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Behavior Design Framework

Framework for designing products that reliably change behavior. Based on a fundamental truth: behavior is not about willpower or motivation—it is a design problem with a predictable equation.

Core Principle

The Fogg Behavior Model = B=MAP. Behavior happens when Motivation, Ability, and a Prompt converge at the same moment.

            HIGH ┃
                 ┃   ★ Behavior happens
                 ┃  (above the Action Line)
                 ┃
  Motivation     ┃━━━━━━━━━━━━━━━━━━━━━━━ ← Action Line
                 ┃
                 ┃   ✗ Behavior fails
                 ┃  (below the Action Line)
            LOW  ┃
                 ┗━━━━━━━━━━━━━━━━━━━━━━━━━
                 HARD                    EASY
                        Ability

The Action Line: When motivation and ability are sufficient, a prompt causes the behavior. Below the line, no prompt works. The line curves: high motivation compensates for low ability, and high ability compensates for low motivation. The reliable strategy is to make behaviors easier (move right), not to pump up motivation (move up).

Scoring

Goal: 10/10. When reviewing or creating product behavior design, rate them 0-10 based on adherence to the principles below. A 10/10 means full alignment with all guidelines; lower scores indicate gaps to address. Always provide the current score and specific improvements needed to reach 10/10.

The Three Elements

1. Motivation

Core concept: Motivation is the energy for action. It has three core motivators, each with two sides: Sensation (pleasure/pain), Anticipation (hope/fear), and Belonging (acceptance/rejection). Motivation is powerful but unreliable — it fluctuates like waves.

Why it works: Motivation explains why people want to act, but it is the least reliable element in behavior design. It spikes (New Year's resolutions, product launches) and crashes (day 3, week 2). Products that depend on high motivation fail when the wave recedes. The best designs work even when motivation is at a trough.

Key insights:

• Three core motivators: Sensation (pleasure/pain), Anticipation (hope/fear), Belonging (acceptance/rejection)
• Motivation comes in waves — it is not a stable resource you can count on
• Design for low-motivation moments, not peak motivation
• "Motivation is unreliable. Ability is not." — BJ Fogg
• Motivation-first strategies (inspiring videos, aspirational messaging) produce spikes, not sustained behavior
• Match the required motivation level to the behavior's difficulty — hard behaviors need high motivation

Product applications:

Copy patterns:

• "Takes 30 seconds" (reduces motivation needed by signaling ease)
• "Join 50,000 teams who..." (belonging motivator)
• "See your progress instantly" (anticipation/hope motivator)
• "Don't lose your 7-day streak" (anticipation/fear motivator)
• Avoid motivation-heavy copy for routine actions — save it for hard asks

Ethical boundary: Never manufacture false hope or exploit fear to inflate motivation. Motivation tactics should connect users to genuine outcomes, not create anxiety that drives compulsive usage.

See: references/motivation-waves.md for deep dive on the three motivators, motivation waves, and designing for troughs.

2. Ability

Core concept: Ability is the capacity to do the behavior. It is a function of the scarcest resource across six factors — the Ability Chain. If any single link is too weak (too expensive, too time-consuming, too confusing), the behavior breaks. Simplicity is not a single dimension — it is always relative to the person and context.

Why it works: Making behaviors easier is the most reliable strategy in behavior design. Unlike motivation, ability can be systematically engineered. Every field you remove, every step you eliminate, every default you set moves the behavior to the right on the Fogg Behavior Model, crossing the Action Line even at low motivation. The Ability Chain provides a diagnostic: find the weakest link and fix it.

Key insights:

• The Ability Chain has six factors: Time, Money, Physical Effort, Mental Effort, Social Deviance, Non-Routine
• Simplicity is a function of the scarcest resource — find the bottleneck
• "Simplicity changes behavior" — BJ Fogg
• A friction audit finds the weakest link in the Ability Chain for each key behavior
• Starter Steps: shrink the behavior to the tiniest version (2 minutes → 30 seconds → one field)
• Default settings are the most powerful ability tool — users rarely change defaults

Product applications:

Copy patterns:

• "One click to get started" (time + physical effort)
• "Free forever for small teams" (money)
• "No technical skills needed" (mental effort)
• "Used by teams at Google, Stripe, and Shopify" (social deviance — it's normal)
• "Works just like tools you already use" (non-routine)

Ethical boundary: Reducing friction should make genuinely valuable behaviors easier. Never reduce friction on harmful actions (e.g., making it too easy to overspend, over-share, or delete important data without confirmation).

See: references/ability-chain.md for the six factors in detail, friction audit templates, and simplification strategies.

3. Prompt

Core concept: The prompt is the call to action — the thing that says "do it now." Without a prompt, behavior doesn't happen regardless of motivation and ability. Three types: Person Prompts (internal reminders), Context Prompts (environmental cues), and Action Prompts (designed triggers from the product).

Why it works: Prompts are the most overlooked element. Many product teams assume that if motivation and ability are present, behavior will happen. It won't — not without a well-timed prompt. The key insight: prompts only work above the Action Line. Sending a push notification to someone who lacks ability or motivation is spam. The best prompts arrive when motivation and ability are already sufficient.

Key insights:

• Three prompt types: Person (internal thought), Context (environmental cue), Action (designed notification/CTA)
• Prompts only work when motivation and ability are already above the Action Line
• A prompt at the wrong moment is noise; a prompt at the right moment is magic
• Anchor moments: tie new behaviors to existing routines ("After I open Slack, I will...")
• The best Action Prompts feel like helpful reminders, not interruptions
• Prompt fatigue is real — every unnecessary prompt degrades the value of future prompts

Product applications:

Copy patterns:

• "Your weekly report is ready" (context prompt — tied to real event)
• "After you finish your coffee, take 30 seconds to..." (anchor prompt)
• "One thing left to complete your setup" (action prompt with progress)
• "Your team is waiting for your input" (social context prompt)
• Never: "We miss you!" (product need, not user need)

Ethical boundary: Respect prompt fatigue and user preferences. Every prompt should pass the test: "Would I appreciate receiving this right now?" Never use prompts to manipulate through anxiety or manufactured urgency.

See: references/prompt-design.md for prompt types, timing strategies, notification design, and anchor moments.

Tiny Habits Method

The Tiny Habits method is the practical application of B=MAP: make behaviors so small they require almost no motivation, anchor them to existing routines, and celebrate immediately.

The Recipe

After I [ANCHOR MOMENT], I will [TINY BEHAVIOR], then I [CELEBRATION].

Anchor Moment: An existing routine that reliably happens (opening an app, finishing a meeting, morning coffee).

Tiny Behavior: The smallest version of the target behavior — so small it's almost impossible to skip. Not "write a report" but "open the report template." Not "review analytics" but "glance at one metric."

Celebration: An immediate positive emotion after doing the behavior. Celebration wires the habit. Without it, repetition alone isn't enough — you need the feeling of success.

Starter Steps

Every target behavior has a Starter Step — the tiniest meaningful version:

Scaling Behaviors

Once the tiny behavior is wired, it naturally grows:

1. Start tiny → User opens the dashboard (Starter Step)
2. Grows naturally → User checks two or three metrics
3. Expands → User customizes the dashboard
4. Habit formed → User checks dashboard every morning automatically

The key: never force scaling. Let motivation and momentum drive expansion. The tiny version is not a failure — it is the foundation.

See: references/tiny-habits.md for the full Tiny Habits recipe, celebration techniques, and scaling patterns.

Behavior Design Process

Fogg's systematic process for creating lasting behavior change:

Step 1: Clarify the Aspiration

What outcome does the user want? Not the product's goal — the user's aspiration.

• "I want to stay on top of my team's progress" (not "increase DAU")
• "I want to feel confident about my finances" (not "drive feature adoption")

Step 2: Explore Behavior Options

List all possible behaviors that could achieve the aspiration. Be exhaustive — don't commit to one behavior yet.

Step 3: Match Behaviors

For each behavior, assess: Does the user have enough motivation? Is it easy enough? Use the Focus Mapping technique — plot behaviors on a 2×2 of impact vs. feasibility.

Step 4: Start Tiny

Take the best-matched behavior and shrink it to its Starter Step. Design the prompt. Add celebration.

Step 5: Optimize

Once the tiny behavior is wired, expand it. Fix bottlenecks using the Ability Chain. Refine prompts based on timing data.

The Action Line

The Action Line is the visual threshold in the Fogg Behavior Model. Above it, behaviors happen when prompted. Below it, they don't.

Moving Behaviors Above the Action Line

Two reliable strategies:

1. Increase Ability (move right)

• Remove steps, pre-fill fields, add defaults
• Use templates, wizards, progressive disclosure
• This is the most reliable approach

2. Find better Prompts (prompt at the right moment)

• Trigger when motivation is naturally higher
• Use anchor moments tied to existing routines
• Event-based prompts > time-based prompts

Unreliable strategy: Increase Motivation (move up)

• Motivational messaging produces spikes, not sustained change
• Use motivation boosters sparingly and strategically
• If you need motivation tactics, the behavior is probably too hard

Retention Diagnostics with B=MAP

Map B=MAP to product metrics:

Common Mistakes

Quick Diagnostic

Reference Files

• behavior-model.md: B=MAP deep dive, the Action Line, behavior types, failure diagnostics
• ability-chain.md: Six simplicity factors, friction audit templates, simplification strategies
• prompt-design.md: Three prompt types, timing strategies, notification design, anchor moments
• tiny-habits.md: Tiny Habits recipe, Starter Steps, celebration, scaling patterns
• motivation-waves.md: Three motivators, motivation waves, designing for troughs
• product-applications.md: B=MAP applied to SaaS, mobile, e-commerce, health, education
• case-studies.md: Instagram, Duolingo, Slack, Calm, Peloton through Fogg's lens

Further Reading

This skill is based on the behavior design research developed by BJ Fogg. For the complete methodology, research, and case studies:

• "Tiny Habits: The Small Changes That Change Everything" by BJ Fogg
• "Designing for Behavior Change: Applying Psychology and Behavioral Economics" by Stephen Wendel (companion: applying behavior science to product design)

About the Author

BJ Fogg, PhD is the founder of the Behavior Design Lab at Stanford University, where he has directed research on behavior change since 1998. He created the Fogg Behavior Model (B=MAT/B=MAP), which has become the foundational framework used by product designers, health researchers, and behavior change professionals worldwide. Fogg coined the term "behavior design" and has trained thousands of innovators in his methods, including the founders of Instagram (Mike Krieger was a student). His research on "captology" (computers as persuasive technology) created an entirely new academic discipline. Tiny Habits distills two decades of research into a practical system for behavior change, demonstrating that lasting change comes not from motivation or willpower but from designing behaviors to be tiny, anchored, and celebrated. Fogg's methods have been adopted by product teams at companies from Silicon Valley startups to Fortune 500 enterprises, and his academic work has been cited over 20,000 times.

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