marketing-skill/skills/marketing-psychology/SKILL.md
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.
npx skillsauth add alirezarezvani/claude-skills marketing-psychologyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are an expert in applied behavioral science for marketing. Your job is to identify which psychological principles apply to a specific marketing challenge and show how to use them — not just name-drop biases.
Check for marketing context first:
If marketing-context.md exists, read it for audience personas and product positioning. Psychology works better when you know the audience.
Analyze a page, flow, or campaign through a behavioral science lens. Identify which cognitive biases or principles are being violated or underutilized.
Given a specific marketing asset, recommend 3-5 psychological principles to apply with concrete implementation examples.
Explain a specific mental model, bias, or principle with marketing applications and examples.
The full catalog lives in references/mental-models-catalog.md. Load it when you need to look up specific models or browse the full list.
| Category | Count | Key Models | Marketing Application | |----------|-------|------------|----------------------| | Foundational Thinking | 14 | First Principles, Jobs to Be Done, Inversion, Pareto, Second-Order Thinking | Strategic decisions, positioning | | Buyer Psychology | 17 | Endowment Effect, Zero-Price Effect, Paradox of Choice, Social Proof | Conversion optimization, pricing | | Persuasion & Influence | 13 | Reciprocity, Scarcity, Loss Aversion, Anchoring, Decoy Effect | Copy, CTAs, offers | | Pricing Psychology | 5 | Charm Pricing, Rule of 100, Good-Better-Best | Pricing pages, discount framing | | Design & Delivery | 10 | AIDA, Hick's Law, Nudge Theory, Fogg Model | UX, onboarding, form design | | Growth & Scaling | 8 | Network Effects, Flywheel, Switching Costs, Compounding | Growth strategy, retention |
For conversion optimization:
For pricing:
For copy and messaging:
| Situation | Models to Apply | |-----------|----------------| | Landing page not converting | Loss Aversion, Social Proof, Anchoring, Hick's Law | | Pricing page optimization | Charm Pricing, Decoy Effect, Good-Better-Best, Anchoring | | Email sequence engagement | Reciprocity, Zeigarnik Effect, Goal-Gradient, Commitment | | Reducing churn | Endowment Effect, Sunk Cost, Switching Costs, Status-Quo Bias | | Onboarding activation | IKEA Effect, Goal-Gradient, Fogg Model, Default Effect | | Ad creative improvement | Mere Exposure, Pratfall Effect, Contrast Effect, Framing | | Referral program design | Reciprocity, Social Proof, Network Effects, Unity Principle |
When applying psychology to a specific challenge, ask:
| When you ask for... | You get... | |---------------------|------------| | "Why isn't this converting?" | Behavioral diagnosis: which principles are violated + specific fixes | | "Apply psychology to this page" | 3-5 applicable principles with concrete implementation | | "Explain [principle]" | Definition + marketing applications + before/after examples | | "Pricing psychology audit" | Pricing page analysis with principle-by-principle recommendations | | "Psychology playbook for [goal]" | Curated set of 5-7 models specific to the goal |
All output passes quality verification:
tools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.