Growth Engineering

When to Use This Skill

Activate this skill when the user's request involves any of the following:

• Designing or improving a product-led growth (PLG) motion
• Building or optimizing referral programs (customer referral, partner referral, ambassador programs)
• Creating viral loops or increasing organic sharing mechanics
• Planning a product or company launch (Product Hunt, beta launches, waitlists)
• Improving user retention, reducing churn, or designing re-engagement campaigns
• Running growth experiments and building an experimentation culture
• Setting up or optimizing affiliate marketing programs
• Designing activation flows and reducing time-to-value for new users
• Building growth models or forecasting viral growth coefficients
• Solving cold-start problems for marketplaces or platforms
• Identifying and scoring product-qualified leads (PQLs)
• Any question about growth levers, growth loops, or sustainable acquisition strategies

Brand Context (Auto-Applied)

Before producing any marketing output from this module:

1. Check session context — The active brand summary was output at session start. Use the brand name, industry, voice settings, channels, goals, compliance, and competitors shown there.
2. If you need the full profile, read: ~/.claude-marketing/brands/{slug}/profile.json
3. Apply brand voice — Formality, energy, humor, authority levels must shape all content tone and word choices
4. Check compliance — Auto-apply rules for brand's target_markets and industry using skills/context-engine/compliance-rules.md
5. Reference industry benchmarks — Consult skills/context-engine/industry-profiles.md for the brand's industry
6. Use platform specs — Reference skills/context-engine/platform-specs.md for character limits and format requirements
7. Check campaign history — Run python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns before planning new work
8. If no brand exists, say: "No brand profile found. Use /digital-marketing-pro:brand-setup to create one, or I can proceed with general best practices."
9. Check brand guidelines — If ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json exists, load and enforce: restrictions.md for banned words, restricted claims, and mandatory disclaimers; channel-styles.md for channel-specific tone overrides (may differ from base voice); messaging.md for approved key messages, taglines, and positioning language; voice-and-tone.md for detailed voice rules beyond the 4 numeric scores. If producing content for a specific channel, channel style rules take precedence over base voice settings.

Do not ask the user for information that already exists in their brand profile.

Required Context

Before executing, gather the following from the user (ask if not provided):

• Product type: SaaS, marketplace, ecommerce, mobile app, content platform, service business
• Business model: Subscription, transactional, freemium, free-trial, advertising-supported
• Current stage: Pre-launch, early traction (under 1,000 users), growth stage, scale stage
• Key metrics: Current MRR/ARR, user count, activation rate, retention rate, churn rate, NPS
• Existing growth channels: Which acquisition channels are active and their relative performance
• Viral potential: Whether the product has inherent sharing mechanics or requires artificial virality
• Team and resources: Engineering capacity for growth features, marketing budget, partnership resources
• Target user: Who the ideal user is and what their primary motivation for using the product is
• Competitive landscape: Key competitors and their growth strategies

Capabilities

Product-Led Growth (PLG) Strategy

• Free-to-paid conversion: Freemium model design, free trial optimization, feature gating strategy, usage-based pricing triggers
• Activation metrics: Define the "aha moment," map the steps to reach it, measure and optimize activation rate
• Time-to-value optimization: Reduce friction between signup and first value experience through onboarding design, templates, sample data, and guided tours
• PQL scoring: Define product-qualified lead criteria based on usage patterns, feature adoption, team size, and engagement frequency
• Self-serve expansion: In-product upgrade prompts, usage limit notifications, team invite flows, seat expansion triggers
• Reverse trial: Give full access first, then downgrade to free -- when this works better than traditional freemium

Referral Systems

• Give-and-get programs: Both referrer and referee receive incentives (e.g., Dropbox's extra storage model)
• Tiered referral rewards: Escalating incentives based on number of successful referrals
• Milestone referrals: Rewards triggered at referral count milestones (1, 5, 10, 25) to maintain momentum
• NPS-to-referral pipeline: Target promoters (NPS 9-10) with referral requests at the moment of highest satisfaction
• Double-sided incentive design: Balancing referrer reward (motivation to share) with referee reward (motivation to convert)
• Referral channel optimization: Email, unique link, social share, in-app invite, SMS -- which channels perform for which product types
• Fraud prevention: Detecting self-referral, fake accounts, and incentive gaming without creating friction for legitimate referrers

Viral Loop Design

• Inherent virality: The product naturally requires others to use it (Slack, Zoom, Google Docs)
• Artificial virality: Manufactured sharing through incentives, social features, or content creation (shareable reports, badges, results)
• Content virality: User-generated content that surfaces on external platforms and drives new users back
• Social proof virality: Visible usage signals (badges, signatures, "powered by" links, public profiles)
• Viral coefficient calculation: K-factor = invites per user x conversion rate of invites. K > 1 means exponential growth; K between 0.5-1.0 augments paid acquisition significantly
• Viral cycle time: Reducing the time between a user joining and their invitees joining. Shorter cycles compound faster even with lower K-factors

Launch Playbooks

• Tier 1 launch (major product): Full press campaign, influencer seeding, Product Hunt, beta community, launch event, paid amplification
• Tier 2 launch (feature/update): Existing user announcement, targeted outreach, community posts, changelog, email campaign
• Tier 3 launch (minor update): In-app notification, changelog update, social media post
• Pre-launch waitlist: Viral waitlist mechanics (share to move up), early access incentives, drip content to maintain interest
• Product Hunt launch: Preparation timeline (2-4 weeks), hunter selection, launch day playbook, post-launch engagement
• Beta program design: Closed beta recruitment, feedback loops, beta-to-launch transition, early adopter community building

Retention Loops

• Engagement design: Habit loops (trigger, action, variable reward, investment), notification strategy, content cadence
• Re-engagement campaigns: Email sequences, push notifications, in-app messages, retargeting ads triggered by inactivity signals
• Churn prediction: Behavioral signals that indicate churn risk (login frequency drop, feature usage decline, support ticket patterns)
• Winback sequences: Timed outreach to churned users with personalized value reminders, product updates, and incentive offers
• Cohort analysis: Track retention by signup cohort, acquisition channel, activation status, and feature adoption to identify what drives long-term retention
• Expansion revenue: Upsell and cross-sell triggers based on usage patterns, team growth, and feature engagement

Affiliate Marketing

• Program design: Commission structure (percentage, flat fee, tiered, recurring), cookie duration, attribution rules
• Network selection: When to use affiliate networks (ShareASale, CJ, Impact) vs building a custom program
• Affiliate recruitment: Finding high-quality affiliates through competitor analysis, content partnerships, and niche community outreach
• Commission optimization: Balancing commission rates to attract affiliates while maintaining profitability; performance tiers to reward top performers
• Fraud detection: Click fraud, cookie stuffing, brand bidding violations, trademark misuse, coupon abuse
• Content affiliate strategy: Working with bloggers, review sites, comparison sites, and niche publishers
• Affiliate-influencer hybrids: Creator partnerships with performance-based compensation models

Process

PLG Implementation (Most Common Use Case)

1. Map the user journey -- Document every step from first awareness to paid conversion. Identify where users currently drop off and where they experience value.
2. Define the activation metric -- Determine the specific action or combination of actions that correlates with long-term retention. This is the "aha moment" the entire PLG motion revolves around.
3. Design the free offering -- Structure the free tier or trial to give users enough value to experience the activation moment while creating natural upgrade triggers. Reference common models: feature-limited freemium, usage-limited freemium, time-limited trial, reverse trial.
4. Optimize time-to-value -- Redesign onboarding to get users to the activation metric as fast as possible. Remove unnecessary steps, add templates/sample data, implement guided tours, and offer quick-start paths.
5. Build PQL scoring -- Define the behavioral signals that indicate a free user is ready for a sales touch or upgrade prompt. Combine usage frequency, feature breadth, team size, and engagement depth into a composite score.
6. Implement expansion loops -- Design in-product mechanisms for organic growth: team invites, shared workspaces, public outputs, integrations that touch other teams, and usage-based upgrade paths.
7. Measure and iterate -- Track activation rate, free-to-paid conversion rate, time-to-activation, expansion revenue, and viral coefficient. Run experiments on each stage of the funnel.

Referral Program Build

1. Assess viral potential -- Determine whether the product has inherent sharing mechanics or requires incentive-driven referrals. Analyze NPS data to identify promoter concentration.
2. Design incentive structure -- Choose the referral model (give-and-get, tiered, milestone) based on product type, customer LTV, and competitive benchmarks. Set incentive values at 10-25% of customer acquisition cost.
3. Build referral mechanics -- Create unique referral links, sharing interfaces, tracking infrastructure, and reward fulfillment flows. Make sharing frictionless (one-click, pre-written messages).
4. Integrate referral touchpoints -- Embed referral prompts at high-satisfaction moments: post-purchase, after achieving a milestone, after a positive support interaction, at NPS survey completion.
5. Launch and promote -- Announce the program to existing users, feature it in onboarding, add it to account dashboards, and include it in email communications.
6. Monitor and optimize -- Track share rate, invite conversion rate, referral revenue, and fraud signals. A/B test incentive types, sharing copy, and prompt placement.

Reference Files

• product-led-growth.md -- PLG frameworks, freemium vs trial decision trees, activation metric identification, PQL scoring models
• referral-systems.md -- Referral program templates, incentive design principles, fraud prevention tactics, and case studies
• viral-loops.md -- Viral coefficient calculations, loop design patterns, virality assessment frameworks
• launch-strategy.md -- Tier 1/2/3 launch playbooks, Product Hunt guide, waitlist mechanics, beta program design
• retention-loops.md -- Engagement frameworks, churn prediction models, winback sequences, cohort analysis methods
• affiliate-marketing.md -- Program setup guides, network comparisons, commission optimization, fraud detection systems

Output Formats

• PLG strategy document: User journey map, activation metric definition, free tier design, PQL criteria, expansion loop mechanics, and success metrics
• Referral program spec: Incentive structure, mechanics description, integration points, launch plan, and monitoring dashboard requirements
• Launch playbook: Pre-launch timeline, launch day checklist, channel-by-channel activation plan, post-launch follow-up sequence
• Retention analysis: Cohort retention curves, churn risk indicators, re-engagement campaign designs, and winback sequence templates
• Growth model: Spreadsheet-ready model with viral coefficient, cycle time, channel-level CAC, retention curves, and projected growth trajectories
• Affiliate program blueprint: Commission structure, network/platform recommendation, recruitment strategy, compliance guidelines, and fraud monitoring plan

Edge Cases

Non-Viral Products

Some products have no inherent sharing mechanic (e.g., personal finance tools, solo productivity apps). For these, create artificial virality through shareable outputs (reports, achievements, results), social proof features (public profiles, leaderboards), content creation tools that naturally surface the brand, or incentive-driven referrals. Not every product needs a viral coefficient above 1 -- even K=0.3 meaningfully reduces CAC.

Marketplace Cold-Start Problem

Two-sided marketplaces face the chicken-and-egg problem: no supply without demand, no demand without supply. Solve by starting with one side (usually supply) through manual recruitment, seeding content, or offering the supply side a standalone value proposition. Concentrate on a narrow geography or vertical first. Reference strategies: Uber started with black cars, Airbnb started with events, and Yelp started with reviews before transactions.

B2B vs B2C Retention Dynamics

B2B retention depends on product becoming embedded in workflows, multi-user adoption within an organization, and integration with other tools. B2C retention depends on habit formation, content freshness, and emotional engagement. Do not apply B2C engagement tactics (daily push notifications, gamification) to B2B products. B2B retention is measured monthly or quarterly, not daily.

Referral Fraud Prevention

Common referral fraud patterns include self-referral with multiple accounts, referral rings between friends who churn after receiving rewards, and bot-generated fake referrals. Mitigate by requiring the referred user to complete a meaningful action (purchase, usage threshold) before rewards are distributed, implementing device fingerprinting, setting reasonable reward caps, and monitoring for anomalous patterns.

International Launch Sequencing

When launching internationally, do not launch everywhere simultaneously. Sequence by market attractiveness (TAM, competitive density, regulatory complexity), language/localization readiness, and existing demand signals. Start with one new market, prove the playbook, then expand. Payment methods, compliance requirements, and cultural norms vary significantly and can break launch plans built for the home market.

Related Skills

• Paid Advertising -- Paid acquisition that PLG and viral loops can amplify or replace over time
• CRO -- Activation and onboarding optimization overlaps heavily with CRO principles
• Content Engine -- Content marketing that feeds growth loops and launch amplification
• Analytics & Insights -- Cohort analysis, experiment measurement, and growth modeling
• Influencer & Creator Marketing -- Creator partnerships for launch amplification and affiliate-influencer hybrids
• Funnel Architect -- Growth engineering strategies plug into the broader acquisition and retention funnel

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Supporting file: affiliate-marketing.md

Affiliate Marketing — Program Strategy

Affiliate marketing is a performance-based channel where external partners (affiliates) promote your product in exchange for commissions on qualified actions. When structured well, it delivers predictable, profitable customer acquisition.

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Program Setup Guide

Platform Selection

Choose between building in-house or using an affiliate network/platform.

Program Setup Checklist

• Define program goals (revenue target, number of active affiliates, target CAC)
• Set commission structure and terms (see Commission Optimization section)
• Select tracking platform or network
• Implement conversion tracking (pixel, postback, API integration)
• Create affiliate landing page explaining the program, benefits, and application process
• Write program terms and conditions (legal review required)
• Build creative asset library (banners, text links, email templates, product images)
• Set up affiliate dashboard (reporting, link generation, payout tracking)
• Configure fraud detection rules and monitoring
• Design affiliate onboarding sequence (welcome email, getting started guide, first commission tips)
• Establish payment terms and method (PayPal, wire, check; net-30 or net-60)
• Create internal processes for affiliate approval, support, and escalation

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Network Comparison

Selection Criteria

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Commission Optimization

Commission Models

Commission Rate Calibration

Tiered Commission Structure Example

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Affiliate Recruitment Strategies

Affiliate Segments

Recruitment Outreach Process

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Fraud Detection

Common Affiliate Fraud Types

Fraud Prevention Checklist

• Set commission hold period to 30-60 days (beyond refund window)
• Monitor click-to-conversion ratios by affiliate (flag outliers)
• Block trademark bidding in program terms, monitor with SEMrush or SpyFu
• Require minimum customer retention period before commission is finalized
• Review new affiliate applications manually (check website, traffic sources)
• Implement IP analysis for click and conversion matching
• Set maximum conversion rate threshold (flag if >20% without explanation)
• Audit top 10 affiliates quarterly (traffic sources, content, compliance)
• Use fraud detection tools built into your network/platform
• Reserve contractual right to claw back commissions for fraudulent activity

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Content Guidelines & Brand Protection

Affiliate Content Policy

Brand Protection Checklist

• Publish clear brand guidelines document for affiliates
• Provide approved creative assets (logos, banners, product images)
• Require FTC disclosure on all affiliate content ("This post contains affiliate links")
• Monitor affiliate content quarterly for compliance
• Set up Google Alerts for brand + affiliate-related queries
• Include content compliance requirements in program T&Cs
• Establish a violation escalation process (warning → commission hold → termination)
• Maintain a list of approved and prohibited promotional methods

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Program Management

Monthly Management Tasks

Key Program Metrics

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Affiliate vs Influencer Distinction

When to Use Each

Hybrid Model

Many programs blend affiliate and influencer approaches:

Influencer receives: Flat content creation fee ($500-$5,000) + affiliate commission (15-25%)
Brand receives: High-quality branded content + trackable, ongoing revenue from that content
Result: Influencer is motivated to create AND promote; brand gets awareness AND conversions

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A well-run affiliate program is a portfolio of performance partnerships. Recruit deliberately, compensate fairly, monitor consistently, and treat your best affiliates like the revenue-generating partners they are.

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Supporting file: experimentation-frameworks.md

Growth Experimentation — Frameworks & Methodology Reference

Experiment Prioritization Frameworks

ICE Score (Impact, Confidence, Ease)

ICE Score = (Impact + Confidence + Ease) / 3

Example:
Experiment: Change CTA button color from gray to green
- Impact: 3 (minor UX change)
- Confidence: 4 (no strong data suggesting impact)
- Ease: 10 (5-minute code change)
- ICE Score: (3 + 4 + 10) / 3 = 5.7

Experiment: Redesign pricing page with social proof and FAQ
- Impact: 9 (pricing page is highest-intent page)
- Confidence: 7 (competitor analysis + user research supports it)
- Ease: 4 (requires design, copy, dev work)
- ICE Score: (9 + 7 + 4) / 3 = 6.7  ← Prioritize this one

Best for: Small teams, fast-moving startups, early-stage experimentation programs. Quick to score, easy to debate.

RICE Score (Reach, Impact, Confidence, Effort)

RICE Score = (Reach x Impact x Confidence) / Effort

Example:
Experiment: Add exit-intent popup with email capture on blog
- Reach: 50,000 visitors/month
- Impact: 1 (medium — captures some emails)
- Confidence: 80%
- Effort: 0.5 person-months
- RICE Score: (50,000 x 1 x 0.8) / 0.5 = 80,000

Experiment: Rebuild checkout flow to reduce steps from 5 to 3
- Reach: 8,000 checkout initiators/month
- Impact: 3 (massive — directly affects revenue)
- Confidence: 80%
- Effort: 3 person-months
- RICE Score: (8,000 x 3 x 0.8) / 3 = 6,400

Best for: Product teams with clear user data, growth teams needing to justify investment to stakeholders.

PIE Score (Potential, Importance, Ease)

PIE Score = (Potential + Importance + Ease) / 3

Best for: CRO teams focused on website optimization, where you're comparing pages/funnels against each other.

When to Use Each Framework

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Hypothesis Format

Every experiment must start with a falsifiable hypothesis. A vague "let's try this" is not an experiment.

Standard Hypothesis Template

If we [specific change],
then [target metric] will [direction: increase/decrease]
by [estimated magnitude: %, absolute number, or range]
because [reasoning based on data, research, or user insight].

Examples of Strong Hypotheses

Common Hypothesis Mistakes

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Experiment Types

A/B Test Decision Criteria

Is your change a single, isolated variable?
├── Yes → A/B test
└── No → Multiple elements changing?
    ├── Yes, and I need to know interaction effects → Multivariate test
    ├── Yes, but they're part of a complete redesign → Split URL test
    └── No, it's a product feature → Feature flag with holdout

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Statistical Foundations

Sample Size Calculation

Before running any test, calculate the required sample size to avoid inconclusive results.

Sample Size Reference Table (95% significance, 80% power)

Rule of thumb: If your baseline CVR is low or your desired MDE is small, you need very large sample sizes. If you can't reach the required sample size within 4-6 weeks, either increase your MDE threshold or find a higher-traffic test location.

Duration Estimation

Test Duration (days) = Required Sample Size per Variant x 2 / Daily Traffic to Page

Example:
- Baseline CVR: 5%
- MDE: 10% relative (5% → 5.5%)
- Required: 146,000 per variant = 292,000 total
- Daily traffic: 8,000 visitors/day
- Duration: 292,000 / 8,000 = 36.5 days → run for 5 weeks (include full weekday cycles)

Avoiding Statistical Pitfalls

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Growth Experiment Categories (AARRR Framework)

Acquisition Experiments

Activation Experiments

Retention Experiments

Revenue Experiments

Referral Experiments

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Experiment Tracking

Experiment Log Template

Experiment Status Board

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Experimentation Velocity

Benchmark Velocity Targets

How to Increase Velocity

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Learning from Failures

Why Experiments Fail (and What to Learn)

Post-Experiment Analysis Template

Experiment: [Name]
Result: [Won / Lost / Inconclusive]

1. What did we expect to happen?
   [Restate hypothesis]

2. What actually happened?
   [Primary metric result + confidence level + secondary metric results]

3. Why did it happen?
   [Analysis of user behavior, segment breakdowns, qualitative insights]

4. What did we learn?
   [Insight that applies beyond this single experiment]

5. What's the next experiment?
   [Follow-up test based on this learning, or new direction]

6. Should we update any existing assumptions or strategies?
   [Broader implications for growth model, personas, or messaging]

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Growth Model: Inputs to Compound Growth

The Growth Equation

Growth = Acquisition x Activation x Retention x Revenue x Referral

Each factor is a multiplier. A 10% improvement in each:
1.1 x 1.1 x 1.1 x 1.1 x 1.1 = 1.61x total growth (61% improvement)

This is why experimentation compounds.

Growth Model Template

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Common Growth Experiments by Business Model

SaaS

eCommerce

Marketplace

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Growth is not about one brilliant idea. It is about the velocity of learning. Every experiment, whether it wins or loses, makes the system smarter. The teams that grow fastest are not the ones with the best ideas. They are the ones that test the most ideas, learn the fastest, and compound those learnings over time.

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Supporting file: launch-strategy.md

Launch Strategy — Product Launch Playbook

A disciplined launch process turns product releases into measurable growth events. This playbook covers the full lifecycle from planning through post-launch optimization.

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Three-Tier Launch Framework

Not every release deserves the same investment. Categorize launches to allocate effort appropriately.

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Product Hunt Launch Guide

Pre-Launch Preparation (2-4 Weeks Before)

Launch Day Execution

• Launch at 12:01 AM PST (Product Hunt resets daily)
• Post founder's first comment immediately after listing goes live
• Send first wave of notifications to supporter list (personal messages, not mass blast)
• Share on Twitter/X with compelling hook and Product Hunt link
• Post in relevant communities (Slack groups, Discord, Reddit — check rules first)
• Respond to every comment within 30 minutes
• Send second wave of notifications at 9 AM PST (US waking up)
• Share progress updates on social media throughout the day
• Send third wave at 12 PM PST if momentum is building
• Thank supporters publicly in comments and social posts
• Monitor and respond to questions until midnight PST

Post-Product Hunt

• Send thank-you email to all supporters within 24 hours
• Add "Featured on Product Hunt" badge to website (if applicable)
• Create blog post summarizing the launch and results
• Follow up with every lead generated from Product Hunt
• Analyze traffic and conversion data from launch day

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Waitlist Mechanics

Waitlist Strategy

Waitlist Email Sequence

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Beta Program Design

Beta Types

Beta Feedback Collection

• In-app feedback button (always visible during beta)
• Weekly NPS or CSAT micro-survey (1-2 questions)
• Dedicated Slack or Discord channel for beta participants
• Bi-weekly user interviews with 5-10 active beta users
• Session recordings (with consent) via Hotjar, FullStory, or LogRocket
• Automated bug reporting with screenshot capture
• Feature request voting board (Canny, Productboard, or similar)
• Exit survey for users who leave beta early

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Pre-Launch Content Sequence

Build momentum in the weeks before launch with a structured content calendar.

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Launch Day Checklist

Product Readiness (Complete Before Launch)

• Core features tested and stable (zero P0/P1 bugs)
• Onboarding flow tested with 5+ external users
• Performance benchmarks met (page load <2s, API response <500ms)
• Error monitoring configured (Sentry, Datadog, or equivalent)
• Scalability tested for expected traffic (3-5x normal capacity)
• Security review completed (auth, data handling, API keys)
• Terms of service and privacy policy published
• Payment processing tested end-to-end (if applicable)
• Mobile/responsive experience verified
• Accessibility basics covered (WCAG 2.2 AA minimum)

Marketing Assets Ready

• Landing page live and optimized (hero, features, social proof, CTA)
• Product demo video produced and uploaded
• Press kit prepared (logos, screenshots, founder photos, fact sheet)
• Blog post drafted and scheduled
• Email announcement drafted for full list
• Social media posts drafted for all platforms (Twitter, LinkedIn, Facebook, Instagram)
• Community posts drafted (Reddit, Hacker News, Indie Hackers, relevant Slacks)
• Help documentation and FAQ published
• Pricing page live and tested

Team Coordination

• All team members briefed on launch timeline and their roles
• Support team trained on new features and common questions
• Sales team equipped with updated pitch deck and battle cards
• War room or dedicated Slack channel created for launch day communication
• Escalation paths defined (who handles what if something breaks)
• On-call engineering rotation confirmed for launch window

Distribution Execution

• Email blast sent to subscriber list (timed for 10 AM recipient local time)
• Social media posts published across all platforms
• Product Hunt listing live (if applicable)
• Press embargo lifted and pitches sent
• Partner co-marketing posts coordinated
• Paid ads activated (if budget allocated for launch amplification)
• Influencer and advocate outreach completed
• Community posts published (stagger across platforms to avoid spam flags)
• Retargeting audiences updated with launch messaging
• Internal all-hands or Slack announcement to celebrate and mobilize team

Monitoring and Response

• Real-time analytics dashboard open (traffic, sign-ups, conversions)
• Server health monitoring active
• Social media monitoring active (brand mentions, hashtag tracking)
• Support queue staffed at 2x normal capacity
• Bug triage process active with fast-fix deployment capability
• Hourly check-in cadence established for launch team

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Post-Launch Optimization

First 7 Days

First 30 Days

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Launch Metric Tracking

Dashboard Metrics

Launch Scorecard Template

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A great launch is not a single day. It is a coordinated sequence that builds momentum before, executes with precision during, and compounds results after. Plan the full arc.

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Supporting file: product-led-growth.md

Product-Led Growth — Strategy & Frameworks

PLG is a go-to-market strategy where the product itself drives acquisition, expansion, and retention. Users experience value before committing budget.

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PLG Flywheel

The PLG engine follows a four-stage flywheel. Each stage feeds the next.

Value Delivery → Habit Formation → Expansion → Advocacy
      ↑                                          |
      └──────────────────────────────────────────┘

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Freemium vs Free Trial Decision Tree

Use this framework to select the right model for your product.

Hybrid approach: Offer freemium for individuals and free trials for team/enterprise tiers. This captures both bottoms-up adoption and top-down evaluation.

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Activation Metric Identification

Activation is the single most important PLG metric. It defines the moment a user first experiences meaningful value.

How to Find Your Activation Metric

1. Pull behavioral data — Export event logs for the first 7-14 days of all users
2. Segment by outcome — Split users into retained (active at Day 30+) vs churned
3. Compare behaviors — Identify actions that retained users performed at significantly higher rates
4. Rank by correlation — Find the action with the strongest correlation to retention
5. Validate causation — Run an experiment: guide new users toward that action and measure retention lift
6. Set the threshold — Define the minimum frequency or depth (e.g., "created 3 projects in first 7 days")

Activation Metric Examples

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PQL (Product-Qualified Lead) Scoring Model

PQLs replace MQLs in PLG. A PQL is a user whose product behavior signals buying intent.

PQL Scoring Components

Scoring Tiers

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Self-Serve Onboarding Optimization

Onboarding Checklist

• Time-to-first-value is under 5 minutes
• Sign-up requires no more than 3 fields (email, name, password — or SSO)
• Welcome flow asks 1-2 segmentation questions to personalize experience
• Empty states include templates, sample data, or guided actions
• Progress indicators show completion status
• Tooltips and contextual help are triggered by user behavior, not timers
• Email sequence supplements in-app guidance (Day 0, 1, 3, 7)
• Users can invite teammates before completing onboarding
• Mobile experience is functional even if desktop is primary
• Exit points offer help (chat, docs, video) before abandonment

Onboarding Anti-Patterns

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PLG Metrics Dashboard

Primary Metrics

Cohort Tracking

Track these for each weekly or monthly sign-up cohort:

• Day 1, Day 7, Day 30, Day 90 retention
• Activation rate within first 7 days
• Median time-to-activation
• Free-to-paid conversion by Day 30, 60, 90
• Expansion revenue generated by Day 180
• Referrals generated per cohort

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PLG for Different Business Models

PLG + Sales-Assist Hybrid

Most successful PLG companies add sales as they scale. The model evolves:

Stage 1: Pure self-serve (0 → $1M ARR)
Stage 2: Self-serve + inbound sales for large accounts ($1M → $10M ARR)
Stage 3: PLG-qualified pipeline feeds AE team ($10M → $50M ARR)
Stage 4: Full hybrid — PLG for SMB, sales-led for enterprise ($50M+ ARR)

Implementation Priority

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PLG compounds because every user is a potential acquisition channel, every team is an expansion opportunity, and every integration deepens retention. The product is the growth engine.

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Supporting file: referral-systems.md

Referral Systems — Program Design & Optimization

A structured referral program turns satisfied customers into a scalable, low-CAC acquisition channel. The best programs align incentives for both referrer and referee.

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Referral Program Templates

Template Comparison

Double-Sided Program Design

Referrer Action → Unique Link/Code Generated → Referee Signs Up →
Referee Qualifies (activation event) → Both Parties Rewarded

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Incentive Design Principles

Reward Type Selection

Incentive Calibration Checklist

• Reward value is 10-25% of customer LTV (ensures positive ROI)
• Reward is immediately understandable (no complex calculations)
• Reward matches user motivation (intrinsic vs extrinsic)
• Double-sided rewards are roughly balanced (neither party feels shortchanged)
• Escalating rewards exist for power referrers (5+ successful referrals)
• Reward fulfillment is automated (no manual approval bottleneck)
• Tax implications are documented for cash rewards above reporting thresholds
• Reward expiration policy is clearly communicated

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Referral Mechanics

Tracking Infrastructure

Link Architecture

https://yourapp.com/invite?ref=USER_ID&campaign=CAMPAIGN_NAME

Parameters tracked:
- ref: unique referrer identifier
- campaign: which referral program variant
- channel: where the link was shared (email, social, direct)
- timestamp: when the link was generated

Attribution Rules

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Fraud Prevention Tactics

Common Fraud Patterns

Fraud Prevention Checklist

• Require a meaningful activation event before issuing rewards (not just sign-up)
• Implement device fingerprinting to detect multi-accounting
• Set velocity limits (max 5 referrals per day, 20 per week)
• Block known disposable email domains
• Hold rewards for 7-14 day cooling period before payout
• Monitor referral-to-activation ratio by referrer (flag if <20%)
• Review top referrers manually each month
• Build an automated flagging system for anomalous patterns
• Include anti-fraud terms in referral program T&Cs
• Reserve the right to revoke rewards retroactively

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Launch Playbook

Pre-Launch (2-4 Weeks Before)

Launch Day Checklist

• Referral widget or page is live in product
• Triggered email sent to top 20% most active users announcing program
• In-app notification or banner promoting referral program
• Social media announcement with shareable assets
• Support team briefed on program details and FAQ
• Analytics dashboards confirmed working (referrals, conversions, rewards)
• Fraud monitoring active and alerting configured
• Referral link generation tested across all platforms (web, mobile, email)

Post-Launch (First 30 Days)

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Benchmarks

Key Metrics

K-Factor Calculation

K = i × c

Where:
  i = average number of invites sent per user
  c = conversion rate of those invites

Example:
  Each user sends 5 invites, 10% convert → K = 5 × 0.10 = 0.5
  (Each user brings 0.5 new users — not viral, but contributes to growth)

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Integration Points

Where to Surface Referrals in the Product

Tech Stack Considerations

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The best referral programs don't feel like marketing programs. They feel like one friend helping another discover something valuable. Design for that.

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Supporting file: retention-loops.md

Retention Loops — Engagement & Churn Prevention

Retention is the foundation of sustainable growth. No acquisition strategy survives a leaky bucket. This guide covers frameworks for building habit-forming products, predicting churn, and re-engaging lapsed users.

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Hook Model Framework

Nir Eyal's Hook Model explains how products create habitual usage through a four-step loop.

Trigger → Action → Variable Reward → Investment
   ↑                                      |
   └──────────────────────────────────────┘

Step 1: Trigger

Triggers prompt the user to take action. They come in two forms.

Goal: Start with external triggers, then associate the product with internal triggers through repeated positive experiences.

Step 2: Action

The simplest behavior done in anticipation of a reward. Follows BJ Fogg's behavior model: B = MAT (Behavior = Motivation + Ability + Trigger).

Step 3: Variable Reward

The reward must be variable (unpredictable) to maintain engagement over time. Fixed rewards lose their power.

Step 4: Investment

The user puts something into the product that makes it more valuable over time and increases switching costs.

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Churn Prediction Models

Leading Indicators of Churn

Identify users at risk before they cancel. These signals typically appear 2-4 weeks before churn.

Churn Risk Scoring Model

Risk Tiers:

• 80-100: Healthy — nurture and upsell
• 60-79: Monitor — proactive check-in
• 40-59: At risk — intervention required
• 0-39: Critical — immediate personal outreach

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Win-Back Sequences

Email Win-Back Flow

Win-Back Tactics by Churn Reason

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Cohort Analysis Methodology

Setting Up Cohort Analysis

Cohort Definition: Group users by sign-up week or month
Metric: Retention rate (% of cohort still active in period N)
Periods: Week 0, Week 1, Week 2, ... Week 12 (or Month 0-12)

Cohort Retention Table Template

What to Look For

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Habit Loop Design

Daily Active Usage Checklist

Design your product to support daily habits.

• There is a clear daily use case (not just a weekly/monthly tool)
• Users receive a meaningful trigger each day (notification, email digest, dashboard)
• The first action upon opening the product takes less than 5 seconds
• Variable content or data refreshes daily (new insights, updated feeds, fresh tasks)
• Completing the core action delivers immediate visible feedback
• Users invest something with each session (data, content, preferences)
• Streak or consistency tracking is visible (optional but powerful)
• Social elements create accountability (team visibility, shared goals)

Habit Formation Timeline

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Re-Engagement Triggers

Trigger Types and Timing

Re-Engagement Priority Matrix

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Retention Benchmarks by Business Model

Monthly Retention (% active after N months)

Net Revenue Retention Benchmarks (B2B SaaS)

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Customer Health Scoring

Health Score Components

Health Score Actions

Health Score Review Cadence

• Automated alerts for any account dropping below 50 (immediate)
• Weekly review of all accounts in Yellow or below by CS team
• Monthly health score trend analysis across full customer base
• Quarterly correlation analysis: health score vs actual churn outcomes (calibrate model)
• Semi-annual model refinement based on prediction accuracy

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Retention is not a feature. It is the cumulative result of delivering consistent value, building habits, and catching problems before users give up. Every percentage point of improved retention compounds into dramatically better unit economics.

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Supporting file: viral-loops.md

Viral Loops — Design & Optimization

A viral loop is a self-reinforcing cycle where existing users bring in new users through normal product usage. When designed well, each cohort of users generates the next.

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Viral Coefficient (K-Factor) Calculation

The K-factor measures how many new users each existing user generates.

K = i × c

Where:
  i = average number of invites (or exposures) sent per user
  c = conversion rate of those invites into new users

If K > 1.0 → Exponential (viral) growth
If K = 0.5-1.0 → Strong organic growth supplement
If K < 0.5 → Minimal viral contribution

K-Factor Examples

Improving K-Factor

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Viral Cycle Time

K-factor alone does not determine growth speed. Viral cycle time — the time it takes for one user to generate a new user — is equally critical.

Viral Cycle Time = Time from user sign-up → invite sent → invitee converts → invitee sends their own invite

Shorter cycle time = faster compounding, even with the same K-factor.

Tactics to Reduce Cycle Time

• Trigger invite prompts during onboarding, not after (move invite step earlier)
• Pre-compose invite messages (reduce effort to share)
• Send invite reminders if initial invites have not converted within 48 hours
• Offer instant rewards rather than delayed gratification
• Reduce sign-up friction for invitees (SSO, pre-filled forms)
• Enable real-time notifications when invitees take action

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Loop Design Patterns

Pattern 1: Inherent Viral Loops

The product requires multiple users to function. Inviting others is not optional; it is the product.

Pattern 2: Artificial Viral Loops

Incentives are added on top of the product to encourage sharing. The product works without sharing, but rewards make it appealing.

Pattern 3: Word-of-Mouth Loops

Users share because the product is remarkable, not because they are prompted or incentivized.

Pattern 4: Exposure / Embedded Loops

The product exposes itself to non-users as part of normal usage.

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Virality Assessment Framework

Score your product on each dimension (1-5) to assess viral potential.

Scoring:

• 32-40: Strong viral potential — invest heavily in loop optimization
• 24-31: Moderate potential — focus on 2-3 highest-scoring dimensions
• 16-23: Supplemental virality — viral loops will assist but not drive growth
• 8-15: Low viral potential — prioritize other acquisition channels

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Social Proof Loops

Social proof creates a secondary viral effect by making adoption visible and desirable.

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Content Virality Mechanics

When the product generates content, that content can become its own viral channel.

Content Viral Loop

User creates content → Content is shared/published → Viewer sees content →
Viewer notices product branding/CTA → Viewer signs up → New user creates content → ...

Optimization by Content Type

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Network Effects vs Viral Loops

These are related but distinct concepts. Understanding the difference matters for strategy.

Combined power: The strongest growth engines combine both. Slack has network effects (more teammates = more value) and viral loops (team invites expose new organizations).

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Optimizing Each Loop Step

Every viral loop has discrete steps. Optimize each one independently.

Step-by-Step Optimization

Funnel Benchmark Example

A/B Testing Priority for Viral Loops

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Viral growth is not magic. It is engineering. Each step in the loop is a conversion rate that can be measured, tested, and improved. Small improvements compound across the entire loop.

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Supporting file: skills/context-engine/platform-specs.md

Platform Specifications Reference

Last updated: 2026-02-11 This file is consumed by an AI agent to validate marketing content against platform requirements. All specs reflect current 2026 platform standards.

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Section 1: Social Media Platform Specs

Instagram

Feed Posts

Instagram Reels

Instagram Stories

Instagram Carousel

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TikTok

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LinkedIn

LinkedIn Post

LinkedIn Article

LinkedIn Newsletter

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Twitter/X

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Facebook

Facebook Post

Facebook Reels

Facebook Stories

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Pinterest

Standard Pin

Idea Pin

Video Pin

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YouTube

YouTube Video

YouTube Shorts

YouTube Live

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Reddit

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Threads

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Snapchat

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Section 2: Email Specifications

Subject Line Limits by Client

Recommendation: Keep subject lines under 40 characters for reliable cross-client mobile rendering. Front-load the most important words.

Preview Text (Preheader)

Recommendation: Write 40-90 characters of preview text. If not explicitly set, clients pull the first visible body text. Use hidden preheader text in HTML to control this.

Email Body Specs

Image Handling

CTA Button Specs

Dark Mode Considerations

Dark mode design rules:

• Add transparent padding around logos (prevents awkward color clashes)
• Use semi-transparent PNGs with contrasting edges
• Test with both dark and light backgrounds
• Define both light and dark color schemes in CSS where supported
• Avoid pure white (#FFFFFF) backgrounds; use #FAFAFA so dark mode detection triggers consistently

Mobile Rendering Rules

Apple Mail Privacy Protection (MPP)

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Section 3: Ad Platform Creative Specs

Google Ads

Responsive Search Ads (RSA)

Google Display Ads

Performance Max (PMax)

YouTube Ads

Google Shopping

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Meta Ads (Facebook & Instagram)

Feed Ads

Stories & Reels Ads

Carousel Ads

Collection Ads

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LinkedIn Ads

Sponsored Content (Single Image)

Sponsored Content (Video)

Sponsored Messaging (Message Ads)

Document Ads

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TikTok Ads

In-Feed Ads

TopView Ads

Spark Ads

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Pinterest Ads

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Amazon Ads

Sponsored Products

Sponsored Brands

Sponsored Display

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Section 4: Schema Markup Reference

Use schema.org structured data (JSON-LD format recommended) to enhance search appearance and enable rich results.

Schema Implementation Notes

• Format: Always use JSON-LD (recommended by Google) over Microdata or RDFa.
• Placement: Insert <script type="application/ld+json"> in the <head> or <body> of the page.
• Validation: Test with Google Rich Results Test (https://search.google.com/test/rich-results) and Schema Markup Validator (https://validator.schema.org).
• Nesting: Nest related schemas (e.g., Product containing AggregateRating and Offers).
• Avoid: Do not mark up content that is not visible on the page. Do not use schema for deceptive purposes. Google may issue manual actions for misleading structured data.

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Section 5: Image Format Guide

Format Comparison

Browser Support (as of 2026)

When to Use Each Format

Implementation Best Practice

Use the <picture> element with format fallback:

<picture>
  <source srcset="image.avif" type="image/avif">
  <source srcset="image.webp" type="image/webp">
  <img src="image.jpg" alt="Description" width="800" height="600" loading="lazy">
</picture>

Key rules:

• Always include width and height attributes to prevent Cumulative Layout Shift (CLS)
• Use loading="lazy" for below-the-fold images
• Use loading="eager" or fetchpriority="high" for LCP (Largest Contentful Paint) images
• Serve responsive sizes with srcset and sizes attributes for resolution switching
• Compress JPEG at quality 75-85 for web, WebP at quality 75-80, AVIF at quality 60-70
• Always strip EXIF metadata for web delivery (privacy and file size)