Design an in-product referral or viral loop covering user value, incentives, journey triggers, UX, invitation copy, measurement, fraud prevention, privacy, and experimentation.
This prompt has variables that can be replaced with your own information. Copy and use it with your preferred LLM, or try it out in the LearnerBox Prompt Playground.
Act as an experienced Growth Product Marketer specializing in product-led growth, referral systems, lifecycle engagement, and experimentation.
Develop the requested campaign using the information below.
Product and User Value:
{{product_value}}
User Journey and Activation Event:
{{user_journey}}
Referrer and Referee Segments:
{{user_segments}}
Business Model and Incentive Limits:
{{business_model}}
Capabilities and Constraints:
{{capabilities_constraints}}
Campaign requirements:
1. Determine whether referral, collaboration, invitation, content sharing, or network-effect mechanics best match the product.
2. Define priority referrer and referee segments.
3. Identify the demonstrated-value event that should trigger the referral ask.
4. Map the loop from value realization to invitation, acceptance, activation, reward, repeat sharing, and retention.
5. Develop three incentive models: two-sided, one-sided, and non-monetary utility or status.
6. Compare cost, attractiveness, abuse risk, margin impact, and sustainability; recommend one.
7. Design trigger logic, eligibility, frequency caps, suppression rules, and friction-minimized UX.
8. Write copy for in-app prompt, modal, share page, email invitation, share link, reward confirmation, reminder, and status screen.
9. Define analytics events and KPIs including invitation, acceptance, activation, qualified referral, retention, fraud, and properly calculated viral coefficient.
10. Create an experiment roadmap for timing, incentive, copy, channel, eligibility, and UX.
11. Include controls for self-referrals, duplicate accounts, bots, incentive farming, payment fraud, consent, and messaging compliance.
12. Do not invent conversion rates or product capabilities; do not recommend spam, address-book scraping without consent, or deceptive sharing.
Present the result as:
{{output_format}}
Include:
- viral-loop diagnosis;
- target segments;
- trigger event;
- loop map;
- three incentive models;
- recommended incentive;
- eligibility rules;
- complete copy set;
- UX outline;
- analytics events;
- KPI dashboard;
- experiment roadmap;
- anti-fraud controls;
- privacy checklist;
- professional validation checklist.Replace each variable shown in double curly brackets with accurate information from your own professional context.
{{product_value}}
Example: Describe the product, core use case, value moment, collaboration features, and sharing potential.
Provide verified information and label assumptions clearly.
{{user_journey}}
Example: Describe signup, onboarding, activation, habit formation, payment, and retention milestones.
Provide verified information and label assumptions clearly.
{{user_segments}}
Example: Describe active users, high-value users, teams, creators, customers, and likely invitees.
Provide verified information and label assumptions clearly.
{{business_model}}
Example: Provide pricing, margins, trial structure, credits, discounts, free usage, and reward budget.
Provide verified information and label assumptions clearly.
{{capabilities_constraints}}
Example: Describe in-app messaging, referral links, analytics, email, push, engineering capacity, privacy, fraud, and legal limits.
Provide verified information and label assumptions clearly.
{{output_format}}
Choose the format needed for planning, approval, or execution.
A product-led referral framework containing loop diagnosis, segments, trigger logic, incentive models, UX copy, analytics, KPIs, experiments, fraud controls, privacy safeguards, and implementation priorities.
These characteristics describe the type of thinking, customization, and output structure involved in using this prompt effectively.
This breakdown explains how the prompt’s major components work together to guide the AI toward a useful, reliable, and well-structured response.
Positions the AI as an experienced Growth Product Marketer specializing in product-led growth, referral systems, lifecycle engagement, and experimentation.
Defines the campaign inputs, audience, resources, evidence, and operating constraints.
Requires a complete professional campaign strategy with execution, measurement, and governance.
Prevents invented evidence, deceptive tactics, unsupported claims, and guaranteed outcomes.
Requires structured analysis, campaign actions, KPIs, testing, risks, and professional validation.
Campaign context, audience, resources, constraints, and output format.
AI-generated responses can contain errors, omissions, unsupported assumptions, outdated information, or recommendations that do not reflect your jurisdiction or professional context.
Verify calculations, evidence, regulations, standards, policies, and professional recommendations before relying on the result. The qualified professional remains responsible for the final decision.
Return to the specialization page to explore additional professional workflows and prompt templates.
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