growth-engineering

Files (8)

• SKILL.md
• affiliate-marketing.md
• experimentation-frameworks.md
• launch-strategy.md
• product-led-growth.md
• referral-systems.md
• retention-loops.md
• viral-loops.md

SKILL.md (first lines)

# Growth Engineering

> **Script location.** If your host does not set `${CLAUDE_PLUGIN_ROOT}`, the scripts are in this plugin's `scripts/` folder, next to `skills/`. Run them with Python 3.10 or newer: `python`, or `python3` where `python` is not found (macOS, many Linux systems).

## 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.

From indranilbanerjee/digital-marketing-pro, MIT.

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