🎯

loss-aversion-psychology

🎯Skill

from flpbalada/my-opencode-config

What it does

Helps product designers strategically frame messaging and features to leverage psychological loss aversion, driving user behavior through potential loss framing.

📦

Part of

flpbalada/my-opencode-config(40 items)

loss-aversion-psychology

Installation

📋 No install commands found in docs. Showing default command. Check GitHub for actual instructions.

Quick InstallInstall with npx

npx skills add flpbalada/my-opencode-config --skill loss-aversion-psychology

Need more details? View full documentation on GitHub →

1Installs

AddedFeb 4, 2026

View on GitHub Back to Skills

Skill Details

SKILL.md

Overview

# Loss Aversion Psychology - Losses Loom Larger Than Gains

Loss Aversion is a cognitive bias discovered by Daniel Kahneman and Amos Tversky

showing that people feel losses approximately twice as strongly as equivalent

gains. This asymmetry profoundly influences decision-making and behavior.

When to Use This Skill

Designing retention and anti-churn features
Crafting pricing and upgrade messaging
Creating urgency in conversion funnels
Building streak and progress features
Writing copy for landing pages
Framing feature benefits

Core Concepts

The 2:1 Ratio

```

Psychological Impact

| Gains

| /

+--------|----/---------> Value

| /

Loss|/

| The loss curve is ~2x steeper

```

A $100 loss feels as bad as a $200 gain feels good.

Prospect Theory Framework

| Concept | Description | Example |

| -------------------- | -------------------------------- | ---------------------------- |

| Reference Point | Current state as baseline | "You currently have X" |

| Loss Frame | Emphasis on what could be lost | "Don't lose your progress" |

| Gain Frame | Emphasis on what could be gained | "Get 50% more" |

| Endowment Effect | Valuing owned things higher | Free trial creates ownership |

When Loss Framing Works Best

| Situation | Loss Frame Effective? |

| ------------------------- | --------------------- |

| High stakes decisions | Yes |

| Preventing bad outcomes | Yes |

| Risk-averse audiences | Yes |

| Building habits | Yes |

| Low-involvement decisions | Less effective |

| Exploratory behavior | Less effective |

Analysis Framework

Step 1: Identify Loss Opportunities

Map user journey for potential loss frames:

| Stage | What User Has | Potential Loss |

| ------- | ------------------ | --------------- |

| Trial | Access to features | Losing access |

| Active | Progress/data | Losing progress |

| At-risk | Streak/status | Breaking streak |

| Churned | History/investment | Losing history |

Step 2: Choose Frame Appropriately

```

Decision: Frame as loss or gain?

Consider:

├── User relationship stage

│ └── New users: Gains more welcoming

│ └── Existing users: Losses more motivating

├── Action reversibility

│ └── Reversible: Lighter touch OK

│ └── Irreversible: Loss frame powerful

└── Ethical considerations

└── Does this genuinely help the user?

```

Step 3: Implement Ethically

| Approach | Ethical | Manipulative |

| ------------------------ | ---------------- | ------------------ |

| "Your streak will reset" | Honest reminder | Manufactured guilt |

| "Unused credits expire" | Clear policy | Hidden deadline |

| "Limited time offer" | Genuine scarcity | Fake urgency |

Output Template

```markdown

Loss Aversion Analysis

Feature/Message: [Name] Date: [Date]

Current Framing

As gain: [Current copy/design] User response: [Current metrics]

Loss Frame Opportunity

What user has: [Established value] Potential loss: [What could be lost]

Loss frame version: [Proposed copy/design]

Ethical Check

[ ] User genuinely benefits from taking action
[ ] Loss is real, not manufactured
[ ] Messaging is honest and transparent
[ ] Would we be comfortable if users knew the psychology?

Implementation Plan

| -------- | --------- | -------- | --------------- |

```

Real-World Examples

Example 1: Duolingo Streaks

Mechanism: Users build daily learning streaks Loss frame: "Don't lose

your 47-day streak!" Psychology:

Streak = accumulated investment (endowment)
Breaking it = losing days of effort
Effect: 2x stronger than "Build a 48-day streak!"

Example 2: LinkedIn Profile Completion

Gain frame: "Complete your profile to get more views" Loss frame:

"You're missing out on 40% more profile views"

The loss frame outperforms because it highlights what you're currently losing.

Example 3: Trial Expiration

Weak: "Your trial ends tomorrow" Strong: "Tomorrow you'll lose access

to:

47 saved projects
12 team members
All your custom settings"

Making the loss concrete and specific amplifies the effect.

Ethical Guidelines

Do

Use loss framing for genuinely beneficial actions
Be honest about what's at stake
Give users real control and options
Balance loss frames with positive experiences
Test that users feel good after taking action

Avoid

Manufacturing fake urgency or scarcity
Guilt-tripping for engagement metrics
Hiding information to create loss anxiety
Using loss aversion on vulnerable users
Dark patterns that exploit psychology

The Ethics Test

Ask: "If users knew exactly how this works psychologically, would they:

Thank us for the helpful reminder?
Feel manipulated and resentful?"

If (2), reconsider the approach.

Best Practices

Effective Loss Messaging

| Element | Example |

| --------------- | ----------------------------------------------- |

| Specific | "Lose your 23 saved items" not "Lose your data" |

| Immediate | "Expires tonight" not "Expires soon" |

| Personal | "Your progress" not "Progress" |

| Recoverable | Show how to prevent the loss |

Timing Matters

| Timing | Effectiveness |

| ---------- | ------------------------- |

| Too early | Feels irrelevant, ignored |

| Just right | Motivates action |

| Too late | Creates resentment |

| After loss | Recovery opportunity |

Integration with Other Methods

| Method | Combined Use |

| -------------------------- | --------------------------------------- |

| Hooked Model | Investment phase creates loss potential |

| Fogg Behavior Model | Loss increases motivation |

| Cognitive Biases | Combine with other biases carefully |

| Progressive Disclosure | Reveal loss implications gradually |

Resources

[Thinking, Fast and Slow - Daniel Kahneman](https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555)
[Predictably Irrational - Dan Ariely](https://www.amazon.com/Predictably-Irrational-Hidden-Forces-Decisions/dp/0061353248)
[Prospect Theory Original Paper - Kahneman & Tversky](https://www.jstor.org/stable/1914185)

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