Target User Definition

TLDR: Target user definition in Behavioral Strategy goes beyond demographics to create behavioral profiles using the 8-component Behavioral State Model, enabling precise behavior prediction and intervention design.

Overview

Traditional user segmentation relies on demographics (age, income, location) or psychographics (values, interests). Behavioral Strategy requires deeper understanding through behavioral profiling - mapping users’ actual behaviors, capabilities, and contexts to predict and influence future actions.

We use the Big Five as the only personality framework with broad empirical support. Popular typologies are not used for selection or prediction. Where personality matters, we translate validated trait signals into BSM component scores.

Data hygiene

Store raw behavioral data separately from interpreted BSM profiles.
Version profiles and record evidence for each component score.
Re‑score profiles after major context shifts.

Why Behavioral Profiling Matters

Demographics tell you who someone is. Behavioral profiles tell you what they’ll actually do.

Example: Two 35-year-old professionals with similar incomes may have completely different behavioral profiles:

User A: High ability (8), low motivation (3), supportive environment (8)
User B: Low ability (4), high motivation (9), restrictive environment (3)

These users require completely different interventions despite demographic similarity.

Example: Low Conscientiousness with high Motivation may still produce target behavior if Ability and Physical Environment are ≥ 7 and the path is scaffolded.

The BSM Profiling Method

Step 1: Initial Segmentation

Start with behavioral patterns, not demographics:

# Behavioral Segmentation Example
behavioral_segments = {
    "active_seekers": {
        "defining_behavior": "Actively searching for solutions",
        "frequency": "Daily research/exploration",
        "key_indicators": ["search queries", "comparison shopping", "forum participation"]
    },
    "passive_sufferers": {
        "defining_behavior": "Experiencing problem but not seeking",
        "frequency": "Problem occurs but no action taken",
        "key_indicators": ["complaints without action", "workarounds", "acceptance"]
    },
    "solution_jumpers": {
        "defining_behavior": "Trying multiple solutions rapidly",
        "frequency": "New solution every 1-2 weeks",
        "key_indicators": ["high churn", "multiple accounts", "quick abandonment"]
    }
}

Step 2: BSM Component Assessment

For each segment, evaluate all 8 components:

Component Assessment Framework

Component	Assessment Method	Data Sources	Scoring Criteria
Personality	Behavioral consistency analysis	Past choices, preferences	0-10 based on trait alignment
Perception	Belief mapping interviews	Surveys, interviews	0-10 based on accuracy/positivity
Emotions	Emotional journey mapping	Observation, self-report	0-10 based on emotional readiness
Abilities	Skill testing/observation	Task completion, errors	0-10 based on demonstrated capability
Social Status	Network analysis	Social platforms, roles	0-10 based on influence/position
Motivations	Goal hierarchy elicitation	Interviews, behavior tracking	0-10 based on drive strength
Social Environment	Context mapping	Peer behavior, norms	0-10 based on social support
Physical Environment	Environmental audit	Location data, resources	0-10 based on enablement

Step 3: Create Behavioral Personas

Unlike traditional personas, behavioral personas focus on action likelihood:

## Behavioral Persona: "The Capable but Unmotivated"

**Behavioral Summary**: Has all necessary skills and resources but lacks drive

**BSM Profile**:
- Personality: 6 (Neutral fit with behavior)
- Perception: 7 (Believes it's valuable)
- Emotions: 5 (Indifferent)
- Abilities: 9 (Highly capable)
- Social Status: 6 (No status impact)
- Motivations: 2 (Very low drive) ⚠️ LIMITING FACTOR
- Social Environment: 7 (Peers support)
- Physical Environment: 8 (Easy access)

**Behavioral Prediction**: Will NOT perform behavior due to motivation barrier

**Intervention Strategy**: Focus entirely on motivation - rewards, gamification, social competition

**Anti-Patterns**: Don't waste resources on training (ability already high) or access (environment supportive)

Dynamic User Modeling

Users’ behavioral states change over time. Implement dynamic tracking:

class DynamicUserProfile:
    def __init__(self, user_id):
        self.user_id = user_id
        self.state_history = []
        self.current_state = self.assess_current_state()
    
    def assess_current_state(self):
        """Assess all 8 BSM components"""
        return {
            'timestamp': datetime.now(),
            'components': {
                'personality': self.assess_personality(),
                'perception': self.assess_perception(),
                'emotions': self.assess_emotions(),
                'abilities': self.assess_abilities(),
                'social_status': self.assess_social_status(),
                'motivations': self.assess_motivations(),
                'social_environment': self.assess_social_environment(),
                'physical_environment': self.assess_physical_environment()
            },
            'limiting_factor': self.identify_minimum_component()
        }
    
    def predict_behavior(self, target_behavior):
        """Predict likelihood of specific behavior"""
        min_component = min(self.current_state['components'].values())
        
        if min_component < 3:
            return {
                'likelihood': 0.05,
                'blocker': self.current_state['limiting_factor'],
                'recommendation': f"Address {self.current_state['limiting_factor']} first"
            }
        
        # Calculate weighted likelihood
        weights = self.get_behavior_specific_weights(target_behavior)
        likelihood = self.calculate_weighted_score(weights)
        
        return {
            'likelihood': likelihood,
            'confidence': self.calculate_confidence(),
            'interventions': self.recommend_interventions()
        }

Research Methods for User Definition

Quantitative Methods

Behavioral Analytics
- Track actual behaviors, not claimed intentions
- Measure frequency, duration, context
- Identify patterns and segments
A/B Testing Component Impacts
- Test interventions targeting specific components
- Measure which components most influence behavior
- Validate BSM scores against outcomes
Longitudinal Tracking
- Monitor how behavioral states evolve
- Identify triggers for state changes
- Build predictive models

Qualitative Methods

Behavioral Interviews

Not: "Would you use feature X?"
But: "Walk me through the last time you tried to [behavior]"

Contextual Observation
- Shadow users in natural environment
- Note environmental barriers/enablers
- Observe social influences
Component Deep Dives
- Dedicated sessions per BSM component
- Use projective techniques
- Uncover hidden barriers

Common Mistakes in User Definition

Mistake 1: Demographic Determinism

Wrong: “Our target is women 25-34 with college degrees” Right: “Our target exhibits daily problem-seeking behavior with high ability (7+) but low solution awareness (<4)”

Mistake 2: Static Profiling

Wrong: “User type A always behaves like X” Right: “Users in behavioral state A tend toward X, but states change based on context”

Mistake 3: Average User Fallacy

Wrong: “Our average user scores 6 across all components” Right: “We have three distinct segments with different limiting factors”

Validation Techniques

Behavioral Prediction Accuracy

Track prediction vs actual behavior:

Accuracy = (Correct Predictions / Total Predictions) × 100
Target: >80% accuracy for high-confidence predictions

Component Importance Validation

Test which components matter most:

def validate_component_importance(user_segment, behavior):
    results = {}
    
    for component in BSM_COMPONENTS:
        # Run intervention targeting only this component
        intervention_results = run_targeted_intervention(
            segment=user_segment,
            component=component,
            behavior=behavior
        )
        
        results[component] = {
            'impact': intervention_results['behavior_change'],
            'cost_effectiveness': intervention_results['roi'],
            'significance': intervention_results['p_value']
        }
    
    return rank_by_impact(results)

Tools and Templates

User Profile Template

user_segment:
  name: "Segment Name"
  size: "Estimated population"
  defining_behaviors:
    - behavior: "Primary behavior pattern"
      frequency: "How often"
      context: "When/where"
  
  bsm_profile:
    personality:
      score: 0-10
      evidence: "How measured"
      intervention_potential: "low|medium|high"
    perception:
      score: 0-10
      beliefs: ["key beliefs"]
      misconceptions: ["what they get wrong"]
    emotions:
      score: 0-10
      dominant_emotions: ["primary feelings"]
      triggers: ["what sets them off"]
    abilities:
      score: 0-10
      strengths: ["what they're good at"]
      gaps: ["what they struggle with"]
    social_status:
      score: 0-10
      role: "Their position"
      influence: "Their reach"
    motivations:
      score: 0-10
      drivers: ["what moves them"]
      rewards_valued: ["what they want"]
    social_environment:
      score: 0-10
      supporters: ["who helps"]
      detractors: ["who hinders"]
    physical_environment:
      score: 0-10
      enablers: ["what helps"]
      barriers: ["what blocks"]
  
  behavioral_predictions:
    target_behavior_1:
      likelihood: 0-1
      limiting_factors: ["components <6"]
      intervention_focus: "Primary component to address"

Integration with Behavioral Strategy Process

Target User Definition feeds into every stage:

Define Phase: Identify user segments experiencing the problem
Research Phase: Deep dive into behavioral patterns
Integrate Phase: Design for specific user states
Verify Phase: Track user response by segment
Enhance Phase: Refine profiles based on data

Next Steps

Download the User Profile Template
Learn about Behavioral Research Methods
Explore Behavior Ranking for your defined users
See Case Studies of successful user definition

Related Concepts: Behavioral State Model

Behavioral Research

DRIVE Framework

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