved"
key_activities:
- launch_mvp: "With behavior tracking"
- monitor_kpis: "Daily behavioral metrics"
- user_feedback: "Qualitative insights"
enhance_phase:
duration: "Continuous"
deliverables:
- optimization_roadmap: "Based on behavioral data"
- scaling_plan: "Expand successful behaviors"
- learning_documentation: "What worked/didn't"
key_activities:
- a_b_testing: "Behavior-focused experiments"
- segment_analysis: "Different user groups"
- iterative_improvement: "Weekly cycles"
Behavioral KPI Framework #
# Behavioral KPI Definition and Tracking
class BehavioralKPIFramework:
"""
Define and track behavioral KPIs for any initiative.
"""
def __init__(self, initiative_type):
self.initiative_type = initiative_type
self.kpi_templates = self.load_kpi_templates()
def load_kpi_templates(self):
return {
'adoption': {
'first_behavior_completion': {
'definition': 'Users completing target behavior once',
'calculation': 'completed_once / total_users',
'target_basis': 'Baseline, value requirement, and decision stakes',
'measurement_period': 'Set from the expected time to first value'
},
'behavior_activation_rate': {
'definition': 'Users who start behavior journey',
'calculation': 'started_behavior / exposed_users',
'target_basis': 'Baseline and exposure-to-action decision rule',
'measurement_period': 'Set from the workflow'
}
},
'engagement': {
'behavior_frequency': {
'definition': 'Average behaviors per active user',
'calculation': 'total_behaviors / active_users',
'target_basis': 'The frequency required to deliver the intended value',
'measurement_period': 'Set from the behavior cadence'
},
'behavior_streak': {
'definition': 'Consecutive days with behavior',
'calculation': 'median(user_streaks)',
'target_basis': 'The repetition pattern required for the outcome',
'measurement_period': 'Set from the behavior cadence'
}
},
'quality': {
'behavior_completion_quality': {
'definition': 'Completeness of behavior performance',
'calculation': 'quality_score / attempts',
'target_basis': 'The minimum quality required for the intended outcome',
'measurement_period': 'per_behavior'
},
'error_rate': {
'definition': 'Failed behavior attempts',
'calculation': 'errors / total_attempts',
'target_basis': 'Risk tolerance and the cost of an error',
'measurement_period': 'daily'
}
},
'retention': {
'behavior_retention_30d': {
'definition': 'Users still performing after 30 days',
'calculation': 'active_at_30d / cohort_size',
'target_basis': 'Baseline and the retention period required for value',
'measurement_period': 'Cohort at decision-relevant intervals'
},
'behavior_resurrection': {
'definition': 'Dormant users who return',
'calculation': 'returned_users / dormant_users',
'target_basis': 'Baseline and the cost and value of reactivation',
'measurement_period': 'Set from the normal return opportunity'
}
}
}
def select_kpis(self, stage, goals):
"""
Select appropriate KPIs based on initiative stage and goals.
Args:
stage: 'launch', 'growth', 'maturity'
goals: List of primary goals
Returns:
Recommended KPI set with project-specific target bases
"""
recommended_kpis = {}
if stage == 'launch':
# Focus on adoption and initial quality
recommended_kpis.update({
'primary': [
self.kpi_templates['adoption']['first_behavior_completion'],
self.kpi_templates['adoption']['behavior_activation_rate'],
self.kpi_templates['quality']['error_rate']
],
'secondary': [
self.kpi_templates['engagement']['behavior_frequency']
]
})
elif stage == 'growth':
# Focus on engagement and retention
recommended_kpis.update({
'primary': [
self.kpi_templates['engagement']['behavior_frequency'],
self.kpi_templates['engagement']['behavior_streak'],
self.kpi_templates['retention']['behavior_retention_30d']
],
'secondary': [
self.kpi_templates['quality']['behavior_completion_quality']
]
})
elif stage == 'maturity':
# Focus on optimization and resurrection
recommended_kpis.update({
'primary': [
self.kpi_templates['retention']['behavior_retention_30d'],
self.kpi_templates['retention']['behavior_resurrection'],
self.kpi_templates['quality']['behavior_completion_quality']
],
'secondary': [
self.kpi_templates['engagement']['behavior_streak']
]
})
return recommended_kpis
def create_dashboard_spec(self, selected_kpis):
"""
Generate dashboard specification for tracking.
"""
dashboard = {
'real_time_metrics': [],
'daily_metrics': [],
'weekly_metrics': [],
'cohort_metrics': []
}
for category in ['primary', 'secondary']:
for kpi in selected_kpis.get(category, []):
period = kpi['measurement_period']
metric_spec = {
'name': list(kpi.keys())[0],
'definition': kpi['definition'],
'calculation': kpi['calculation'],
'target_basis': kpi['target_basis'],
'visualization': self.recommend_visualization(kpi)
}
if period in ['per_behavior', '24 hours']:
dashboard['real_time_metrics'].append(metric_spec)
elif period == 'daily':
dashboard['daily_metrics'].append(metric_spec)
elif period == 'weekly':
dashboard['weekly_metrics'].append(metric_spec)
else:
dashboard['cohort_metrics'].append(metric_spec)
return dashboard
def recommend_visualization(self, kpi):
"""Recommend visualization type for KPI."""
kpi_name = list(kpi.keys())[0]
if 'rate' in kpi_name or 'retention' in kpi_name:
return 'line_chart_with_reference_range'
elif 'frequency' in kpi_name:
return 'bar_chart_with_distribution'
elif 'streak' in kpi_name:
return 'histogram'
elif 'quality' in kpi_name:
return 'gauge_chart'
else:
return 'time_series'
# Example usage
kpi_framework = BehavioralKPIFramework('mobile_app')
# Select KPIs for launch stage
launch_kpis = kpi_framework.select_kpis('launch', ['user_adoption', 'behavior_quality'])
# Create dashboard specification
dashboard_spec = kpi_framework.create_dashboard_spec(launch_kpis)
print("Recommended Primary KPIs:")
for kpi in launch_kpis['primary']:
print(f"- {list(kpi.keys())[0]}: {kpi['definition']}")
Quick Diagnosis Tool #
# Behavioral Strategy Problem Diagnosis
quick_diagnosis:
symptoms_to_causes:
low_adoption:
symptom: "Users sign up but don't engage"
likely_causes:
- "No Problem Market Fit - they don't need this"
- "Poor onboarding - first behavior too hard"
- "Motivation mismatch - external vs intrinsic"
diagnosis_steps:
1: "Sample non-engaged users using a documented rationale"
2: "Observe onboarding completion rates"
3: "Check time to first behavior"
high_churn:
symptom: "Users leave after initial use"
likely_causes:
- "No Behavior Market Fit - behaviors unsustainable"
- "Value not realized - outcomes unclear"
- "Repetition failed - no reliable cues/triggers"
diagnosis_steps:
1: "Analyze behavior patterns before churn"
2: "Interview churned users"
3: "Compare retained vs churned behaviors"
feature_requests_but_low_usage:
symptom: "Users request features they don't use"
likely_causes:
- "Saying vs doing gap - aspirational requests"
- "Implementation doesn't enable behavior"
- "Context doesn't support usage"
diagnosis_steps:
1: "Map features to actual behaviors"
2: "Observe feature usage in context"
3: "Validate behavior feasibility"
plateaued_growth:
symptom: "Growth stalls after initial success"
likely_causes:
- "Exhausted early adopter segment"
- "Behaviors don't scale to mainstream"
- "Missing network effects"
diagnosis_steps:
1: "Segment analysis of users vs non-users"
2: "Identify behavioral barriers for next segment"
3: "Validate new behaviors for growth"
Implementation Readiness Checklist #
# Are You Ready for Behavioral Strategy?
readiness_assessment:
organizational_readiness:
leadership_buy_in:
indicator: "Executives understand behavior drives outcomes"
assessment: "Can they explain the four-fit hierarchy?"
not_ready_if: "Still focused on features over behaviors"
research_capability:
indicator: "Team can conduct behavioral research"
assessment: "Have they done ethnographic observation?"
not_ready_if: "Only do surveys and focus groups"
measurement_infrastructure:
indicator: "Can track behavioral events"
assessment: "Do you have behavior-level analytics?"
not_ready_if: "Only track page views and clicks"
iteration_velocity:
indicator: "Can test and iterate weekly"
assessment: "How fast can you deploy behavior tests?"
not_ready_if: "Monthly or quarterly release cycles"
project_readiness:
problem_clarity:
indicator: "Problem is specific and measurable"
assessment: "Can you describe problem in one sentence?"
not_ready_if: "Problem is vague or too broad"
user_access:
indicator: "Can recruit and observe target users"
assessment: "Can the team recruit a justified sample across relevant segments and contexts?"
not_ready_if: "No direct user access"
timeline_flexibility:
indicator: "Time for proper validation"
assessment: "Is there enough time to gather the evidence required by the decision?"
not_ready_if: "The delivery date prevents any realistic validation"
success_definition:
indicator: "Success defined behaviorally"
assessment: "What behaviors indicate success?"
not_ready_if: "Success is adoption or satisfaction"
scoring:
all_ready: "Proceed with full Behavioral Strategy"
mostly_ready: "Address gaps while starting"
half_ready: "Build capabilities first"
not_ready: "Focus on prerequisites"
Common Questions Quick Answers #
# Rapid-Fire Q&A for Common Scenarios
quick_qa:
"How many users for Problem Market Fit?":
answer: "There is no universal sample size"
detail: "Choose and document a sampling and stopping rule based on the research question, segment diversity, stakes, and evidence saturation"
"What if users say they want it but won't do it?":
answer: "Classic say-do gap. Observe actual behavior."
detail: "What people say ≠ what they do. Trust behavior."
"How long should validation take?":
answer: "Long enough to meet the pre-committed evidence standard for each fit"
detail: "Set timing from access, behavior cadence, risk, and the cost of a wrong decision"
"What's the minimum viable behavior?":
answer: "Smallest behavior that delivers core value"
detail: "Use a workflow-specific time target rather than a universal cutoff"
"Should we A/B test behaviors?":
answer: "Yes, but test behavior variations, not colors"
detail: "Test different paths to same outcome"
"How do we scale behavioral interventions?":
answer: "Expand in stages and revalidate when the population, context, or operating system changes"
detail: "Set each stage's evidence requirement before expansion"
"What if stakeholders want to skip validation?":
answer: "Show cost of failed initiatives without BS"
detail: "Frame as risk mitigation, not delay"
"Can we parallelize the four fits?":
answer: "No. Each depends on the previous."
detail: "Parallel work = wasted work"
"How do we measure behavior quality?":
answer: "Completion + accuracy + time + repetition"
detail: "Quality beats quantity for sustainability"
"What's the #1 mistake in Behavioral Strategy?":
answer: "Skipping to solutions before validating behaviors"
detail: "Exciting to build, critical to validate first"
Tools and Templates Reference #
# Essential Tools for Each Phase
tools_by_phase:
problem_validation:
interview_guide:
purpose: "Uncover problem-seeking behavior"
key_questions:
- "Tell me about the last time you missed a bill payment"
- "What have you tried to solve this?"
- "How much time/money have you spent on solutions?"
- "What would change if this were solved?"
evidence_tracker:
columns: ["User", "Problem Description", "Current Solutions", "Seeking Evidence"]
decision_rule: "Use a justified sample and require observed solution-seeking evidence"
behavior_research:
observation_protocol:
what_to_observe:
- "Current behavior patterns"
- "Environmental constraints"
- "Social influences"
- "Friction points"
how_to_record: "Video, photos, journey maps"
behavior_fit_assessment:
dimensions: ["Dispositional Fit", "Capability Fit", "Context Fit"]
starting_threshold: "6/10 on each dimension, calibrated by domain, population, context, stakes, and observed behavior"
decision_rule: "Use the minimum dimension as a bottleneck and prioritization heuristic, then validate in context"
solution_design:
behavior_to_feature_map:
format: "Behavior → Enabling Features → Success Metrics"
example: "Daily logging → Quick entry + Reminders → Pre-committed completion target"
friction_audit:
categories: ["Cognitive", "Physical", "Emotional", "Social"]
measurement: "Time, steps, and effort per behavior"
implementation:
behavioral_analytics_plan:
events_to_track:
- "Behavior started"
- "Behavior completed"
- "Time to completion"
- "Error points"
- "Abandonment reasons"
dashboard_template:
real_time: "Current active users, behaviors/minute"
daily: "Completion rates, error rates, time trends"
weekly: "Retention curves, segment analysis"
monthly: "Cohort retention, behavior evolution"
Next Steps by Role #
# Role-Specific Implementation Paths
implementation_paths:
product_manager:
week_1: "Run problem validation interviews"
week_2: "Define behavioral success metrics"
week_3: "Create behavior-focused roadmap"
ongoing: "Track behavioral KPIs, not features shipped"
designer:
week_1: "Observe users in natural context"
week_2: "Map behaviors to interface elements"
week_3: "Prototype behavior-enabling flows"
ongoing: "Test designs for behavior completion"
engineer:
week_1: "Implement behavioral event tracking"
week_2: "Build behavior analytics dashboard"
week_3: "Create A/B testing framework"
ongoing: "Optimize for behavior performance"
executive:
week_1: "Align on behavioral success definition"
week_2: "Resource behavioral research"
week_3: "Review behavior-based KPIs"
ongoing: "Make decisions based on behavior data"
consultant:
week_1: "Audit current behavioral blindspots"
week_2: "Train team on BS methodology"
week_3: "Guide first validation cycle"
ongoing: "Build organizational capability"
This Quick Reference Guide is designed for rapid access to Behavioral Strategy concepts and methods. For detailed explanations, see the comprehensive guides for each topic.