Implement Product-Led Growth Frameworks

This skill covers Product-Led Growth (PLG) frameworks - designing and implementing growth strategies where the product itself drives acquisition, activation, and expansion, covering PLG metrics, onboarding, freemium models, viral mechanics, and data-driven optimization. The PLG flywheel has four stages: Evaluate (reduce friction to trial), Activate (drive users to meaningful value quickly), Adopt (convert trial users to paying customers), and Expand (grow revenue through existing customers). Value-first approach: lead with product value before payment, design for time-to-value under 5 minutes, use progressive disclosure for advanced features, and implement contextual onboarding that teaches through usage.

The PLG metrics framework organizes primary metrics into four categories: acquisition (organic sign-up rate, virality coefficient/k-factor, cost per acquisition), activation (time to first value, Product Qualified Leads, activation rate), retention (day 1/7/30 retention, monthly active users, feature adoption rate), and expansion (Net Revenue Retention, expansion revenue rate, upsell conversion rate) - alongside key formulas for PQL score (a weighted sum of feature usage, engagement frequency, and user-role fit), the virality/k-factor (invitations per user times invitation conversion rate), and PLG efficiency (new MRR from product divided by product plus marketing investment).

User-onboarding optimization is shown via a progressive-onboarding state machine (Welcome - profile creation, max 3 steps; First Value - core value delivered within 5 minutes; Habit Formation - three sessions over 14 days; Expansion - advanced features triggered by activation) and an activation-event definition:

-- Example: Define activation for a collaboration tool
WITH user_actions AS (
  SELECT 
    user_id,
    MIN(created_at) as signup_date,
    COUNT(CASE WHEN action = 'invite_teammate' THEN 1 END) as invites_sent,
    COUNT(CASE WHEN action = 'create_project' THEN 1 END) as projects_created,
    COUNT(CASE WHEN action = 'collaborate_action' THEN 1 END) as collaborations
  FROM events 
  WHERE created_at >= signup_date 
    AND created_at <= signup_date + INTERVAL '14 days'
  GROUP BY user_id
)
SELECT 
  user_id,
  CASE 
    WHEN invites_sent >= 1 AND projects_created >= 1 AND collaborations >= 3 
    THEN 'activated' 
    ELSE 'not_activated' 
  END as activation_status
FROM user_actions;

Freemium strategy design covers a feature-gating framework (usage limits like seat, storage, and API-call caps, feature limits like advanced analytics and priority support, and time limits like trial duration) and a value-metric pricing function that scales a base price by usage, seat, and storage multipliers.

Viral-growth mechanics are shown via built-in sharing incentives (a collaboration trigger prompting team invitations with a premium-unlock incentive, content-sharing with a branded footer and engagement tracking, and a referral program rewarding both referrer and referee). The PLG experiment framework covers ICE scoring for prioritizing A/B tests (impact times confidence divided by ease) and a growth-experiment template (hypothesis, metric, minimum detectable effect, sample size, duration, control/treatment variants, and primary/secondary success criteria). Data instrumentation covers critical PLG events to track across the funnel: acquisition (signup, trial start), activation (first value achieved, onboarding completed), adoption (upgrade prompted, payment completed), and expansion (feature limit hit, teammate invited), each with relevant properties.

Common PLG anti-patterns to avoid: a premature paywall before demonstrating value, feature dumping that overwhelms users at once, generic one-size-fits-all onboarding, optimizing for vanity signup metrics over activation and retention, and friction-heavy trials requiring credit cards or lengthy forms upfront. An implementation roadmap spans three phases: foundation (months 1-2: core analytics, activation-event definition, basic self-serve onboarding), optimization (months 3-4: A/B testing onboarding, progressive feature disclosure, in-app upgrade prompts), and scale (months 5-6: viral and sharing mechanisms, pricing and packaging optimization, advanced segmentation and personalization).

Use it when designing or optimizing a product-led growth motion - defining the PLG metrics hierarchy, building progressive onboarding, designing freemium gating and pricing, adding viral mechanics, or prioritizing growth experiments. It is not a sales-led or enterprise-GTM playbook - it is scoped specifically to growth driven by the product itself rather than a sales team.

Given a product and its usage or account data, it produces a PLG metrics dashboard structure, an onboarding state machine, an activation-event definition, freemium gating and pricing logic, and a prioritized growth-experiment backlog.

Code samples span JavaScript (onboarding state machine, viral mechanics, PLG event tracking), SQL (activation-event definition), Python (value-based pricing function), and YAML (growth-experiment template).

Growth, product, and PLG teams designing or optimizing a product-led acquisition, activation, and expansion motion.