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:
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.If you need the full profile , read: ~/.claude-marketing/brands/{slug}/profile.jsonApply brand voice — Formality, energy, humor, authority levels must shape all content tone and word choicesCheck compliance — Auto-apply rules for brand's target_markets and industry using skills/context-engine/compliance-rules.mdReference industry benchmarks — Consult skills/context-engine/industry-profiles.md for the brand's industryUse platform specs — Reference skills/context-engine/platform-specs.md for character limits and format requirementsCheck campaign history — Run python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns before planning new workIf 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."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 businessBusiness model : Subscription, transactional, freemium, free-trial, advertising-supportedCurrent stage : Pre-launch, early traction (under 1,000 users), growth stage, scale stageKey metrics : Current MRR/ARR, user count, activation rate, retention rate, churn rate, NPSExisting growth channels : Which acquisition channels are active and their relative performanceViral potential : Whether the product has inherent sharing mechanics or requires artificial viralityTeam and resources : Engineering capacity for growth features, marketing budget, partnership resourcesTarget user : Who the ideal user is and what their primary motivation for using the product isCompetitive 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 triggersActivation metrics : Define the "aha moment," map the steps to reach it, measure and optimize activation rateTime-to-value optimization : Reduce friction between signup and first value experience through onboarding design, templates, sample data, and guided toursPQL scoring : Define product-qualified lead criteria based on usage patterns, feature adoption, team size, and engagement frequencySelf-serve expansion : In-product upgrade prompts, usage limit notifications, team invite flows, seat expansion triggersReverse 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 referralsMilestone referrals : Rewards triggered at referral count milestones (1, 5, 10, 25) to maintain momentumNPS-to-referral pipeline : Target promoters (NPS 9-10) with referral requests at the moment of highest satisfactionDouble-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 typesFraud 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 backSocial 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 significantlyViral 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 amplificationTier 2 launch (feature/update): Existing user announcement, targeted outreach, community posts, changelog, email campaignTier 3 launch (minor update): In-app notification, changelog update, social media postPre-launch waitlist : Viral waitlist mechanics (share to move up), early access incentives, drip content to maintain interestProduct Hunt launch : Preparation timeline (2-4 weeks), hunter selection, launch day playbook, post-launch engagementBeta 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 cadenceRe-engagement campaigns : Email sequences, push notifications, in-app messages, retargeting ads triggered by inactivity signalsChurn 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 offersCohort analysis : Track retention by signup cohort, acquisition channel, activation status, and feature adoption to identify what drives long-term retentionExpansion 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 rulesNetwork selection : When to use affiliate networks (ShareASale, CJ, Impact) vs building a custom programAffiliate recruitment : Finding high-quality affiliates through competitor analysis, content partnerships, and niche community outreachCommission optimization : Balancing commission rates to attract affiliates while maintaining profitability; performance tiers to reward top performersFraud detection : Click fraud, cookie stuffing, brand bidding violations, trademark misuse, coupon abuseContent affiliate strategy : Working with bloggers, review sites, comparison sites, and niche publishersAffiliate-influencer hybrids : Creator partnerships with performance-based compensation models
Process#
PLG Implementation (Most Common Use Case)#
Map the user journey -- Document every step from first awareness to paid conversion. Identify where users currently drop off and where they experience value.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.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.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.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.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.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#
Assess viral potential -- Determine whether the product has inherent sharing mechanics or requires incentive-driven referrals. Analyze NPS data to identify promoter concentration.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.Build referral mechanics -- Create unique referral links, sharing interfaces, tracking infrastructure, and reward fulfillment flows. Make sharing frictionless (one-click, pre-written messages).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.Launch and promote -- Announce the program to existing users, feature it in onboarding, add it to account dashboards, and include it in email communications.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 modelsreferral-systems.md -- Referral program templates, incentive design principles, fraud prevention tactics, and case studiesviral-loops.md -- Viral coefficient calculations, loop design patterns, virality assessment frameworkslaunch-strategy.md -- Tier 1/2/3 launch playbooks, Product Hunt guide, waitlist mechanics, beta program designretention-loops.md -- Engagement frameworks, churn prediction models, winback sequences, cohort analysis methodsaffiliate-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 metricsReferral program spec : Incentive structure, mechanics description, integration points, launch plan, and monitoring dashboard requirementsLaunch playbook : Pre-launch timeline, launch day checklist, channel-by-channel activation plan, post-launch follow-up sequenceRetention analysis : Cohort retention curves, churn risk indicators, re-engagement campaign designs, and winback sequence templatesGrowth model : Spreadsheet-ready model with viral coefficient, cycle time, channel-level CAC, retention curves, and projected growth trajectoriesAffiliate 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 timeCRO -- Activation and onboarding optimization overlaps heavily with CRO principlesContent Engine -- Content marketing that feeds growth loops and launch amplificationAnalytics & Insights -- Cohort analysis, experiment measurement, and growth modelingInfluencer & Creator Marketing -- Creator partnerships for launch amplification and affiliate-influencer hybridsFunnel Architect -- Growth engineering strategies plug into the broader acquisition and retention funnel
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.
Program Setup Guide#
Platform Selection#
Choose between building in-house or using an affiliate network/platform.
Approach Pros Cons Best For In-house (custom build) Full control, no platform fees, custom attribution Engineering investment, slower to launch, limited affiliate discovery Companies with dev resources and existing partner relationships Affiliate SaaS platform Fast launch, built-in tracking, affiliate management tools Monthly fees, some customization limits Most companies launching their first program Affiliate network Access to large affiliate pool, built-in compliance, payment handling Higher fees (network override), less control, commoditized Companies wanting rapid scale through established affiliates Hybrid Combine in-house tracking with network distribution Complexity of managing multiple systems Mature programs optimizing for both control and reach
Program Setup Checklist#
Network Comparison#
Feature ShareASale Impact CJ Affiliate Awin PartnerStack Best for SMB, e-commerce Enterprise, SaaS Large brands, retail Global programs B2B SaaS Setup cost $625 one-time + $35/mo Custom pricing Custom pricing $5,000+ setup Custom pricing Network fee 20% of commissions Negotiable Negotiable Negotiable Negotiable Affiliate pool size 270,000+ 100,000+ 170,000+ 240,000+ 65,000+ Tracking quality Good Excellent Good Good Excellent SaaS features Basic Advanced (partnerships) Moderate Moderate Advanced (PRM) Reporting Standard Advanced Standard Standard Advanced Global support US-focused Global Global Global (EU strong) Global Payment handling Included Included Included Included Included Cookie duration Configurable Configurable Configurable Configurable Configurable Integration ease Easy (Shopify, WP) Moderate Moderate Moderate Easy (SaaS stack)
Selection Criteria#
Your Situation Recommended Platform E-commerce, Shopify store, budget-conscious ShareASale Enterprise SaaS, complex partnership models Impact Large brand, want established affiliate relationships CJ Affiliate European or global audience Awin B2B SaaS, want partner relationship management PartnerStack Need full control and have engineering resources In-house + Rewardful, FirstPromoter, or Tapfiliate
Commission Optimization#
Commission Models#
Model How It Works Typical Rate Best For CPA (Cost Per Acquisition) Flat fee per converted customer $20-$200+ depending on ACV SaaS with predictable LTV, defined conversion event Revenue share (recurring) Percentage of customer revenue, ongoing 15-30% recurring SaaS with monthly subscriptions, long LTV Revenue share (one-time) Percentage of first purchase only 20-50% of first payment E-commerce, one-time purchases Tiered commission Rate increases with volume Base rate + escalators at thresholds Motivating top affiliates to increase volume Hybrid (CPA + RevShare) Flat fee upfront + smaller ongoing percentage $50 CPA + 10% recurring Balancing affiliate motivation with program economics Performance bonuses Additional payouts for hitting milestones Varies Driving behavior during specific campaigns or periods
Commission Rate Calibration#
Factor Lower Commission Higher Commission Customer LTV Low LTV (<$500) High LTV (>$2,000) Conversion rate High (affiliates convert easily) Low (hard to convert, need incentive) Competition for affiliates Few competitors recruiting Many programs competing for same affiliates Product awareness Well-known brand Unknown brand needing introduction Sales cycle length Short (same-session purchase) Long (multi-touch, weeks/months)
Tiered Commission Structure Example#
Tier Monthly Conversions Commission Rate Bonus Bronze 1-10 20% None Silver 11-25 25% $100 monthly bonus Gold 26-50 30% $300 monthly bonus + dedicated account manager Platinum 51+ 35% $500 monthly bonus + co-marketing budget + quarterly strategy call
Affiliate Recruitment Strategies#
Affiliate Segments#
Segment Description Volume Quality Recruitment Approach Content creators / Bloggers Write reviews, comparisons, tutorials Medium High Outreach based on existing content in your niche YouTube / Video creators Product reviews, tutorials, unboxings Medium High Identify creators reviewing competitors Email list owners Promote via newsletters High Medium Look for niche newsletters with engaged audiences Coupon / Deal sites List offers and discount codes Very High Low Selectively recruit top-tier sites only Comparison / Review sites Rank and compare products Medium Very High Ensure accurate listing, offer exclusive data Social media influencers Promote on Instagram, TikTok, Twitter High Variable See Affiliate vs Influencer section Niche community leaders Promote within forums, groups, communities Low Very High Build relationship first, offer program second Existing customers Happy users who refer through affiliate links Low Highest Invite top NPS respondents into affiliate program Agencies / Consultants Recommend to their clients Low Very High Partner program with higher commissions
Recruitment Outreach Process#
Step Action Timeline 1 Identify target affiliates (search for niche content, competitor mentions) Ongoing 2 Research each affiliate (audience size, content quality, engagement rate) Before outreach 3 Personalize outreach (reference specific content, explain why your product fits) Individual emails 4 Follow up if no response (2 follow-ups, 5-7 days apart) +5 and +12 days 5 Onboard accepted affiliates (send welcome kit, schedule intro call for top-tier) Within 48 hours 6 Provide first 30-day support (check in at Day 7 and Day 21, offer content ideas) First month 7 Review performance and optimize (adjust commission, provide better assets) Monthly
Fraud Detection#
Common Affiliate Fraud Types#
Fraud Type Description Detection Method Cookie stuffing Affiliate drops cookies without user knowledge to claim attribution Monitor conversion paths — flag conversions with no click event Click fraud / Click spam Generating fake clicks to inflate metrics or steal attribution Abnormal click-to-conversion ratios, geographic anomalies Trademark bidding Affiliates bid on your brand terms in paid search Regular SEM monitoring, brand + affiliate keyword alerts Incentivized traffic Offering users cash/points to sign up through affiliate link Low retention rates from affiliate cohort, high refund rates Self-referral Affiliate signs up using their own link Cross-reference affiliate and customer data Fake leads / form fills Submitting bogus information to trigger CPA payouts Lead quality scoring, email verification, phone verification Return abuse Purchase through affiliate link, then return after commission paid Extend commission hold period past return window
Fraud Prevention Checklist#
Content Guidelines & Brand Protection#
Affiliate Content Policy#
Allowed Restricted Prohibited Honest product reviews Income claims without disclosure False or misleading claims Feature comparisons Direct competitor disparagement Trademark misuse in domain names Tutorial and how-to content Pricing guarantees (price may change) Spam (unsolicited email, comment spam) Use of approved brand assets Modified logos or brand imagery Coupon codes not officially issued FTC-compliant disclosure Hidden affiliate relationships Cookie stuffing or click fraud Social media promotion Implied endorsement by company Trademark PPC bidding (if restricted)
Brand Protection Checklist#
Program Management#
Monthly Management Tasks#
Task Frequency Time Investment Review affiliate applications Weekly 1-2 hours Monitor top affiliate performance Weekly 1 hour Process commission payouts Monthly (net-30) 1-2 hours Audit for fraud and compliance Monthly 2-3 hours Refresh creative assets and offers Monthly 2-4 hours Affiliate newsletter / communication Bi-weekly or monthly 1-2 hours Recruit new affiliates Ongoing 3-5 hours/week Optimize commission structure Quarterly 2-3 hours Performance review and reporting Monthly 2-3 hours
Key Program Metrics#
Metric Formula Good Great Active affiliate rate Affiliates with 1+ conversion / Total affiliates 10-20% 20%+ Revenue per affiliate Total affiliate revenue / Active affiliates Varies Top 20% drive 80% of revenue Affiliate CAC Total affiliate costs / Affiliate-driven customers <50% of paid CAC <30% of paid CAC Affiliate contribution Affiliate revenue / Total revenue 10-15% 15-25% Average commission rate Total commissions / Total affiliate revenue 15-25% Optimized per tier Time to first conversion Median days from affiliate activation to first sale <30 days <14 days Affiliate retention rate Affiliates active this quarter / Active last quarter 50-60% 70%+
Affiliate vs Influencer Distinction#
Dimension Affiliate Influencer Compensation Performance-based (commission on sales/leads) Flat fee, product gifting, or hybrid (fee + commission) Content style Review, comparison, tutorial, deal-focused Lifestyle, narrative, brand integration Measurement Tracked conversions, revenue, ROI Impressions, engagement, brand lift, tracked conversions Relationship Transactional, scalable, many affiliates Relational, curated, fewer partnerships Control over content Low — affiliate creates independently Moderate — briefs and approval process Timeline Ongoing, evergreen Campaign-based, time-bound Discovery Affiliate networks, competitor analysis Social platforms, influencer databases Best for Driving measurable conversions at scale Building brand awareness and trust with specific audiences Risk Brand compliance, fraud Off-brand content, audience mismatch
When to Use Each#
Scenario Use Affiliate Use Influencer Use Both Driving direct sales, clear ROI needed Yes Building brand awareness in new market Yes Product launch with sustained promotion Yes Scaling a proven acquisition channel Yes Reaching a specific niche audience Yes Long-term evergreen content strategy Yes Seasonal campaign with urgency Yes Mature program optimizing all channels Yes
Hybrid Model#
Many programs blend affiliate and influencer approaches:
TEXT · 3 LINES Copy 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 conversionsA 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.
Supporting file: experimentation-frameworks.md#
Growth Experimentation — Frameworks & Methodology Reference#
Experiment Prioritization Frameworks#
ICE Score (Impact, Confidence, Ease)#
Dimension Scale Definition Impact 1-10 How much will this move the target metric? Confidence 1-10 How confident are you in the predicted impact? (based on data, research, or precedent) Ease 1-10 How easy is this to implement? (time, resources, dependencies)
TEXT · 14 LINES Copy 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 oneBest for: Small teams, fast-moving startups, early-stage experimentation programs. Quick to score, easy to debate.
RICE Score (Reach, Impact, Confidence, Effort)#
Dimension Scale Definition Reach # of users/month How many people will this affect in a given time period? Impact 0.25 / 0.5 / 1 / 2 / 3 Minimal / Low / Medium / High / Massive impact per person Confidence 50% / 80% / 100% How certain are you? Low / Medium / High Effort Person-months Total work required (design + dev + QA)
TEXT · 16 LINES Copy 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 = 6,400Best for: Product teams with clear user data, growth teams needing to justify investment to stakeholders.
PIE Score (Potential, Importance, Ease)#
Dimension Scale Definition Potential 1-10 How much room for improvement exists? (based on current performance vs benchmarks) Importance 1-10 How valuable is the traffic/audience affected? (high-value pages score higher) Ease 1-10 How easy is the test to implement?
TEXT Copy PIE Score = (Potential + Importance + Ease) / 3Best for: CRO teams focused on website optimization, where you're comparing pages/funnels against each other.
When to Use Each Framework#
Framework Best Scenario Weakness ICE Quick prioritization, small teams, many ideas Subjective, no quantified reach RICE Data-rich environments, product teams Requires user reach data, slower to calculate PIE CRO and website optimization Limited to conversion optimization context
Hypothesis Format#
Every experiment must start with a falsifiable hypothesis. A vague "let's try this" is not an experiment.
Standard Hypothesis Template#
TEXT · 4 LINES Copy 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#
Hypothesis Strength "If we add customer testimonials to the pricing page, then the pricing-page-to-signup conversion rate will increase by 10-15% because user research shows that 68% of prospects cite 'lack of social proof' as their top hesitation." Specific metric, grounded in research, realistic magnitude "If we reduce the signup form from 7 fields to 3 (email, name, password), then the form completion rate will increase by 25-40% because our analytics show a 60% drop-off between fields 3 and 7." Data-backed reasoning, clear change, measurable outcome "If we send cart abandonment emails within 1 hour instead of 24 hours, then the cart recovery rate will increase by 15-20% because industry data shows email engagement drops 50% after the first hour." Industry benchmark as reasoning, testable timing change
Common Hypothesis Mistakes#
Mistake Example Fix No specific metric "Making the page better will improve performance" Define which metric and by how much No reasoning "If we change the button to green, conversions will increase" Add the "because" — what evidence supports this? Untestable "If we rebuild the entire product, users will be happier" Scope to a testable, isolated change No magnitude "Conversion rate will increase" Estimate a range: "by 5-10%" Multiple changes "If we change the headline, image, CTA, and layout..." Test one variable at a time, or use multivariate design
Experiment Types#
Type What It Is When to Use Complexity A/B test Two variants (control vs challenger) on the same page/element Testing a single change with sufficient traffic Low A/B/n test Multiple variants (3+) against a control Testing several ideas for the same element Medium Multivariate (MVT) Multiple elements changed simultaneously, all combinations tested Understanding interaction effects between elements High Split URL test Traffic split between entirely different page URLs Testing fundamentally different page designs Medium Feature flag New feature exposed to a percentage of users Product changes, gradual rollouts Medium Holdout test Suppress a feature/campaign from a control group Measuring incremental impact of an existing feature Low-Medium Sequential test Run variant A for a period, then variant B When traffic is too low for simultaneous split Low (but less reliable) Bandit (explore/exploit) Algorithm allocates traffic toward better-performing variant dynamically When you want to minimize opportunity cost during the test High
A/B Test Decision Criteria#
TEXT · 6 LINES Copy 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 holdoutStatistical Foundations#
Sample Size Calculation#
Before running any test, calculate the required sample size to avoid inconclusive results.
Input Definition How to Estimate Baseline conversion rate Current conversion rate of the control Use last 30-60 days of data Minimum Detectable Effect (MDE) Smallest improvement worth detecting Typically 5-20% relative change Statistical significance Probability of avoiding false positives Standard: 95% (alpha = 0.05) Statistical power Probability of detecting a true effect Standard: 80% (beta = 0.20)
Sample Size Reference Table (95% significance, 80% power)#
Baseline CVR 5% Relative MDE 10% Relative MDE 20% Relative MDE 1% 3,070,000 per variant 770,000 per variant 193,000 per variant 2% 1,500,000 376,000 94,500 5% 580,000 146,000 36,700 10% 275,000 69,400 17,500 20% 125,000 31,500 8,000 50% 38,000 9,600 2,500
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#
TEXT · 8 LINES Copy 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#
Pitfall What Happens How to Avoid Peeking at results Checking daily and stopping early when results look good inflates false positive rate to 20-30% Pre-commit to sample size and duration; only evaluate at the end Underpowered tests Test ends with "no significant result" but sample was too small to detect real effect Calculate sample size before starting; do not run tests you cannot power Multiple comparisons Testing 10 variants without adjustment means ~40% chance of false positive Apply Bonferroni correction or use sequential testing methods Novelty effect New variant performs well initially because it is unfamiliar, then regresses Run tests for at least 2 full weeks; monitor for regression in week 2-3 Selection bias Non-random traffic split (e.g., different time periods or geographies) Use proper randomization; verify that control/treatment demographics match Simpson's Paradox Overall result is flat, but segments show opposite effects that cancel out Always segment results by device, traffic source, new vs returning
Growth Experiment Categories (AARRR Framework)#
Acquisition Experiments#
Experiment Metric Example Channel testing CAC by new channel Test Reddit ads for B2B audience vs LinkedIn Landing page variants Landing page CVR Test long-form vs short-form landing page Ad creative testing CTR, CPA Test benefit-focused vs pain-focused ad copy Referral mechanics Referral conversion rate Test "Give $20, Get $20" vs "Give 1 month free, Get 1 month free" SEO content format Organic traffic, time on page Test comprehensive guide vs comparison post for same keyword Partnership channels Qualified leads from partners Test co-webinar vs guest blog vs integration marketplace listing Outbound messaging Reply rate Test personalized video vs text-only cold email
Activation Experiments#
Experiment Metric Example Onboarding flow Activation rate (Day 7) Test guided setup wizard vs self-serve with tooltips First-value acceleration Time-to-value Test pre-populated templates vs empty state Signup friction Signup completion rate Test social login vs email-only signup Welcome email sequence Day 7 engagement Test 5-email sequence vs 3-email sequence Personalized onboarding Feature adoption rate Test role-based onboarding paths vs generic Empty state design First-action completion Test sample data vs "create your first [item]" prompt
Retention Experiments#
Experiment Metric Example Engagement triggers Weekly active user rate Test push notification with insight vs generic reminder Re-engagement campaigns Reactivation rate Test incentive email vs product update email for dormant users Feature stickiness Feature retention at Day 30 Test in-app tip sequence vs video tutorial Communication cadence 30-day retention Test weekly digest vs real-time notifications Habit loop design Session frequency Test streak mechanics vs progress bar Community features 90-day retention Test forum access vs peer group matching
Revenue Experiments#
Experiment Metric Example Pricing page design Pricing-page-to-purchase CVR Test 3-tier vs 2-tier pricing layout Upgrade triggers Free-to-paid conversion Test in-app usage limit popup vs email upgrade prompt Upsell timing Expansion revenue per account Test upgrade prompt at feature limit vs after 30 days Annual vs monthly framing Annual plan selection rate Test "Save 20%" vs "2 months free" messaging Cross-sell placement Add-on attach rate Test post-purchase page vs in-cart recommendation Price anchoring AOV Test showing enterprise tier first vs starter tier first
Referral Experiments#
Experiment Metric Example Incentive structure Referral send rate Test double-sided vs single-sided reward Referral placement Referrals per user Test post-purchase prompt vs account settings page Social sharing Share rate Test pre-written social post vs custom message Referral messaging Referral conversion rate Test "Share and save" vs "Give your friend a gift" Timing of referral ask Referrals per activated user Test ask at activation vs ask after first value milestone
Experiment Tracking#
Experiment Log Template#
Field Description Example Experiment ID Unique identifier EXP-2025-042 Name Descriptive experiment name "Pricing page social proof test" Owner Person responsible Sarah Chen Hypothesis Full hypothesis statement "If we add 3 customer logos and review scores to the pricing page..." Primary metric The one metric this experiment targets Pricing page → signup conversion rate Secondary metrics Additional metrics to monitor for side effects Time on pricing page, support ticket volume Guardrail metrics Metrics that must NOT degrade Overall site conversion rate, revenue per visitor Variant description What the challenger variant changes Add logo bar + 3 review scores above pricing table Traffic allocation % of traffic to each variant 50/50 control/variant Required sample size Pre-calculated sample per variant 35,000 per variant Start date When the experiment goes live 2025-03-01 Planned end date When sample size will be reached 2025-03-28 Actual end date When the experiment was actually stopped 2025-03-30 Result Won / Lost / Inconclusive Won Lift Measured change in primary metric +12.4% (95% CI: +6.1% to +18.7%) Statistical significance P-value or confidence level p = 0.003 (99.7% confidence) Decision Ship / Iterate / Kill Ship to 100% Key learning What was learned regardless of outcome Social proof near pricing decisions significantly reduces hesitation
Experiment Status Board#
Status Definition Color Backlog Hypothesis written, not yet prioritized Gray Prioritized Scored and scheduled for upcoming sprint Blue In Development Being built/designed/configured Yellow Running Live and collecting data Green Analysis Data collection complete, being analyzed Orange Decided Decision made (ship/kill/iterate) Purple Shipped Winning variant rolled out to 100% Dark Green
Experimentation Velocity#
Benchmark Velocity Targets#
Team Size Target Experiments/Month Notes Solo growth marketer 2-4 Focus on high-impact, easy-to-implement tests Growth team (2-3) 4-8 Mix of quick wins and deeper experiments Growth team (4-6) 8-15 Run parallel experiments across funnel stages Dedicated experimentation team 15-30 Full experimentation infrastructure and culture
How to Increase Velocity#
Lever Implementation Reduce experiment scope Test one variable, not redesigns; smaller scope = faster cycles Pre-built test templates Standardize experiment setup in your testing tool Hypothesis backlog Maintain a scored backlog so tests are ready when a slot opens Parallel testing Run experiments on different pages/funnels simultaneously (no overlap) Automated analysis Set up auto-reporting when experiments reach significance Learning documentation Avoid re-running failed experiments by documenting learnings Reduce approval bottlenecks Empower growth team to launch tests without executive approval
Learning from Failures#
Why Experiments Fail (and What to Learn)#
Failure Type What Happened What to Learn Inconclusive (no winner) Neither variant significantly outperformed Your change was too small to matter, MDE was too tight, or the sample was too small Negative result (variant lost) Challenger performed worse than control The hypothesis was wrong — but now you know. Document why and test a different approach Execution failure Test was misconfigured, traffic not split properly Improve QA process for experiment setup External contamination Seasonal effect, site outage, or marketing campaign skewed results Run tests for full weekly cycles; exclude known anomaly periods Metric moved but business didn't Primary metric improved but revenue/retention didn't follow You optimized for the wrong metric — revisit metric selection
Post-Experiment Analysis Template#
TEXT · 20 LINES Copy 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]Growth Model: Inputs to Compound Growth#
The Growth Equation#
TEXT · 6 LINES Copy 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#
Stage Input Metric Current Target Lever Experiment Ideas Acquisition Monthly new signups 2,000 2,500 New channels, referrals Reddit ads test, referral program launch Activation 7-day activation rate 35% 45% Onboarding, first-value Guided wizard, pre-built templates Retention 30-day retention 60% 70% Engagement, habit loops Weekly insight email, streak feature Revenue Free-to-paid conversion 5% 7% Pricing, upgrade prompts Pricing page redesign, in-app limits Referral Referrals per activated user 0.3 0.5 Incentives, sharing mechanics Double-sided rewards, social sharing
Common Growth Experiments by Business Model#
SaaS#
Stage High-Impact Experiment Acquisition Free tool or calculator that captures emails and demonstrates product value Activation Role-based onboarding flow that shows relevant features first Retention Weekly email with personalized usage insights and "try this feature" prompts Revenue In-app modal at usage limit showing upgrade value with social proof Referral "Invite your team" prompt after activation milestone with free seat for referrer
eCommerce#
Stage High-Impact Experiment Acquisition Quiz/recommendation engine as top-of-funnel content play Activation First-purchase discount tied to email signup (10% off first order) Retention Post-purchase replenishment email timed to product consumption cycle Revenue Dynamic bundle recommendations on product pages ("Complete the look") Referral Post-purchase "give $15, get $15" referral card in shipping box
Marketplace#
Stage High-Impact Experiment Acquisition (supply) Automated seller onboarding that imports listings from competitor platform Acquisition (demand) SEO-optimized category pages targeting "[product] near me" queries Activation First transaction incentive for both buyer and seller (subsidized) Retention Personalized weekly digest of new listings matching buyer's search history Revenue Tiered seller plans with premium placement and analytics Referral Seller referral program with reduced commission for referred sellers
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.
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.
Three-Tier Launch Framework#
Not every release deserves the same investment. Categorize launches to allocate effort appropriately.
Dimension Tier 1: Major Launch Tier 2: Feature Launch Tier 3: Minor Update Scope New product, major rebrand, platform shift Significant new capability or module Bug fixes, UI tweaks, incremental improvements Lead time 8-12 weeks 4-6 weeks 1-2 weeks Cross-functional teams Product, Marketing, Sales, CS, PR, Leadership Product, Marketing, Growth Product, Marketing External PR Press embargo, media outreach, analyst briefing Blog post, select media pitches Changelog entry, in-app notification Customer communication Email to full list, webinar, demo event Email to relevant segments, in-app announcement In-app tooltip or banner Sales enablement New pitch deck, battle cards, training sessions Feature one-pager, FAQ document Internal Slack update Content assets Landing page, video, case study, blog series Blog post, help docs, short video Help doc update Success metrics Sign-ups, revenue impact, press coverage, NPS Feature adoption rate, engagement lift Bug resolution rate, satisfaction score Examples Figma launching FigJam, Notion launching AI Slack launching Huddles Dashboard loading speed improvement
Product Hunt Launch Guide#
Pre-Launch Preparation (2-4 Weeks Before)#
Task Details Timeline Research top launches in your category Study taglines, descriptions, and first comments of successful launches -4 weeks Recruit a credible Hunter Find someone with 1,000+ followers on Product Hunt; reach out personally -3 weeks Build a supporter list 200+ people who will upvote, comment, and share on launch day -3 weeks Prepare assets Thumbnail (240x240), gallery images (5-8 screenshots), video (optional but recommended) -2 weeks Write tagline and description Tagline: 60 chars max, clear and punchy. Description: problem → solution → proof -2 weeks Draft first comment Founder story, why you built this, what makes it different. Personal and authentic. -1 week Prepare launch day team Assign roles: comment responder, social media, supporter coordinator -1 week
Launch Day Execution#
Post-Product Hunt#
Waitlist Mechanics#
Waitlist Strategy#
Component Standard Waitlist Viral Waitlist VIP Waitlist Sign-up Email only Email + share to move up Application with qualification criteria Position visibility Hidden or shown Shown with "move up" mechanic Acceptance/rejection notification Incentive to share None Higher position for referrals Early access for qualified applicants Communication Periodic updates Real-time position updates Personalized outreach Best for Simple interest capture Pre-launch buzz generation High-end or limited-capacity products Tools Mailchimp, ConvertKit Viral Loops, Waitlist.me, ReferralHero Typeform + custom CRM workflow
Waitlist Email Sequence#
Email Timing Content Welcome Immediate Confirm position, explain what they're getting, set expectations Social proof Day 3 "X people joined after you" — create urgency Behind the scenes Day 7 Founder story, product preview, build connection Referral nudge Day 10 "Move up the list by inviting friends" (for viral waitlists) Sneak peek Day 14+ Exclusive preview content, screenshots, or demo Access granted Launch day Clear CTA, onboarding instructions, limited-time offer
Beta Program Design#
Beta Types#
Beta Type Audience Duration Feedback Method Closed Alpha Internal team + advisors 2-4 weeks Direct Slack/meetings Private Beta Hand-picked power users (50-200) 4-8 weeks In-app feedback widget, weekly surveys Open Beta Anyone who signs up 2-4 weeks Community forum, in-app feedback, analytics Dogfooding Entire company uses product daily Ongoing Internal bug reports, feature requests
Beta Feedback Collection#
Pre-Launch Content Sequence#
Build momentum in the weeks before launch with a structured content calendar.
Week Content Type Channel Goal -6 Problem-awareness blog post Blog, LinkedIn, Twitter Establish the problem you solve -5 Industry data or original research Blog, email list Build authority and attract attention -4 Founder story / origin narrative Twitter thread, LinkedIn post Build personal connection -3 Teaser video or product preview Social media, email list Generate curiosity and anticipation -2 Early testimonials from beta users Social media, landing page Social proof before launch -1 "Launching next week" countdown Email list, social media Convert awareness into intent Launch Full launch announcement All channels simultaneously Maximum coordinated impact
Launch Day Checklist#
Product Readiness (Complete Before Launch)#
Marketing Assets Ready#
Team Coordination#
Distribution Execution#
Monitoring and Response#
Post-Launch Optimization#
First 7 Days#
Day Focus Actions 1 Respond and stabilize Fix critical bugs, answer every comment and email, monitor server load 2 Analyze first cohort Review Day 1 sign-up → activation funnel, identify drop-off points 3 Iterate on onboarding Fix top 3 friction points in sign-up/activation flow 4 Amplify what works Double down on highest-performing channels and messages 5 Engage early users Personal outreach to first 50-100 users, collect feedback 6 Content follow-up Publish "What we learned from launch" post, share early metrics 7 Week 1 retrospective Full team review of metrics, wins, failures, and priorities for Week 2-4
First 30 Days#
Week Focus Area Key Actions Week 1 Stabilize and learn Bug fixes, onboarding optimization, user interviews Week 2 Optimize conversion A/B test landing page, improve activation flow, refine messaging Week 3 Expand reach Launch on additional channels, activate referral program, begin SEO Week 4 Retention focus Analyze Day 7 and Day 14 retention, build engagement loops, plan next release
Launch Metric Tracking#
Dashboard Metrics#
Metric Source Tracking Frequency Unique visitors Google Analytics / Plausible Hourly on launch day, daily after Sign-ups Product database Hourly on launch day, daily after Activation rate Product analytics (Mixpanel, Amplitude) Daily Sign-up → Activation time Product analytics Daily Channel attribution UTM parameters, referral data Daily Revenue (if applicable) Stripe, payment processor Daily Support tickets Help desk (Zendesk, Intercom) Hourly on launch day NPS / CSAT Survey tool Weekly starting Day 7 Social mentions Brand monitoring (Mention, Brandwatch) Hourly on launch day Press coverage Media monitoring Daily for first 2 weeks
Launch Scorecard Template#
KPI Target Actual Status Day 1 sign-ups ___ ___ On track / Behind / Exceeded Week 1 sign-ups ___ ___ Week 1 activation rate ___ ___ Month 1 sign-ups ___ ___ Month 1 activation rate ___ ___ Month 1 revenue ___ ___ CAC (by channel) ___ ___ Press articles ___ ___ Product Hunt ranking ___ ___ NPS at Day 30 ___ ___
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.
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.
PLG Flywheel#
The PLG engine follows a four-stage flywheel. Each stage feeds the next.
TEXT · 3 LINES Copy Value Delivery → Habit Formation → Expansion → Advocacy
↑ |
└──────────────────────────────────────────┘Stage Objective Key Lever Example Value Delivery Get user to "aha moment" fast Frictionless onboarding Slack — send first message in <2 min Habit Formation Embed product into daily workflow Triggers + variable rewards Notion — daily workspace opens Expansion Grow revenue within accounts Usage-based pricing, seat expansion Figma — designer invites developer Advocacy Turn users into acquisition channels Referral loops, social proof Calendly — every invite link = marketing
Freemium vs Free Trial Decision Tree#
Use this framework to select the right model for your product.
Factor Favors Freemium Favors Free Trial Time-to-value Long (user needs weeks to see ROI) Short (value apparent within days) Marginal cost per user Near zero Meaningful infrastructure cost Network effects Strong (more users = more value) Weak or absent Product complexity Low — self-explanatory UI High — requires setup, training Competitive landscape Crowded — need to remove risk Differentiated — value is clear Virality potential High (free users spread product) Low (usage is private/internal) Average deal size Low ACV (<$5K/yr) High ACV (>$15K/yr) Sales involvement Minimal — self-serve dominant Required — consultative sale
Hybrid approach: Offer freemium for individuals and free trials for team/enterprise tiers. This captures both bottoms-up adoption and top-down evaluation.
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#
Pull behavioral data — Export event logs for the first 7-14 days of all usersSegment by outcome — Split users into retained (active at Day 30+) vs churnedCompare behaviors — Identify actions that retained users performed at significantly higher ratesRank by correlation — Find the action with the strongest correlation to retentionValidate causation — Run an experiment: guide new users toward that action and measure retention liftSet the threshold — Define the minimum frequency or depth (e.g., "created 3 projects in first 7 days")
Activation Metric Examples#
Product Activation Metric Threshold Slack Messages sent in a channel 2,000 team messages Dropbox File saved in Dropbox folder 1 file in first session HubSpot Contacts imported + email sent Within first 7 days Zoom Hosted a meeting with 2+ participants First 48 hours Figma Created and shared a design file First 7 days
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#
Signal Category Weight Examples Activation completion 25% Completed onboarding, hit aha moment Usage depth 25% Features used, frequency, session duration Usage breadth 15% Number of team members active, departments involved Growth signals 20% Seat additions, hitting plan limits, API usage Firmographic fit 15% Company size, industry, tech stack match
Scoring Tiers#
Tier Score Range Action Hot PQL 80-100 Sales outreach within 24 hours Warm PQL 60-79 Automated nurture + contextual in-app upgrade prompts Developing PQL 40-59 Product-led nurture sequences, feature discovery nudges Early PQL 0-39 Onboarding optimization, activation campaigns
Self-Serve Onboarding Optimization#
Onboarding Checklist#
Onboarding Anti-Patterns#
Anti-Pattern Why It Hurts Fix Feature tour on first login Overwhelms user before they have context Defer tours until relevant feature is needed Mandatory profile completion Adds friction before value delivery Make optional, ask progressively No segmentation Generic experience misses user needs Ask role/goal upfront, customize flow Long email verification loop Delays activation by hours or days Allow limited access immediately, verify later Hiding the upgrade path Users can't self-serve to paid Show pricing contextually at limit moments
PLG Metrics Dashboard#
Primary Metrics#
Metric Formula Benchmark (B2B SaaS) Activation Rate Activated users / Sign-ups 20-40% Time-to-Value (TTV) Median time from sign-up to activation <5 minutes (ideal), <24 hours (acceptable) Free-to-Paid Conversion Paid users / Free users 2-5% (freemium), 10-25% (free trial) Expansion Revenue (% of ARR) Expansion MRR / Starting MRR >30% Net Revenue Retention Viral Coefficient (K-factor) Avg invites per user x invite conversion rate >0.5 (good), >1.0 (viral) Revenue Per User (RPU) Total revenue / Active users Varies — track trend over time Natural Rate of Growth (NRG) Annual growth rate from organic + PLG channels >50% = strong PLG motion
Cohort Tracking#
Track these for each weekly or monthly sign-up cohort:
PLG for Different Business Models#
Business Model PLG Approach Key Challenge Example Horizontal SaaS Freemium + viral sharing Activation across many use cases Notion, Airtable Vertical SaaS Free trial + guided setup Domain-specific onboarding required Gusto, Procore API / Developer Tools Free tier + usage-based pricing Docs and DX are the product Stripe, Twilio Marketplace / Platform Free buyer side, monetize supply Cold start problem Airbnb, Upwork Infrastructure Free tier with generous limits Expansion triggers at scale AWS, Vercel Collaboration Tools Free for small teams, paid at scale Must reach team-level adoption Slack, Figma
PLG + Sales-Assist Hybrid#
Most successful PLG companies add sales as they scale. The model evolves:
TEXT · 4 LINES Copy 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#
Priority Action Impact 1 Define and instrument activation metric Foundation for all PLG 2 Reduce time-to-value to under 5 minutes Directly lifts conversion 3 Build PQL scoring and alerting Connects product usage to revenue 4 Implement in-app upgrade prompts at limit moments Captures expansion intent 5 Add viral loops (invites, sharing, embedding) Compounds growth over time 6 Launch referral program for activated users Reduces CAC systematically
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.
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.
Referral Program Templates#
Template Comparison#
Model How It Works Best For Example Double-Sided Both referrer and referee get rewarded SaaS, fintech, marketplaces Dropbox — both get extra storage Single-Sided (Referrer) Only the referrer is rewarded High-consideration purchases Amex — referrer gets bonus points Single-Sided (Referee) Only the new user gets a benefit Low-friction trials, e-commerce "Give your friend $20 off" Tiered Rewards escalate with number of referrals Community-driven products Morning Brew — unlock swag at milestones Milestone Unlock rewards at specific referral counts Waitlist and launch campaigns Harry's pre-launch — 5, 10, 25, 50 referral tiers Leaderboard Top referrers win premium rewards Time-bound campaigns, contests Launch competitions with grand prizes Embedded / Native Referral is built into the product UX Collaboration and network tools Calendly — every link is a referral
Double-Sided Program Design#
TEXT · 2 LINES Copy Referrer Action → Unique Link/Code Generated → Referee Signs Up →
Referee Qualifies (activation event) → Both Parties RewardedComponent Decision Recommendation Referrer reward Cash, credit, free months, points Match to what users already value in your product Referee reward Discount, extended trial, bonus Remove friction for first purchase/activation Qualification event Sign-up, activation, purchase, retention Tie to meaningful value moment, not just registration Reward timing Instant vs delayed Instant for referrer motivation; delayed for fraud prevention Cap per referrer Unlimited vs capped Cap at 10-20 initially; raise for power referrers
Incentive Design Principles#
Reward Type Selection#
Reward Type Pros Cons Best For Account credit High perceived value, keeps users in ecosystem No value if user churns SaaS, platforms Cash / gift cards Universally appealing, easy to understand Expensive, attracts fraud Fintech, high-ACV products Free months Low marginal cost, extends retention Only valuable if user is paying Subscription products Feature unlocks Zero marginal cost, drives engagement Limited appeal if features aren't compelling Freemium products Physical goods / swag Tangible, shareable, social proof Logistics complexity, cost Brand-driven companies Charitable donation Aligns with values, positive brand Lower direct motivation Mission-driven brands
Incentive Calibration Checklist#
Referral Mechanics#
Tracking Infrastructure#
Mechanism How It Works Strengths Weaknesses Unique referral link URL with referrer ID parameter Easy to share, trackable Can be lost if user clears cookies Referral code Alphanumeric code entered at sign-up Works offline, memorable Requires manual entry, friction Email invite Direct email sent from product High intent signal, personalized Limited reach vs social sharing In-app invite Share directly within product UI Contextual, low friction Requires active product usage QR code Scannable code linking to referral URL Works for physical/event contexts Niche use case
Link Architecture#
TEXT · 7 LINES Copy 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 generatedAttribution Rules#
Scenario Recommended Rule Multiple referral links clicked Last-click attribution within 30-day window Referral link + paid ad touchpoint Referral takes priority (reward the advocate) User signs up without link but enters code Code attribution honored Cookie expires before conversion No attribution (extend cookie to 90 days) Referred user already exists in system No reward (de-duplicate on email)
Fraud Prevention Tactics#
Common Fraud Patterns#
Fraud Type Description Detection Method Self-referral User creates multiple accounts to refer themselves IP matching, device fingerprinting, email domain analysis Referral rings Groups of users refer each other in circles Network graph analysis, timestamp clustering Incentive abuse Users sign up solely for the reward, then churn Require activation event before reward; monitor 7-day retention Bot-generated signups Automated account creation to claim rewards CAPTCHA, behavioral analysis, signup velocity monitoring Fake email accounts Disposable emails used for referee accounts Block disposable email domains, require email verification
Fraud Prevention Checklist#
Launch Playbook#
Pre-Launch (2-4 Weeks Before)#
Week Action Owner -4 Define program goals, KPIs, and budget Growth / Marketing -4 Select referral platform or build in-house tracking Engineering -3 Design reward structure and fraud prevention rules Growth / Finance -3 Create referral landing page and email templates Design / Content -2 Build referral dashboard (referrer view + admin view) Engineering -2 Write program terms and conditions Legal / Marketing -1 QA referral flow end-to-end (link generation → reward fulfillment) QA -1 Seed program with 50-100 power users for soft launch Growth
Launch Day Checklist#
Post-Launch (First 30 Days)#
Day Action 1-3 Monitor conversion rates, fix broken flows, address support tickets 7 First performance review — referral rate, share rate, conversion rate 14 A/B test reward messaging and CTA placement 21 Identify and engage top referrers with personalized outreach 30 Full program review — ROI analysis, fraud audit, optimization plan
Benchmarks#
Key Metrics#
Metric Formula Good Great Elite Referral Rate Referrers / Total active users 2-5% 5-15% 15%+ Share Rate Users who share link / Users who see referral prompt 10-15% 15-25% 25%+ Invite Conversion Rate Referred sign-ups / Total invites sent 5-10% 10-20% 20%+ K-Factor Invites per user x conversion rate 0.1-0.3 0.3-0.7 0.7+ CAC Reduction (Standard CAC - Referral CAC) / Standard CAC 30-50% 50-70% 70%+ Referral LTV vs Organic LTV Referred user LTV / Organic user LTV 1.0x 1.1-1.25x 1.25x+ Time to Referral Median days from sign-up to first referral 30-60 days 14-30 days <14 days
K-Factor Calculation#
TEXT · 9 LINES Copy 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)Integration Points#
Where to Surface Referrals in the Product#
Touchpoint Timing Why It Works Post-activation prompt After user completes key action User just experienced value — peak motivation Settings / Account page Persistent, always accessible Power users seek it out Post-purchase confirmation After payment or upgrade Buyer's high — social proof motivation Share / Export flow When user shares content externally Natural sharing moment, embedded referral Milestone celebrations After achievement or usage milestone Emotional high, gratitude response NPS follow-up After user rates 9-10 on NPS Promoters are pre-qualified referrers Billing / invoice page During renewal or plan review Budget-conscious moment, credit appeals Help / Support resolution After successful support interaction Gratitude and relief drive advocacy
Tech Stack Considerations#
Approach Pros Cons Best For In-house build Full control, deep integration Engineering time, maintenance burden Products with unique referral mechanics Referral SaaS (ReferralCandy, Friendbuy) Fast to launch, proven UX Monthly cost, limited customization E-commerce, standard programs Affiliate platform (Impact, PartnerStack) Scales to partners + affiliates Complexity, cost B2B SaaS with partner channels CRM integration (HubSpot, Salesforce) Links referrals to sales pipeline Requires CRM maturity Sales-assisted referral programs
The best referral programs don't feel like marketing programs. They feel like one friend helping another discover something valuable. Design for that.
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.
Hook Model Framework#
Nir Eyal's Hook Model explains how products create habitual usage through a four-step loop.
TEXT · 3 LINES Copy Trigger → Action → Variable Reward → Investment
↑ |
└──────────────────────────────────────┘Step 1: Trigger#
Triggers prompt the user to take action. They come in two forms.
Trigger Type Description Examples External triggers Environmental cues that prompt action Push notification, email, ad, CTA button, colleague mention Internal triggers Emotional states or routines that prompt action Boredom (scroll Instagram), anxiety (check Slack), curiosity (open Reddit)
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).
Design Principle Implementation Reduce friction Fewer clicks, faster load, simpler UI Increase motivation Clear value proposition at point of action Ensure trigger visibility Notification lands when user can act on it
Step 3: Variable Reward#
The reward must be variable (unpredictable) to maintain engagement over time. Fixed rewards lose their power.
Reward Type Description Product Example Rewards of the Tribe Social validation, acceptance, belonging Likes, comments, follower counts Rewards of the Hunt Resources, information, deals News feed content, search results, deal alerts Rewards of the Self Mastery, competence, completion Leveling up, skill badges, streak counts
Step 4: Investment#
The user puts something into the product that makes it more valuable over time and increases switching costs.
Investment Type Example Retention Effect Data Saved preferences, history, files More personalization, harder to leave Content Posts, documents, projects Accumulated value stored in product Reputation Reviews, ratings, follower count Social capital that cannot transfer Skill Learned workflows, keyboard shortcuts Efficiency advantage in current product Social connections Team members, contacts, followers Network locked into the platform
Churn Prediction Models#
Leading Indicators of Churn#
Identify users at risk before they cancel. These signals typically appear 2-4 weeks before churn.
Signal Category Specific Indicators Risk Level Usage decline Login frequency drops 40%+, session duration decreases, fewer core actions High Feature disengagement Stops using advanced features, reverts to basic usage only Medium-High Support signals Multiple unresolved tickets, negative CSAT scores, complaint escalation High Billing signals Failed payment, downgrade inquiry, cancellation page visit Critical Team signals Admin account goes inactive, seat count decreases, key user leaves High Engagement signals Stops opening emails, ignores in-app messages, unsubscribes from updates Medium Competitive signals Visits competitor pricing pages (if tracked), mentions competitors in support Medium-High
Churn Risk Scoring Model#
Factor Weight Score Range Scoring Method Login frequency trend (14-day) 25% 0-100 100 if stable/growing, 0 if >60% decline Core feature usage (14-day) 20% 0-100 Based on actions vs historical average Support ticket sentiment 15% 0-100 NLP sentiment analysis on recent tickets Days since last login 15% 0-100 100 if <3 days, 50 if 3-7, 25 if 7-14, 0 if >14 Contract/billing status 10% 0-100 100 if healthy, 0 if payment failed or cancel page visited Onboarding completion 10% 0-100 Percentage of onboarding steps completed NPS / CSAT score 5% 0-100 Latest survey response normalized to 0-100
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
Win-Back Sequences#
Email Win-Back Flow#
Email Timing After Churn Subject Line Approach Content Strategy 1 Day 1 "We're sorry to see you go" Ask for feedback, offer help resolving issues 2 Day 7 "Here's what you're missing" Highlight new features or improvements since they left 3 Day 14 "We've made changes based on your feedback" Show specific improvements relevant to their churn reason 4 Day 30 "Come back with [X% discount / free month]" Time-limited incentive to return 5 Day 60 "A lot has changed at [Product]" Major update roundup, no hard sell 6 Day 90 "Last chance: special offer for returning customers" Final incentive, then move to quarterly nurture
Win-Back Tactics by Churn Reason#
Churn Reason Win-Back Approach Offer Price / budget Downgrade option, annual discount, pause subscription 20-30% discount or free month Missing feature Notify when feature ships, invite to beta Early access to requested feature Poor experience Personal apology from leadership, dedicated support White-glove onboarding, dedicated CSM Switched to competitor Competitive comparison content, migration assistance Free migration service, extended trial No longer needed Stay in touch with value content, seasonal re-engagement Free tier to maintain relationship Bad onboarding Offer guided setup session, improved onboarding flow 1-on-1 onboarding call with product expert
Cohort Analysis Methodology#
Setting Up Cohort Analysis#
TEXT · 3 LINES Copy 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#
Cohort Week 0 Week 1 Week 2 Week 4 Week 8 Week 12 Jan W1 (500 users) 100% 45% 32% 22% 18% 15% Jan W2 (600 users) 100% 48% 35% 25% 20% 17% Jan W3 (550 users) 100% 52% 38% 28% 23% 20%
What to Look For#
Pattern Interpretation Action Retention curve flattens Users who survive early weeks tend to stick Focus on improving early retention (Week 1-2) Recent cohorts retain better Product or onboarding improvements are working Continue iterating on what changed Recent cohorts retain worse Something broke — regression, quality issue, wrong audience Investigate recent changes, review acquisition sources Sharp drop at specific week Users hit a wall at that point in their journey Map user journey to that week, identify friction One segment retains much better You have found your ideal customer profile Double down on acquiring that segment
Habit Loop Design#
Daily Active Usage Checklist#
Design your product to support daily habits.
Habit Formation Timeline#
Phase Duration User Behavior Product Role Learning Days 1-7 Exploring, evaluating, deciding Hand-hold through activation, demonstrate value Practicing Days 8-21 Using with conscious effort, building routine Reinforce triggers, celebrate progress Habituation Days 22-60 Usage becomes automatic, part of workflow Reduce friction further, introduce advanced features Mastery Days 60+ Power user, advocate, invested Expansion opportunities, referral prompts, community
Re-Engagement Triggers#
Trigger Types and Timing#
Trigger Channel Timing Content Inactivity nudge Email 3 days without login "Your [project/task/data] is waiting for you" Social trigger Push / Email When a teammate takes action "Alex commented on your document" Value trigger Email Weekly Personalized digest of insights, metrics, or updates Achievement trigger In-app + Email Upon milestone "You're 80% to your goal — keep going" Content trigger Email When new relevant content is published "New template in your category" Feature trigger In-app + Email When relevant new feature ships "New: the feature you requested is live" External trigger Push Calendar-based or event-based "Your report is ready for Monday's meeting" FOMO trigger Email / Push When peers are active "Your team completed 15 tasks this week"
Re-Engagement Priority Matrix#
User Segment Days Inactive Priority Approach High-value, recently lapsed 3-7 days Critical Personal outreach, in-app message, email High-value, moderately lapsed 7-30 days High Win-back email sequence, phone call from CSM Low-value, recently lapsed 3-7 days Medium Automated nudge email, push notification Low-value, moderately lapsed 7-30 days Low Automated email sequence, no manual effort Any segment, long-term lapsed 30+ days Evaluate Cost-benefit analysis — may not be worth pursuing
Retention Benchmarks by Business Model#
Monthly Retention (% active after N months)#
Business Model Month 1 Month 3 Month 6 Month 12 Notes B2B SaaS (SMB) 70-80% 55-65% 45-55% 35-45% Higher if annual contracts B2B SaaS (Enterprise) 90-95% 85-92% 80-88% 75-85% Multi-year contracts stabilize Consumer Subscription 60-70% 40-50% 30-40% 20-30% Highly variable by category Mobile App (Social) 25-35% 12-18% 8-12% 5-8% Day 1 retention: 25-40% Mobile App (Utility) 30-40% 18-25% 12-18% 8-12% Higher if daily use case exists E-commerce (Repeat Purchase) 25-35% 15-22% 10-15% 8-12% Measured by repeat purchase Marketplace 30-40% 20-30% 15-22% 10-18% Supply-side retains better than demand
Net Revenue Retention Benchmarks (B2B SaaS)#
NRR Range Assessment Examples >130% Elite — expansion significantly outpaces churn Snowflake, Twilio, Datadog 110-130% Strong — healthy expansion motion HubSpot, Slack, Zoom 100-110% Acceptable — expansion roughly offsets churn Most mature B2B SaaS 90-100% Concerning — slight net contraction Churn problem emerging <90% Critical — revenue is shrinking from existing customers Urgent retention intervention needed
Customer Health Scoring#
Health Score Components#
Component Weight Data Source Scoring Product usage depth 25% Product analytics Features used / Total features available Usage frequency 20% Product analytics Actual logins / Expected logins for plan Support health 15% Help desk Inverse of open tickets + sentiment Relationship depth 15% CRM Number of stakeholders engaged, executive sponsor Contract value trend 10% Billing system Growing, stable, or declining Onboarding progress 10% Onboarding tracker % of implementation milestones completed Survey sentiment 5% NPS / CSAT tool Latest survey score
Health Score Actions#
Health Score Label Color Action 85-100 Thriving Green Upsell/expand, request referral, case study candidate 70-84 Healthy Light Green Standard check-ins, feature adoption nudges 50-69 Neutral Yellow Proactive outreach, usage review call, training offer 30-49 At Risk Orange Escalate to CSM, executive check-in, create success plan 0-29 Critical Red Immediate intervention, executive-to-executive call, save offer
Health Score Review Cadence#
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.
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.
Viral Coefficient (K-Factor) Calculation#
The K-factor measures how many new users each existing user generates.
TEXT · 9 LINES Copy 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 contributionK-Factor Examples#
Product Invites/User (i) Conversion Rate (c) K-Factor Result Hotmail ("Get free email" signature) 50+ 4% 2.0+ Explosive viral growth Dropbox (storage referral) 7 15% 1.05 Sustained viral growth Slack (team invites) 4 20% 0.8 Strong organic amplifier Typical B2B SaaS 2 5% 0.1 Marginal contribution
Improving K-Factor#
Lever Tactic Impact Increase invites (i) Make sharing effortless, add multiple share channels Moderate Increase invites (i) Embed invitations into core product actions High Increase conversion (c) Optimize landing page for referred visitors High Increase conversion (c) Offer incentive to referee (double-sided reward) Moderate Increase conversion (c) Personalize invitation (include referrer name, context) Moderate
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.
TEXT · 3 LINES Copy 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.Cycle Time K = 0.8 users after 20 days K = 0.8 users after 40 days 1 day ~11,000 from 1,000 seed ~120,000 2 days ~3,300 ~11,000 5 days ~1,600 ~2,600 10 days ~1,250 ~1,600
Tactics to Reduce Cycle Time#
Loop Design Patterns#
Pattern 1: Inherent Viral Loops#
The product requires multiple users to function. Inviting others is not optional; it is the product.
Characteristic Detail Definition Product value requires other users to participate Friction Very low — users must invite to use the product Example Zoom (need someone to meet with), Venmo (need someone to pay) K-factor range 0.5 - 2.0+ Optimization focus Reduce sign-up friction for invitees, improve first-use experience
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.
Characteristic Detail Definition Users are incentivized to invite others through rewards Friction Moderate — requires active decision to share Example Dropbox (free storage for referrals), Uber (ride credits) K-factor range 0.2 - 1.0 Optimization focus Incentive design, timing of referral prompt, reward fulfillment speed
Pattern 3: Word-of-Mouth Loops#
Users share because the product is remarkable, not because they are prompted or incentivized.
Characteristic Detail Definition Users voluntarily tell others about the product Friction High — requires strong emotional reaction to trigger sharing Example ChatGPT (novelty), Superhuman (status), Arc Browser (design) K-factor range 0.1 - 0.5 (harder to measure, but compounds over time) Optimization focus Deliver exceptional experience, create shareable moments, build social proof
Pattern 4: Exposure / Embedded Loops#
The product exposes itself to non-users as part of normal usage.
Characteristic Detail Definition Non-users encounter the brand through user-generated output Friction Zero for the existing user — sharing happens automatically Example Calendly link in emails, "Made with Squarespace" footer, Mailchimp badge K-factor range 0.3 - 1.5 Optimization focus Visibility of branding, CTA on exposed content, landing page conversion
Virality Assessment Framework#
Score your product on each dimension (1-5) to assess viral potential.
Dimension Score 1 (Low) Score 5 (High) Your Score Inherent multi-user need Product is fully useful solo Product requires multiple users ___ Shareability of output Output is private/internal Output is naturally shared externally ___ Emotional trigger Functional, no emotional charge Delightful, surprising, status-granting ___ Invite friction Complex, multi-step invite process One-click share/invite ___ Invitee experience Confusing landing, long sign-up Instant value, frictionless entry ___ Network density Users' contacts are unlikely to need product Users' contacts are ideal prospects ___ Frequency of use Monthly or quarterly Daily or multiple times per day ___ Visibility Usage is invisible to others Usage is publicly observable ___
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
Social Proof Loops#
Social proof creates a secondary viral effect by making adoption visible and desirable.
Social Proof Type Mechanism Implementation Usage counters "Join 500,000+ teams using [Product]" Display on landing pages, in-app, and emails Logo walls Recognizable brand logos build trust Feature on home page, case study pages Activity feeds Show real-time user actions "Sarah from Acme just signed up" (use ethically) User-generated content Customers create content featuring product Hashtag campaigns, template galleries Reviews and ratings Third-party validation G2, Capterra, App Store ratings Milestone sharing Users share achievements from the product "I completed 100 workouts with [App]" — auto-generated share cards
Content Virality Mechanics#
When the product generates content, that content can become its own viral channel.
Content Viral Loop#
TEXT · 2 LINES Copy 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#
Content Type Viral Lever Example Reports / Dashboards Embed product branding, include "Create your own" CTA Typeform results page Templates Make templates publicly discoverable via SEO Canva templates, Notion templates Interactive tools Output includes product attribution "Built with [Product]" watermark User profiles / portfolios Public profiles rank in search, link back to product Behance, LinkedIn Shared workspaces Collaborators must sign up to participate Google Docs, Miro boards
Network Effects vs Viral Loops#
These are related but distinct concepts. Understanding the difference matters for strategy.
Dimension Network Effects Viral Loops Definition Product becomes more valuable as more users join Users bring in new users through sharing Value driver Utility increases with network size Growth increases with sharing behavior Example Telephone network, Facebook, Uber Dropbox referral, Hotmail signature Moat strength Very strong — hard to leave a large network Moderate — can be copied by competitors Cold start problem Severe — product has little value with few users Mild — product works solo, sharing is bonus Measurement Active users on platform, engagement per user K-factor, viral cycle time Strategic focus Reach critical mass in one segment first Optimize each step of the invite flow
Combined power: The strongest growth engines combine both. Slack has network effects (more teammates = more value) and viral loops (team invites expose new organizations).
Optimizing Each Loop Step#
Every viral loop has discrete steps. Optimize each one independently.
Step-by-Step Optimization#
Loop Step Metric Optimization Tactics 1. User experiences value Activation rate Reduce time-to-value, improve onboarding 2. User encounters share trigger Trigger exposure rate Place prompts at peak-value moments 3. User decides to share Share rate (impressions → shares) Reduce friction, pre-compose message, add incentive 4. Invitee sees the invitation Invite delivery rate Optimize email deliverability, use multiple channels 5. Invitee clicks through Click-through rate Personalize message, clear value proposition 6. Invitee lands on product Landing page conversion Tailored landing page for referred visitors 7. Invitee signs up Sign-up completion rate Minimize fields, offer SSO, remove credit card requirement 8. New user activates New user activation rate Dedicated onboarding for referred users 9. New user becomes referrer Repeat referral rate Surface referral prompt after activation
Funnel Benchmark Example#
Step Benchmark Your Product Users who see share prompt 80% of activated users ___ Share prompt → share action 15-25% ___ Share → invitee click 10-20% ___ Click → sign-up 20-40% ___ Sign-up → activation 20-40% ___ Activated → refers others 5-15% ___
A/B Testing Priority for Viral Loops#
Priority What to Test Expected Impact 1 Share prompt timing and placement High — determines if users even see the loop 2 Invitation message copy and format High — affects click-through from invitees 3 Referred user landing page High — conversion bottleneck for new users 4 Incentive type and amount Medium — affects motivation to share 5 Number of share channels offered Medium — more channels = broader reach 6 Sign-up flow for referred users Medium — fewer steps = higher completion
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.
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.
Section 1: Social Media Platform Specs#
Instagram#
Feed Posts#
Spec Value Character limit 2,200 Optimal caption length 125-150 characters (before "more" truncation) Image formats JPEG, PNG Square image 1080 x 1080 px (1:1) Portrait image 1080 x 1350 px (4:5) Landscape image 1080 x 566 px (1.91:1) Max file size (image) 30 MB Hashtag limit 30 max, 3-5 recommended Algorithm priority signals Saves, shares, comments, watch time, relationship closeness Best posting times Tue-Thu 9-11 AM, Wed 11 AM, Fri 10-11 AM (local)
Instagram Reels#
Spec Value Duration 15s, 30s, 60s, 90s (up to 3 min with some accounts) Aspect ratio 9:16 (vertical) Resolution 1080 x 1920 px Video format MP4, MOV Max file size 4 GB Cover photo 1080 x 1920 px Caption limit 2,200 characters Hashtag limit 30 max, 3-8 recommended Algorithm priority signals Watch-through rate, replays, shares, audio usage, originality Best posting times Mon-Thu 9 AM, 12 PM, 7-8 PM (local)
Instagram Stories#
Spec Value Duration per slide Up to 60 seconds Aspect ratio 9:16 Resolution 1080 x 1920 px Image format JPEG, PNG Video format MP4, MOV Max file size (video) 4 GB Text-safe zone Keep critical content within center 1080 x 1420 px (avoid top 250 px and bottom 250 px for UI overlays) Sticker/link limit 1 link sticker per story Lifespan 24 hours (unless added to Highlights)
Instagram Carousel#
Spec Value Slides 2-20 per carousel Aspect ratio All slides must match; 1:1 or 4:5 recommended Resolution (square) 1080 x 1080 px Resolution (portrait) 1080 x 1350 px Image format JPEG, PNG Video per slide Up to 60 seconds Caption limit 2,200 characters Algorithm priority signals Swipe-through rate, saves, shares, dwell time per slide
TikTok#
Spec Value Video duration 15s, 60s, 3 min, 10 min, 30 min, 60 min Aspect ratio 9:16 (vertical) Resolution 1080 x 1920 px minimum Video format MP4, MOV, WebM Max file size 10 GB (desktop), 287 MB (mobile) Caption limit 4,000 characters Hashtag limit No hard limit; 3-5 relevant hashtags recommended Photo mode Up to 35 images per carousel post Algorithm priority signals Completion rate, rewatch rate, shares, comments, profile visits after viewing, content diversity score Best posting times Tue 2-4 PM, Thu 12-3 PM, Fri 1-3 PM (local) Text-safe zone Keep text within center 720 x 1280 px area to avoid UI overlaps
LinkedIn#
LinkedIn Post#
Spec Value Character limit 3,000 Optimal length 800-1,200 characters for engagement Image dimensions 1200 x 627 px (landscape), 1080 x 1080 px (square), 1080 x 1350 px (portrait) Image format JPEG, PNG, GIF Max image file size 8 MB Video duration 3 seconds to 10 minutes Video format MP4 Max video file size 5 GB Video aspect ratio 1:1, 16:9, or 9:16 Document/carousel PDF upload, up to 300 pages, max 100 MB Hashtag limit No hard limit; 3-5 recommended Algorithm priority signals Dwell time, comments (especially early), shares, relevance to network, content type diversity Best posting times Tue-Thu 8-10 AM, Tue 10-11 AM peak (local business timezone)
LinkedIn Article#
Spec Value Headline limit 100 characters Body limit 125,000 characters Cover image 1920 x 1080 px recommended Supports Rich text, images, embeds, links Algorithm priority signals Read-through rate, comments, external shares
LinkedIn Newsletter#
Spec Value Title limit 64 characters Description limit 250 characters Logo image 300 x 300 px Cover image 1920 x 1080 px Body limit Same as Article (125,000 characters) Frequency options Daily, weekly, biweekly, monthly Algorithm priority signals Subscriber growth rate, open rate, engagement rate
Spec Value Character limit (free) 280 Character limit (Premium) 25,000 Optimal tweet length 71-100 characters for engagement Image dimensions 1600 x 900 px (16:9) recommended Image formats JPEG, PNG, GIF, WebP Max images per tweet 4 Max image file size 5 MB (static), 15 MB (GIF) Video duration 0.5s to 140 seconds (up to 240 min for Premium) Video resolution 1920 x 1200 px max Video format MP4 (H.264 video, AAC audio) Max video file size 512 MB Video aspect ratio 16:9 or 1:1 recommended Hashtag limit No hard limit; 1-2 recommended Algorithm priority signals Replies, retweets, bookmark rate, link clicks, profile visits, verified status, dwell time Best posting times Mon-Fri 8-10 AM, Wed 9-11 AM (local)
Facebook#
Facebook Post#
Spec Value Character limit 63,206 Optimal length 40-80 characters for engagement Image dimensions 1200 x 630 px (landscape), 1080 x 1080 px (square) Image format JPEG, PNG, GIF, WebP Max image file size 30 MB Link preview image 1200 x 630 px minimum Video duration 1 second to 240 minutes Video format MP4, MOV Video resolution 1080p recommended Max video file size 10 GB Algorithm priority signals Meaningful interactions (comments, shares), watch time, content type matching user preference, Group engagement Best posting times Mon-Fri 9 AM-12 PM, Wed 11 AM peak (local)
Facebook Reels#
Spec Value Duration Up to 90 seconds Aspect ratio 9:16 Resolution 1080 x 1920 px Video format MP4, MOV Max file size 4 GB Caption limit 2,200 characters Algorithm priority signals Originality, completion rate, shares, audio trends
Facebook Stories#
Spec Value Duration per slide Up to 20 seconds (video) Aspect ratio 9:16 Resolution 1080 x 1920 px Image format JPEG, PNG Video format MP4, MOV Max file size 4 GB Text-safe zone Center 1080 x 1420 px Lifespan 24 hours
Pinterest#
Standard Pin#
Spec Value Title limit 100 characters Description limit 500 characters Image aspect ratio 2:3 recommended (1000 x 1500 px) Minimum image width 600 px Image format JPEG, PNG, WebP Max file size 20 MB Algorithm priority signals Save rate, click-through rate, pin quality score, domain quality, keyword relevance, freshness Best posting times Sat 8-11 PM, Fri-Sun for lifestyle; Tue-Thu for B2B (local)
Idea Pin#
Spec Value Pages Up to 20 Image resolution 1080 x 1920 px (9:16) Video duration per page Up to 60 seconds Video format MP4, MOV Title limit 100 characters Algorithm priority signals View-through rate, saves, follows from pin, topic tag relevance
Video Pin#
Spec Value Duration 4 seconds to 15 minutes Aspect ratio 1:1, 2:3, or 9:16 Resolution 1080 px minimum width Video format MP4, MOV Max file size 2 GB Title limit 100 characters Description limit 500 characters
YouTube#
YouTube Video#
Spec Value Title limit 100 characters Description limit 5,000 characters Tag limit 500 characters total Aspect ratio 16:9 standard Resolution 1920 x 1080 px (1080p) minimum recommended; 3840 x 2160 (4K) supported Video format MP4 (H.264 + AAC) recommended; also MOV, AVI, WMV, FLV, WebM Max file size 256 GB Max duration 12 hours Thumbnail 1280 x 720 px (16:9), max 2 MB, JPEG/PNG/GIF Chapters Minimum 3 chapters, each 10+ seconds, first must start at 0:00 Algorithm priority signals Click-through rate, watch time, average view duration, session time, engagement (likes, comments, shares), subscriber conversion Best posting times Fri-Sat 9-11 AM, Thu 3-4 PM, weekday evenings 5-9 PM (viewer timezone)
YouTube Shorts#
Spec Value Duration Up to 3 minutes Aspect ratio 9:16 (vertical) Resolution 1080 x 1920 px Title limit 100 characters Algorithm priority signals Swipe-away rate (lower is better), replays, likes, subscriber conversion from Shorts
YouTube Live#
Spec Value Resolution Up to 4K (2160p) at 60fps Recommended bitrate (1080p) 4,500-9,000 Kbps Stream format RTMP or HLS Latency options Ultra low, low, normal Thumbnail 1280 x 720 px Schedule in advance Yes, up to weeks ahead
Reddit#
Spec Value Title limit 300 characters Text post limit 40,000 characters Image dimensions No strict requirement; 1200 x 628 px recommended for link posts Image format JPEG, PNG, GIF Max images per post 20 (gallery post) Video duration Up to 15 minutes Video format MP4, MOV Max video file size 1 GB Hashtag strategy Not used on Reddit; flair and subreddit selection matter instead Algorithm priority signals Upvote/downvote velocity, comment count, subreddit relevance, account karma and age Best posting times Mon 6-8 AM, Wed-Fri 7-9 AM EST (US-centric subreddits)
Threads#
Spec Value Character limit 500 Image dimensions 1080 x 1350 px (4:5) recommended Image format JPEG, PNG Max images per post 10 Video duration Up to 5 minutes Video format MP4, MOV Video aspect ratio 9:16 or 1:1 Link preview Supported Hashtag strategy Topic tags (1 per post) Algorithm priority signals Replies, reposts, engagement velocity, follower relationship Best posting times Mon-Fri 8-10 AM, 12-1 PM (local)
Snapchat#
Spec Value Snap duration Up to 60 seconds (video), 10 seconds (image) Aspect ratio 9:16 Resolution 1080 x 1920 px Video format MP4, MOV Max file size 5 MB (image), 32 MB (video for ads) Spotlight video Up to 3 minutes Text limit (caption) 80 characters on-screen Text-safe zone Center 1080 x 1420 px Algorithm priority signals (Spotlight) Completion rate, shares, favorites, screenshot rate Best posting times Thu-Sat 10 PM-1 AM (local; younger demographic engagement peaks)
Section 2: Email Specifications#
Subject Line Limits by Client#
Email Client Visible Characters (Desktop) Visible Characters (Mobile) Gmail 70 40 Outlook (desktop app) 73 38 Outlook.com 60 38 Apple Mail 78 35 Yahoo Mail 46 35 Samsung Mail N/A 33
Recommendation: Keep subject lines under 40 characters for reliable cross-client mobile rendering. Front-load the most important words.
Email Client Preview Text Visible Length Gmail 90-110 characters (varies by subject line length) Outlook 35-90 characters Apple Mail 75-100 characters Yahoo Mail 55-70 characters
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#
Spec Value Recommended body width 600 px (max 640 px) Gmail clipping threshold 102 KB (total HTML size including inline CSS). Messages exceeding this are truncated with a "[Message clipped] View entire message" link. Max email size (practical) Keep under 100 KB HTML. Total with images should stay under 1 MB for fast loading. Font stacks System fonts for reliability: Arial, Helvetica, Georgia, Times New Roman. Web fonts supported in Apple Mail, iOS Mail, Android (default), Outlook.com, Thunderbird. NOT supported in Gmail, Outlook desktop. Minimum font size (mobile) 14 px body, 22 px headlines (iOS auto-sizes text below 13 px) Line height 1.4-1.6 for body text Background images Supported in most clients; NOT supported in Outlook desktop (use VML fallback)
Image Handling#
Spec Value Image formats JPEG, PNG, GIF. WebP supported in Gmail, Apple Mail; NOT in Outlook. Retina support Use 2x resolution images displayed at 1x size (e.g., 1200 px image displayed at 600 px width) Image blocking Outlook, some corporate clients block images by default. Always include alt text. Animated GIF Supported everywhere except Outlook desktop (shows first frame only) Max single image width 600 px display width (1200 px actual for retina) Image-to-text ratio Aim for 60% text / 40% images to avoid spam filters
CTA Button Specs#
Spec Recommendation Minimum button size 44 x 44 px (Apple HIG tap target) Recommended button size 48-60 px height, full-width on mobile Button method Bulletproof buttons using HTML/CSS (padding-based). VML fallback for Outlook. Avoid image-only buttons. Button text 2-5 words, action-oriented. Max 30 characters. Button contrast WCAG AA minimum (4.5:1 contrast ratio for text on button color)
Dark Mode Considerations#
Client Dark Mode Behavior Apple Mail / iOS Mail Full color inversion with @media (prefers-color-scheme: dark) support Gmail (Android) Partial: inverts light backgrounds to dark, adjusts text to white. Does NOT respect prefers-color-scheme. Gmail (iOS) Partial: similar to Android. Does NOT respect prefers-color-scheme. Outlook (desktop) Full inversion. Some transparent PNGs get white backgrounds. Outlook.com Partial inversion. Supports [data-ogsc] and [data-ogsb] selectors. Yahoo Mail Applies its own dark theme. Limited CSS override support.
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#
Rule Detail Responsive approach Use fluid tables with max-width: 600px and width: 100% Media queries Supported in Apple Mail, iOS Mail, Android (default), Thunderbird. NOT supported in Gmail (any), Yahoo Mail, Outlook. Stacking columns Use display: block on table cells for mobile stacking via media queries, or use a hybrid/fluid approach for clients without media query support Touch targets Minimum 44 x 44 px for all tappable elements Single-column layout Recommended for mobile-first design
Apple Mail Privacy Protection (MPP)#
Impact Detail Open tracking Unreliable. Apple pre-fetches all images (including tracking pixels) via proxy, inflating open rates. Approximately 50-60% of Apple Mail users have MPP enabled. IP-based geolocation No longer accurate for Apple Mail users. Apple proxies mask true IP. Device detection Unreliable. User-agent data is obscured. Recommended response Shift KPIs to click-through rate, conversion rate, and revenue attribution. Use UTM parameters for tracking. Build engagement segments based on clicks, not opens.
Section 3: Ad Platform Creative Specs#
Google Ads#
Responsive Search Ads (RSA)#
Spec Value Headlines Up to 15; each max 30 characters Descriptions Up to 4; each max 90 characters Display URL path 2 fields, each max 15 characters Final URL Required Pinning Available for headlines (positions 1, 2, 3) and descriptions (positions 1, 2) Minimum required 3 headlines, 2 descriptions Recommendation Provide all 15 headlines and 4 descriptions for maximum combinations
Google Display Ads#
Spec Value Landscape image 1200 x 628 px (1.91:1) — required Square image 1200 x 1200 px (1:1) — required Portrait image 960 x 1200 px (4:5) — optional Logo (landscape) 1200 x 300 px (4:1) — optional Logo (square) 1200 x 1200 px (1:1) — required Image file size Max 5.12 MB each Image format JPEG, PNG Short headline Max 30 characters (up to 5) Long headline Max 90 characters (1) Description Max 90 characters (up to 5) Business name Max 25 characters CTA options Automated, Learn More, Get Quote, Apply Now, Sign Up, Contact Us, Download, Book Now, Shop Now, Visit Site
Performance Max (PMax)#
Spec Value Images Up to 20. Landscape (1200x628), square (1200x1200), portrait (960x1200). Min 1 landscape and 1 square required. Logos Up to 5. Square (1200x1200) required. Landscape (1200x300) recommended. Videos Up to 5. Landscape (16:9), square (1:1), vertical (9:16). Min 10 seconds. If none provided, Google auto-generates from assets. Headlines Up to 5, max 30 characters each Long headlines Up to 5, max 90 characters each Descriptions Up to 5, max 90 characters each. Plus 1 short description max 60 characters. Business name Max 25 characters CTA Automated or manual selection from standard options Audience signals Required: custom segments, interests, demographics, remarketing lists
YouTube Ads#
Format Spec Skippable in-stream Min 12 seconds, no max. Skippable after 5s. Recommended 15-30s for performance. Non-skippable in-stream 15 seconds exactly (20s in some regions) Bumper ad Max 6 seconds, non-skippable In-feed (Discovery) Thumbnail 1280x720 (auto-selected or custom). Title max 100 chars. Description max 2 lines. Video resolution 1920x1080 (16:9) or 1080x1920 (9:16) for Shorts ads File format MP4 recommended Max file size 256 GB (same as standard YouTube upload) Companion banner 300 x 60 px (desktop, auto-generated or custom)
Google Shopping#
Spec Value Product title Max 150 characters (first 70 most visible) Product description Max 5,000 characters Product image Min 100 x 100 px (non-apparel), 250 x 250 px (apparel). Recommended 800 x 800+. Max 64 MP. Image format JPEG, PNG, GIF (non-animated), BMP, TIFF Image background White or transparent recommended Image content No watermarks, logos, promotional text, borders
Meta Ads (Facebook & Instagram)#
Feed Ads#
Spec Value Primary text 125 characters recommended (max 2,200 before truncation) Headline 27 characters recommended (max 255) Description 27 characters recommended (max 2,200) Image (single) 1080 x 1080 px (1:1) or 1200 x 628 px (1.91:1) Image format JPEG, PNG Max image file size 30 MB Video aspect ratio 1:1 or 4:5 (feed), 9:16 (Stories/Reels) Video duration 1 second to 241 minutes Video format MP4, MOV Max video file size 4 GB Video resolution 1080 x 1080 px minimum CTA options Shop Now, Learn More, Sign Up, Download, Get Offer, Book Now, Contact Us, Apply Now, Subscribe, Get Quote, Watch More, Send Message, Get Directions
Stories & Reels Ads#
Spec Value Aspect ratio 9:16 Resolution 1080 x 1920 px Stories video duration 1-120 seconds Reels video duration 1-90 seconds Text-safe zone Keep text/logos within center 1080 x 1420 px Primary text 125 characters Headline 40 characters
Carousel Ads#
Spec Value Cards 2-10 Image per card 1080 x 1080 px (1:1) Video per card Up to 240 minutes; 1:1 aspect ratio Headline per card 32 characters recommended Description per card 18 characters recommended Primary text 125 characters recommended Landing URL Unique per card or single destination
Collection Ads#
Spec Value Cover image/video 1200 x 628 px or 1080 x 1080 px Product images Pulled from catalog (min 4 products) Headline 40 characters Opens into Instant Experience (full-screen mobile)
LinkedIn Ads#
Spec Value Introductory text 600 characters max (150 recommended) Headline 200 characters max (70 recommended) Description 300 characters max (100 recommended) Image 1200 x 627 px (1.91:1) recommended. Also supports 1080 x 1080 (1:1) and 1080 x 1350 (4:5). Image file size Max 5 MB Image format JPEG, PNG, GIF CTA options Apply, Download, View Quote, Learn More, Sign Up, Subscribe, Register, Join, Attend, Request Demo
Spec Value Duration 3 seconds to 30 minutes Aspect ratio 16:9 (landscape), 1:1 (square), 9:16 (vertical) Resolution 360p to 1080p File format MP4 Max file size 200 MB Captions SRT file upload supported and recommended
Spec Value Subject line Max 60 characters Message body Max 1,500 characters CTA button text Max 20 characters Banner image 300 x 250 px Clickable links in body Up to 3
Document Ads#
Spec Value File format PDF, DOC, DOCX, PPT, PPTX Max file size 100 MB Max pages 300 (first 5 shown as preview in feed) Introductory text 600 characters max Headline 200 characters max Lead gen form Optional gating after preview pages
TikTok Ads#
In-Feed Ads#
Spec Value Aspect ratio 9:16, 1:1, or 16:9 Resolution 720 x 1280 px minimum (9:16 recommended) Video duration 5-60 seconds (9-15 seconds recommended) Video format MP4, MOV, MPEG, AVI Max file size 500 MB Ad description 1-100 characters (emoji allowed) Display name Max 40 characters CTA options Download, Learn More, Shop Now, Sign Up, Contact Us, Apply Now, Book Now, Get Quote, Subscribe, Order Now, View Now
TopView Ads#
Spec Value Duration 5-60 seconds Resolution 1080 x 1920 px Aspect ratio 9:16 Placement First in-feed ad seen when opening the app File format MP4, MOV Max file size 500 MB Sound Required (auto-play with sound on)
Spark Ads#
Spec Value Source Boost existing organic TikTok posts (own or authorized third-party) Specs Same as original post (no modification to video) Added features CTA button, landing page URL, tracking pixel Authorization Requires video authorization code from creator Duration Campaign-controlled
Pinterest Ads#
Spec Value Standard Pin ad image 1000 x 1500 px (2:3) Square Pin ad image 1000 x 1000 px (1:1) Max aspect ratio 1:2.6 Image format JPEG, PNG Image file size Max 20 MB Title Max 100 characters Description Max 500 characters Video Pin ad duration 4 seconds to 15 minutes Video resolution 1080 px width minimum Video format MP4, MOV Video max file size 2 GB Idea Pin ad Up to 20 pages, 1080 x 1920 px per page Shopping ad Linked to product catalog; standard Pin specs CTA options Automated based on campaign objective
Amazon Ads#
Spec Value Type Keyword- or product-targeted; uses existing product listing Image Product listing main image (auto-pulled from catalog) Headline Not customizable (uses product title) No custom creative Ad uses existing product detail page content
Sponsored Brands#
Spec Value Headline Max 50 characters Logo 400 x 400 px minimum, PNG/JPEG, max 1 MB Custom image 1200 x 628 px recommended Products featured 3+ ASINs Video (Sponsored Brands Video) 6-45 seconds; 1920 x 1080 (16:9) or 1080 x 1920 (9:16) or 1080 x 1080 (1:1); MP4/MOV; max 500 MB Store Spotlight Links to brand Store pages
Spec Value Custom image 1200 x 628 px (landscape), 1200 x 1200 px (square) Logo 600 x 600 px minimum Headline Max 50 characters Image format JPEG, PNG Max file size 5 MB Video 6-30 seconds, MP4, 1280 x 720 min, max 500 MB Targeting Product, audience (views, purchases), contextual
Section 4: Schema Markup Reference#
Use schema.org structured data (JSON-LD format recommended) to enhance search appearance and enable rich results.
Schema Type When to Use Rich Result ArticleBlog posts, news articles, editorial content. Use NewsArticle for timely news, BlogPosting for blogs. Article carousel, headline in Top Stories FAQPagePages with a list of questions and answers. Must show Q&A on the visible page (not just in markup). Expandable FAQ dropdowns in SERP HowToStep-by-step instructional content (recipes, DIY, tutorials). Each step must be a distinct action. Step-by-step rich result with images/video ProductProduct pages. Include name, image, description, offers (price, availability, currency). Product snippet with price, availability, rating LocalBusinessBusinesses with a physical location. Include address, phone, hours, geo coordinates. Subtype to specific business type (e.g., Restaurant, Dentist). Knowledge panel, Maps integration OrganizationCompany/brand homepage. Include name, logo, URL, social profiles, contact info. Knowledge panel, logo in search PersonAuthor pages, speaker bios, team pages. Link to social profiles and authored content. Knowledge panel for notable people Review / AggregateRatingProduct reviews, service reviews. AggregateRating for summary of multiple reviews. Must represent genuine user reviews. Star ratings in SERP EventUpcoming events with date, location, ticket info. Include startDate, location, offers for tickets. Event listing with date, venue, ticket link VideoObjectVideo content on pages. Include name, description, thumbnailUrl, uploadDate, duration, contentUrl or embedUrl. Video carousel, key moments SpeakableContent optimized for text-to-speech / voice assistants. Identify which sections are most suitable for audio playback. Voice assistant eligibility (Google Assistant) BreadcrumbListAll pages with breadcrumb navigation. Reflects the page hierarchy. Breadcrumb trail in SERP instead of raw URL SiteNavigationElementMain site navigation menus. Helps search engines understand site structure. Sitelinks refinement (indirect)
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 (opens in a new tab) ) and Schema Markup Validator (https://validator.schema.org (opens in a new tab) ).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.
Section 5: Image Format Guide#
Format Comparison#
Property WebP AVIF PNG JPEG Compression type Lossy + Lossless Lossy + Lossless Lossless Lossy Transparency Yes Yes Yes No Animation Yes Yes (limited tooling) Yes (APNG) No Typical file size vs JPEG 25-35% smaller 40-50% smaller 5-10x larger Baseline Encoding speed Fast Slow (CPU-intensive) Fast Fast Decoding speed Fast Moderate Fast Fast Color depth 8-bit 8, 10, 12-bit (HDR support) 8, 16-bit 8-bit Max dimensions 16,383 x 16,383 px No practical limit No practical limit 65,535 x 65,535 px Progressive loading No Yes No (interlaced PNG exists) Yes
Browser Support (as of 2026)#
Format Chrome Firefox Safari Edge iOS Safari Android WebP 32+ 65+ 16+ 18+ 16+ 4.2+ AVIF 85+ 93+ 16.4+ 85+ 16.4+ 85+ PNG All All All All All All JPEG All All All All All All
When to Use Each Format#
Format Best For Avoid When WebP General web images. Default choice for photos, illustrations, thumbnails. Best balance of compression, quality, and compatibility. Email campaigns (limited client support), environments requiring lossless at maximum quality. AVIF Hero images, high-quality photography where file size is critical. Best compression ratio available. Use when encoding time is not a constraint. Bulk image processing pipelines (slow encoding), older browser support required without fallback, email. PNG Logos, icons, screenshots, images with text, transparency over complex backgrounds, any image requiring pixel-perfect lossless quality. Photographs (file size too large), any large image where lossy compression is acceptable. JPEG Universal fallback. Email images. Social media uploads (platforms re-encode anyway). Legacy system compatibility. Images with text (compression artifacts), transparency needed, logos or sharp-edged graphics.
Implementation Best Practice#
Use the <picture> element with format fallback:
html · 5 LINES Copy <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)