Behavior Matching #

Behavior Matching starts from a practitioner hypothesis: candidate-behavior choice can shape the result. Rather than asking only “How can we make people do X?” organizations can also ask “What behavior Y may fit our target group while achieving the same outcome?”

This reframing changes what teams investigate in product design, marketing, and organizational change. A poor fit between the behavior, population, and context can constrain outcomes even when execution is strong.

The Match Not Hack Philosophy #

Some behavior-change efforts treat a predetermined behavior as fixed and rely on persuasion, incentives, or friction reduction to produce it. That approach can create problems such as:

• Resistance and psychological reactance
• Constant need for motivation and intervention

• Small, temporary effects at scale (often ~1-2 pp in at-scale nudge-unit RCTs) BS-0003

• User disengagement when external pressure stops

Behavior Matching takes the opposite approach. Instead of fighting against user psychology, it works with it. The goal is to find behaviors that users already want to perform, or would want to perform given minimal support, that also achieve organizational objectives.

This is not about manipulation or persuasion. It is about selection and alignment.

Durable behavior is disposition- and context-constrained #

Behavior Matching is intended to be useful when evaluating durable behaviors (the ones you want repeated over weeks and months). In Behavioral Strategy, the working assumption is that durable behaviors may be constrained by:

• Dispositional factors that are relatively enduring over the decision-relevant horizon (personality, recurring preferences, characteristic priorities, and typical responses) BS-0039

• Context realities that are stubbornly real (time, tools, social norms, physical environment)

If a behavior is a poor match on either axis, short-run compliance may decay when incentives, reminders, novelty, or pressure stop. Test this in the relevant population and context.

Self-concept and domain identity may provide adjacent evidence, but they do not define Dispositional Fit. See Dispositional Fit, Identity, and Durable Behavior.

One-off vs durable behaviors #

• One-off actions (a single conversion, a one-time sign-up, a default setting) can sometimes respond to prompts, incentives, and interface optimization.
• Durable behaviors (retention, adherence, routines) may benefit from comparing candidate behaviors and then designing systems that make repetition feasible. The selected behavior still requires real-world validation. See: Behavioral Strategy vs Habit Formation.

The Matching Framework #

Behavior Matching uses three sequential steps to structure the comparison. The resulting candidate must still be tested in a realistic context.

Step 1: Define the Desired Outcome #

Start with outcomes, not behaviors. What specific result do you need to achieve? Be precise about the outcome independent of how it gets achieved.

Common mistake: Teams often conflate outcomes with behaviors. “We need users to complete onboarding” is a behavior. “Users understand our core value proposition” is an outcome. The outcome can be achieved through many different behaviors.

Questions to clarify the outcome:

• What measurable change would indicate success?
• Why does this outcome matter for the user?
• Why does this outcome matter for the business?
• How would we know if we achieved this outcome through a completely different behavior?

Step 2: Explore Multiple Behavioral Alternatives #

Generate at least five candidate behaviors that could accomplish the defined outcome. This expansion phase prevents premature commitment to a single approach.

For each candidate behavior, describe:

• The specific actions involved
• The time and effort required
• The skills or resources needed
• The context where it would occur

Example for the outcome “Users understand our core value proposition”:

1. Read a product overview page
2. Watch a 90-second explainer video
3. Complete an interactive tutorial
4. Talk with a customer success representative
5. Use the product immediately with guided prompts
6. See a side-by-side comparison with alternatives they already use

Each behavior could achieve the same outcome but places different demands on users.

Step 3: Evaluate Using the Behavior Fit Assessment #

Score each candidate behavior using the Behavior Fit Assessment as a provisional structured comparison.

The Behavior Fit Assessment is a practitioner decision tool for comparing candidate behaviors across Dispositional Fit, Capability Fit, and Context Fit. Identity Fit is retained only as the legacy alternate name for Dispositional 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 key rule: Use the minimum rating to identify a candidate bottleneck, evidence gap, or next test. A rating never establishes viability by itself.

Selection criteria: Prioritize candidates with stronger minimum ratings and a clear path to the outcome, then validate the leading candidates through observed behavior in realistic contexts.

After this analysis, design interventions or products that enable the selected behavior. Not before.

Why Forcing Fails #

Organizations default to forcing for understandable reasons. They have already decided what behavior they want. They have built products around that behavior. They have invested in campaigns promoting that behavior. Admitting the behavior is wrong feels like admitting failure.

In practice, forcing tends to produce:

Resistance and Reactance: When people feel pressured to behave in certain ways, they often push back. Psychological reactance is the tendency to do the opposite of what we are told, especially when we feel our freedom is threatened.

Intervention Dependency: Poorly matched behaviors may depend on continuing reinforcement. When an incentive, reminder, or friction reduction is removed, performance can fall back toward baseline. Test whether support can be reduced without losing the behavior.

Small Effects: At scale, “nudge-first” tactics often produce small average effects (often ~1-2 pp in at-scale trials). BS-0003 Bias-corrected analyses report near-zero average effects across nudge meta-analyses. BS-0027

Temporary Change: A poorly matched behavior may not persist after an intervention ends. Measure persistence rather than assuming that an initial effect will last.

Why Matching Works #

Matching is intended to improve these conditions:

Alignment with Existing Dispositions: When a behavior’s recurring demands match the population’s tendencies and preferences, it requires less continuing self-regulation or external pressure.

Reinforcing Loops: When a well-matched behavior produces value for the user, that feedback may reduce the support needed over time. Test whether the behavior continues as reminders or incentives are reduced.

Higher Leverage: Because matching changes what you ask people to do, in addition to how you ask, upstream behavior selection often has more leverage than downstream optimization. In some cases it changes the adoption trajectory entirely (e.g., Burbn -> Instagram).

Persistence: A well-matched behavior may persist with less continuing support. Persistence must be measured after support is reduced, not inferred from a fit assessment.

How to Apply Behavior Matching #

Step-by-Step Process #

1. Start with user research, not product features.

Before defining what behavior you want, understand who your users are. What do they already do? What do they want to do? What comes naturally to them?

Methods:

• Behavioral observation (watch what users actually do, not what they say they do)
• Dispositional research (measure recurring preferences, characteristic responses, and behavior across representative situations)
• Self-concept interviews when domain identity or status meaning is a relevant secondary signal
• Context mapping (understand the environments where users operate)

2. Define outcomes in user-centric terms.

Translate business objectives into outcomes users would recognize and value. “Increase engagement” becomes “help users feel competent and connected.” “Drive adoption” becomes “enable users to accomplish their goals faster.”

3. Generate candidate behaviors through divergent thinking.

Push past the obvious first answer. Ask:

• How would users accomplish this outcome if our product did not exist?
• What behaviors do our most successful users already perform?
• What adjacent behaviors in other domains could transfer?
• What behaviors would feel like play rather than work?

4. Score candidates using the Behavior Fit Assessment.

For your target user population, evaluate each candidate behavior across all three dimensions:

Be honest about low ratings. A rating under the calibrated starting screen identifies an evidence gap or candidate bottleneck to investigate, not a deterministic verdict.

5. Select the strongest-supported behavior that achieves the outcome.

Do not choose a behavior only because it is easy to build or measure. Prefer the candidate with the strongest fit evidence and a feasible execution path, then test it against credible alternatives.

6. Design solutions that enable the selected behavior.

After selecting a candidate, design the product features, interfaces, and interventions that may enable it. Remove unnecessary friction, provide the required tools, and create supporting context.

7. Validate fit before scaling.

Test whether the behavior actually matches. Watch for signs of forcing:

• High drop-off despite good activation
• Constant need for reminders or re-engagement
• Low retention without incentives
• Users describing the behavior as obligation, not value

If you see these signs, return to step 3 and explore alternatives.

Deeper Diagnosis: When Matched Behaviors Fail #

Sometimes a behavior passes Behavior Fit Assessment screening but still doesn’t perform as expected. When this happens, use the full Behavioral State Model for granular diagnosis.

The BFA is informed by the BSM, but it is not a literal one-to-one condensation. For troubleshooting, examine the model’s two exact groups:

These components operate on mixed timescales. Perceptions, emotions, and active motivations may change quickly, but they remain person-side components. In the BFA, Capability Fit isolates actual ability and skills, while Context Fit isolates the external social and physical setting.

Identify which specific component is scoring low, then design targeted interventions to address it.

Examples #

Instagram vs. Burbn #

Burbn was a check-in app. Users were supposed to broadcast their location to friends.

Behavior Fit Assessment scores for check-ins (example):

• Dispositional Fit: 4/10: Repeated location broadcasting conflicts with privacy preferences and comfort with public visibility in much of the target population
• Capability Fit: 7/10: Technically easy, but requires remembering to act
• Context Fit: 4/10: Real-world settings provide little opportunity or support for check-ins

Illustrative comparison: Minimum rating 4. Candidate bottlenecks are Dispositional Fit and Context Fit; observe check-in behavior in realistic contexts before deciding.

Instagram pivoted to photo sharing.

Behavior Fit Assessment scores for photo sharing (example):

• Dispositional Fit: 8/10: Visual expression and selective social sharing match recurring preferences in the target segment
• Capability Fit: 9/10: Phone cameras are easy; filters solved the skill gap
• Context Fit: 8/10: Mobile context supports quick capture; social environment rewards sharing

Illustrative comparison: Minimum rating 8. This candidate has stronger provisional fit ratings; validate photo-sharing behavior in realistic contexts.

Recruiting: Matching vs. Mass Filtering #

Traditional recruiting behavior: review hundreds of resumes to find qualified candidates.

Behavior Fit Assessment scores for CV filtering (example):

• Dispositional Fit: 3/10: Sustained repetitive document review conflicts with the target team’s recurring preferences for interactive and judgment-rich work
• Capability Fit: 5/10: Hard to evaluate candidates from paper; high cognitive load
• Context Fit: 3/10: Fragmented schedules and weak workflow support make focused review difficult

Illustrative comparison: Minimum rating 3. Dispositional Fit and Context Fit are candidate bottlenecks that require evidence and validation.

Alternative behavior: connect directly with well-matched candidates through warm introductions and targeted outreach.

Behavior Fit Assessment scores for targeted connection (example):

• Dispositional Fit: 7/10: Relational and strategic work matches recurring preferences in the target team
• Capability Fit: 7/10: Requires basic outreach and conversational skill, but those skills are learnable
• Context Fit: 8/10: Existing relationships, tools, and hiring workflows support the behavior

Illustrative comparison: Minimum rating 7. Prioritize this candidate for observed validation rather than treating the rating as a viability decision.

Anti-Patterns: Common Matching Mistakes #

Premature Behavior Lock-In #

Mistake: Deciding on a behavior before exploring alternatives, then looking for ways to force it.

Example: “Users need to complete our 12-step onboarding” becomes the fixed requirement. All effort goes into making users complete those 12 steps rather than asking whether 12 steps is the right approach.

Fix: Always start with outcomes. Ask “What are we trying to achieve?” before “How do we get users to do X?”

Scoring Based on Ideal Users #

Mistake: Evaluating behaviors based on your most engaged users rather than your target population.

Example: Power users love the advanced dashboard. Scoring based on them suggests everyone will. But most users aren’t power users.

Fix: Score behaviors for your median target user, not your best users. Better yet, segment and score separately for different user types.

Ignoring Dispositional Fit #

Mistake: Focusing only on Capability Fit and Context Fit while ignoring whether the behavior matches users’ relatively enduring tendencies and preferences.

Example: Reducing friction to zero for a behavior that conflicts with the target population’s recurring preferences. Easier performance does not resolve that mismatch.

Fix: Evaluate Dispositional Fit using evidence about relatively enduring tendencies and preferences, then assess it alongside Capability Fit and Context Fit. If the behavior repeatedly demands tendencies or preferences the population does not show, friction reduction alone may not solve the mismatch.

Optimizing One Dimension at the Expense of Others #

Mistake: Improving one fit dimension while damaging another.

Example: Adding competitive gamification changes the social context but makes the behavior less attractive to a population with strong autonomy or low-competition preferences.

Fix: Evaluate interventions across all three dimensions. A gain in one area that creates a loss elsewhere often nets negative.

Confusing Initial Appeal with Durable Adoption #

Mistake: Selecting behaviors that seem appealing at first but do not sustain.

Example: A reward-based behavior that attracts users initially but loses appeal once the novelty wears off.

Fix: Score behaviors for sustained engagement beyond initial trial. Ask “Would users do this on day 100?” before asking “Would users try this on day 1?”

Key Takeaways #

1. Candidate-behavior choice can constrain change. Compare behaviors rather than treating the first proposed behavior as fixed.
2. Start with outcomes, not behaviors. Define what you want to achieve before deciding how to achieve it.
3. Generate multiple candidates. The first behavior proposed may not be the strongest match.
4. Use the Behavior Fit Assessment. Compare provisional Dispositional Fit, Capability Fit, and Context Fit ratings and record the evidence behind them.
5. The minimum rating identifies a candidate bottleneck. An illustrative 9/9/4 comparison makes the 4 a validation priority, not a probability or verdict.
6. Treat poor fit as a material risk. Execution alone may not resolve a mismatch between the behavior, population, and context.
7. Validate before scaling. Dependence on continuing pressure may indicate a matching problem, an enablement problem, or both.
8. When in doubt, return to evidence. Revisit user research and observed behavior in the relevant context.

Further Reading:

• Behavior Fit Assessment: The rapid evaluation tool for behavior selection
• Behavioral State Model: The full 8-component diagnostic framework