Target User Definition

TLDR: Define target users from observed behavior and context, not demographics alone. Use the eight-component Behavioral State Model to organize evidence and diagnostic hypotheses, then verify those hypotheses against real behavior.

Overview #

Traditional user segmentation relies on demographics (age, income, location) or psychographics (values, interests). Behavioral Strategy adds behavioral profiling: mapping users’ actual behaviors, capabilities, and contexts to define meaningful segments and identify constraints worth testing.

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. The Behavioral State Model is a practitioner model, not a validated psychometric instrument or a universal prediction equation.

When personality is material, use fit-for-purpose validated measures, often from the Big Five tradition, and state what the instrument can and cannot support. Do not use popular typologies or BSM ratings as behavior forecasts.

Data hygiene #

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

Why Behavioral Profiling Matters #

Demographics describe attributes. Behavioral profiles describe what a segment has done, where it happens, and what may constrain a specified next behavior.

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: A personality-related concern should not override direct evidence that a segment can perform the behavior in a supportive environment. Treat traits as one input, not a fixed outcome rule.

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": "Frequent switching between attempted solutions",
        "key_indicators": ["high churn", "multiple accounts", "quick abandonment"]
    }
}

Step 2: BSM Component Assessment #

For each segment, evaluate all 8 components:

Component Assessment Framework #

Step 3: Create Behavioral Personas #

Unlike traditional personas, behavioral personas focus on observed action patterns and testable constraint hypotheses:

## Behavioral Persona: "The Capable but Unmotivated"

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

**BSM Profile**:
- Personality: Mixed evidence; no clear conflict
- Perception: Supportive; believes the behavior is useful
- Emotions: Unknown because they have not yet been observed in the decision context
- Abilities: Supportive; completed the task unaided
- Social Status: Mixed evidence; no clear status effect
- Motivations: Constraining hypothesis; little evidence of an active goal
- Social Environment: Supportive; peers approve
- Physical Environment: Supportive; tools are accessible

**Diagnostic Hypothesis**: Motivation may be the limiting factor; verify this against observation or a targeted test

**Intervention Strategy**: Don't treat this as a persuasion problem. Either choose a behavior that fits observed motives, tendencies, and preferences, or redesign the behavior so it requires less ongoing motivation (automation, defaults, environment change).

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

Evidence-Linked Profiles Over Time #

Behavioral states and contexts change. Maintain a dated profile rather than treating a persona as a permanent property of a person:

1. Record the specific target behavior, population, context, and observation window.
2. Separate raw observations from interpretations about BSM components.
3. Name the evidence supporting each diagnostic hypothesis and the evidence that would disconfirm it.
4. Identify one or two plausible bottlenecks for investigation; do not convert ordinal ratings into a behavior probability.
5. Update the profile after a context change, intervention, or meaningful shift in observed behavior.

Research Methods for User Definition #

Quantitative Methods #

1. Behavioral Analytics
   • Track actual behaviors, not claimed intentions
   • Measure frequency, duration, context
   • Identify patterns and segments
2. A/B Testing Component Impacts
   • Test interventions targeting specific components
   • Measure which components most influence behavior
   • Compare diagnostic hypotheses with observed outcomes
3. Longitudinal Tracking
   • Monitor how behavioral states evolve
   • Identify triggers for state changes
   • Refine context-specific hypotheses

Qualitative Methods #

1. Behavioral Interviews

   Not: "Would you use feature X?"
   But: "Walk me through the last time you tried to set up auto-pay"
   

2. Contextual Observation
   • Shadow users in natural environment
   • Note environmental barriers/enablers
   • Observe social influences
3. 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, demonstrates the required ability, and has little awareness of available solutions”

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 #

Diagnostic Calibration #

Track each diagnostic hypothesis, the evidence behind it, the intervention or observation used to test it, and what actually happened. Revise the profile when the evidence contradicts the hypothesis. Do not report a universal BSM accuracy target unless a defined study, sample, outcome, and validation method support it.

Component Importance Validation #

When feasible and ethical, test a clearly specified intervention aimed at one hypothesized constraint, measure the target behavior with a suitable comparison, and report uncertainty. A changed outcome can inform the diagnosis, but it does not by itself validate the BSM as a universal model or prove that the named component was the only causal pathway.

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:
      assessment: "supportive|mixed|constraining|unknown"
      evidence: "How measured"
      limitations: "What this evidence cannot establish"
    perception:
      assessment: "supportive|mixed|constraining|unknown"
      evidence: "Observed or reported beliefs"
      beliefs: ["key beliefs"]
    emotions:
      assessment: "supportive|mixed|constraining|unknown"
      evidence: "Observation window and method"
      dominant_emotions: ["primary feelings"]
      triggers: ["what sets them off"]
    abilities:
      assessment: "supportive|mixed|constraining|unknown"
      evidence: "Observed task performance"
      strengths: ["what they're good at"]
      gaps: ["what they struggle with"]
    social_status:
      assessment: "supportive|mixed|constraining|unknown"
      evidence: "Role and relationship evidence"
      role: "Their position"
    motivations:
      assessment: "supportive|mixed|constraining|unknown"
      evidence: "Observed commitment or stated goals"
      drivers: ["what moves them"]
    social_environment:
      assessment: "supportive|mixed|constraining|unknown"
      evidence: "Norms and social support"
      supporters: ["who helps"]
      detractors: ["who hinders"]
    physical_environment:
      assessment: "supportive|mixed|constraining|unknown"
      evidence: "Observed setting and resources"
      enablers: ["what helps"]
      barriers: ["what blocks"]
  
  behavioral_hypotheses:
    target_behavior_1:
      possible_constraints: ["Evidence-linked BSM components"]
      disconfirming_evidence: "What would show this diagnosis is wrong"
      next_test: "Observation or intervention used to test the hypothesis"

Frequently asked questions #

What is a target user in Behavioral Strategy? #

A target user is a defined segment described by what people do now, the contexts in which they act, and the constraints that may limit a specific target behavior.

Why not rely on demographics or psychographics? #

Demographics describe who someone is; they do not reliably predict what they will do. Behavioral Strategy segments by observable behavior patterns and context, then profiles the constraints that determine feasibility.

How do personality and psychometrics fit into target-user definition? #

They are treated as evidence about relatively enduring individual differences that can inform Dispositional Fit and preference for certain behaviors. Use fit-for-purpose validated instruments, such as measures from the Big Five tradition, and translate signals into testable behavior-selection hypotheses rather than deterministic forecasts.

What is the minimum-component rule? #

Treat the weakest apparent prerequisite as a bottleneck hypothesis and research priority. It does not calculate the probability that a person or segment will perform the behavior.

When should profiles be updated? #

Whenever context changes (policy, tooling, pricing, seasonality) or when behavior data shifts. Profiles should be versioned and evidence-linked, not treated as static personas.

Integration with Behavioral Strategy Process #

Target User Definition feeds into every stage:

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

Next Steps #

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

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