Stories - Double Affiliate Revenue for DTC Skincare

# Stories - Double Affiliate Revenue for DTC Skincare **Category:** Writing → Stories | **Audience:** Professionals or practitioners using AI for stories tasks. | **Delivery:** User-specified channel or workflow **Job:** Improve the named business/performance outcome using a measurable, testable intervention. ## SOURCE INPUT Use this source brief as the authoritative task/scenario. Preserve its supplied numbers, constraints, assets, frameworks and requested deliverables. Separate facts from assumptions; never invent missing evidence. > Treat this as the authoritative source brief. Preserve useful specifics; do not invent facts or turn scenario values into verified claims. > > > Act as Senior Stories Strategist for Writing (experienced, scaled DTC Skincare to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: DTC Skincare - $1.2M ARR, team 50, targeting SMB owners, stage Seed Current: CAC $192, LTV $1280, churn 15%, traffic 71134/mo, conversion 4.0% Assets: Next.js codebase + Stripe Goal: double affiliate revenue in 30 days Constraint: Budget $8k/mo, GDPR required Tone: bold contrarian Hormozi FRAMEWORK: QUEST + structured 4-step. TASK: Complete Stories system for Writing / DTC Skincare. - common failure modes about stories for DTC Skincare - 5 Whys root cause for double affiliate revenue - Hidden cost of failure - Contrarian insight advanced practitioner insight ## Writing approach variants > Variant A — DIRECT / INFORMATIONAL: Optimize for clarity, structure and fast comprehension. > Variant B — NARRATIVE / EMOTIONAL: Optimize for story, tension, voice and reader engagement. > Variant C — PERSUASIVE / ACTION: Optimize for argument, proof, objection handling and a defined reader action. > Specify audience, purpose, structure, tone, evidence and desired reader response for each. ## METHOD **Workflow:** Purpose → audience → message → structure → evidence/voice → draft → edit → quality check **Execution** - Diagnose the actual task before prescribing when diagnosis changes the answer. - Make the key strategic/technical/creative decision explicit. - Use concrete instructions, structures, examples, thresholds or implementation details. - Distinguish supplied facts, assumptions and validation needs. - Do not invent capabilities, results, claims or evidence. **Model / delivery** Target model(s): ChatGPT-4o, Claude 3.5, Gemini 1.5. Keep the core solution portable; adapt only relevant model behavior. Delivery context: User-specified channel or workflow; respect its real format, audience and constraints. **Output contract** Return the smallest complete deliverable that solves the job. Include the result, material assumptions, measurable/verifiable success criteria where relevant, and next decision/action when useful. **QA** - purpose and reader are clear - structure serves the message - tone is controlled - unsupported claims are avoided - final draft is usable Also check for contradictions, generic recommendations, unusable outputs and constraint violations. ## CONTROLLED VARIANTS Use variants only as real experiments; never as paraphrases. **A — Clarity:** optimize comprehension. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. **B — Persuasion:** optimize belief/action. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. **C — Voice:** optimize distinctive voice while preserving meaning. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. ## FINAL GATE Before answering, ask: Is this specific to the supplied scenario? Is every important instruction actionable? Are assumptions labeled? Is the output genuinely usable? Would a professional get a better result from this than from a generic prompt? If not, revise the weakest part before delivering.