Referral Program Launch Kit System

This prompt is built for Stage 4: Activation and Campaign Launch and focuses on generator-grade decisions instead of generic brainstorming. It is tuned for launch, conversion, content outcomes and returns structured outputs with explicit sequencing, constraints, and measurable checkpoints aligned to Deploy assets in sequence and track stage-level lift.. Use it when you need immediate execution clarity and trustworthy next-step recommendations.

Use Case

Use when you are running you get attention but no money moves - fix it in 7 days and need a high-confidence generator output for stage 4: activation and campaign launch with practical detail across launch and conversion.

Input Variables

Variable	Description	Example
`{{product}}`	What you're getting referrals for	SaaS product - social media scheduling tool, $29/mo
`{{customers}}`	Current customer base size	500 paying customers
`{{budget}}`	What you can offer per referral	$10 credit per successful referral for both parties
`{{goal}}`	Referral program target	100 new customers in first month

Prompt Template

Variable Inputs

Enter values, then click Apply Values.

What you're getting referrals for

Current customer base size

What you can offer per referral

Referral program target

All variables have values and are ready to apply.

Prompt Preview

Original template preview

You are a campaign launch operator for operators who need execution-grade outcomes without generic advice.

Task: Run a package workflow for Stage 4: Activation and Campaign Launch and produce a decision-ready deliverable that can be executed this week for Referral Program Launch Kit System.

Primary domain signals: launch, conversion, content.

Inputs:
- product: {{product}}
- customers: {{customers}}
- budget: {{budget}}
- goal: {{goal}}

Steps:
1) Parse the context and identify the highest-leverage decision point.
2) Apply stage intent logic with explicit assumptions and confidence notes.
3) Preserve the original intent of Referral Program Launch Kit System while increasing specificity and operational value.
4) Ground recommendations in the domain signals listed above.
5) Produce outputs in the exact section order requested below.
6) Prioritize recommendations by impact and implementation effort.

Deliver:
1) Generate 10 high-specificity launch options from {{product}} tailored to {{customers}}.
2) Score each option by stage fit, credibility, and conversion alignment to {{budget}}.
3) Select top 3 with rationale and implementation notes.
4) Add two fallback options for low-confidence contexts.
5) Include one anti-pattern list to avoid generic output.

Constraints:
- Keep language specific and operational; avoid generic filler.
- Do not invent metrics; mark assumptions explicitly.
- Keep one decision focus per section.
- Include at least one risk guardrail and one fallback action.

Output Format:
- Candidate Variants
- Scoring Rationale
- Top 3 Picks
- Implementation Notes
- Risk and Anti-Patterns

Self-check before final answer:
- Every recommendation maps to at least one provided input variable.
- At least one KPI checkpoint has target and review window.
- No section includes repeated boilerplate wording.

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Example Output

Stage 4: Activation and Campaign Launch Candidate Variants

Variant A: specific angle with proof-led opener.
Variant B: contrarian framing with explicit audience filter.
Variant C: utility-first framing with conversion CTA. Scoring Rationale

Variant B wins on differentiation (9/10) and conversion fit (8/10). Implementation Notes
Publish B first; test A as fallback within 48h. Risk and Anti-Patterns
Avoid broad claims without proof examples.

Tips

Make the referral reward valuable enough that people actively promote it, not just passively share a link. Track one KPI baseline before launch, run a 7-day checkpoint review, and iterate one major variable per cycle to preserve signal quality.

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