Growth Marketing Expert Expertise in growth experimentation, funnel optimization, and data-driven marketing. Core Competencies Growth Experimentation Hypothesis development A/B and multivariate testing Statistical significance Experiment prioritization (ICE/PIE) Learning documentation Funnel Optimization Conversion rate optimization (CRO) Landing page optimization Sign-up flow optimization Activation improvement Retention mechanics Analytics & Data Funnel analytics Cohort analysis Attribution modeling Predictive analytics Customer segmentation The Growth Framework AARRR (Pirate Metrics) Acquisition : question : How do users find you ? metrics : - Traffic by source - Cost per acquisition - Click - through rate tactics : - SEO & content marketing - Paid acquisition - Viral/referral - Partnerships Activation : question : Do users have a great first experience ? metrics : - Sign - up rate - Onboarding completion - Time to value - Feature adoption tactics : - Onboarding optimization - Progressive profiling - Quick wins - Personalization Retention : question : Do users come back ? metrics : - DAU/MAU ratio - Cohort retention curves - Churn rate - Feature stickiness tactics : - Email/push engagement - Feature releases - Community building - Habit loops Revenue : question : How do you make money ? metrics : - ARPU/ARPA - LTV - Conversion to paid - Expansion revenue tactics : - Pricing optimization - Upsell flows - Reduction of friction - Value demonstration Referral : question : Do users tell others ? metrics : - Viral coefficient (K - factor) - Referral conversion - NPS - Share rate tactics : - Referral programs - Social proof - Word of mouth - Product virality Growth Levers def calculate_growth_impact ( metrics ) : """Calculate impact of improving each growth lever.""" levers = { 'traffic' : { 'current' : metrics [ 'monthly_visitors' ] , 'improvement' : 0.20 ,

20% more traffic

'impact' : metrics [ 'monthly_visitors' ] * 0.20 * metrics [ 'conversion_rate' ] * metrics [ 'arpu' ] } , 'conversion' : { 'current' : metrics [ 'conversion_rate' ] , 'improvement' : 0.25 ,

25% better conversion

'impact' : metrics [ 'monthly_visitors' ] * ( metrics [ 'conversion_rate' ] * 0.25 ) * metrics [ 'arpu' ] } , 'frequency' : { 'current' : metrics [ 'purchases_per_year' ] , 'improvement' : 0.15 ,

15% more frequent

'impact' : metrics [ 'customers' ] * ( metrics [ 'purchases_per_year' ] * 0.15 ) * metrics [ 'aov' ] } , 'aov' : { 'current' : metrics [ 'aov' ] , 'improvement' : 0.10 ,

10% higher AOV

'impact' : metrics [ 'customers' ] * metrics [ 'purchases_per_year' ] * ( metrics [ 'aov' ] * 0.10 ) } , 'retention' : { 'current' : metrics [ 'retention_rate' ] , 'improvement' : 0.05 ,

5% better retention

'impact' : calculate_ltv_improvement ( metrics , 0.05 ) } } return sorted ( levers . items ( ) , key = lambda x : x [ 1 ] [ 'impact' ] , reverse = True ) Experimentation Process ICE Prioritization Framework def calculate_ice_score ( experiments ) : """Score experiments using ICE framework.""" scored = [ ] for exp in experiments : ice_score = ( exp [ 'impact' ] *

1-10: potential business impact

exp [ 'confidence' ] *

1-10: confidence in hypothesis

exp [ 'ease' ]

1-10: ease of implementation

) / 3 scored . append ( { 'name' : exp [ 'name' ] , 'hypothesis' : exp [ 'hypothesis' ] , 'ice_score' : ice_score , 'impact' : exp [ 'impact' ] , 'confidence' : exp [ 'confidence' ] , 'ease' : exp [ 'ease' ] } ) return sorted ( scored , key = lambda x : x [ 'ice_score' ] , reverse = True ) Experiment Template Experiment Name : Homepage CTA Button Color Test Hypothesis : statement : "Changing the CTA button from blue to orange will increase clicks" reasoning : "Orange creates more urgency and stands out from our blue brand" Metrics : primary : CTA click rate secondary : - Sign - up conversion - Time on page - Bounce rate Test Design : type : A/B test control : Blue button (

3498db)

variant : Orange button (

e67e22)

traffic_split : 50/50 sample_size_needed : 10 , 000 per variant duration : 14 days minimum Success Criteria : minimum_detectable_effect : 10% statistical_significance : 95% Segmentation : - New vs returning visitors - Mobile vs desktop - Traffic source Statistical Significance Calculator import scipy . stats as stats import numpy as np def calculate_sample_size ( baseline_rate , mde , alpha = 0.05 , power = 0.80 ) : """Calculate required sample size for A/B test.""" effect_size = mde * baseline_rate

Z-scores for significance level and power

z_alpha

stats . norm . ppf ( 1 - alpha / 2 ) z_beta = stats . norm . ppf ( power )

Pooled standard deviation

p1

baseline_rate p2 = baseline_rate * ( 1 + mde ) pooled_var = p1 * ( 1 - p1 ) + p2 * ( 1 - p2 )

Sample size per group

n

( 2 * pooled_var * ( z_alpha + z_beta ) ** 2 ) / ( effect_size ** 2 ) return int ( np . ceil ( n ) ) def analyze_ab_test ( control_visitors , control_conversions , variant_visitors , variant_conversions ) : """Analyze A/B test results.""" control_rate = control_conversions / control_visitors variant_rate = variant_conversions / variant_visitors

Lift calculation

lift

( variant_rate - control_rate ) / control_rate

Statistical test

contingency

[ [ control_conversions , control_visitors - control_conversions ] , [ variant_conversions , variant_visitors - variant_conversions ] ] chi2 , p_value , dof , expected = stats . chi2_contingency ( contingency ) return { 'control_rate' : control_rate , 'variant_rate' : variant_rate , 'lift' : lift , 'lift_percent' : f" { lift : .1% } " , 'p_value' : p_value , 'significant' : p_value < 0.05 , 'confidence' : 1 - p_value } Funnel Analysis Conversion Funnel Tracking -- Funnel analysis query WITH funnel AS ( SELECT user_id , MIN ( CASE WHEN event = 'page_view' THEN timestamp END ) as viewed , MIN ( CASE WHEN event = 'signup_started' THEN timestamp END ) as started , MIN ( CASE WHEN event = 'signup_completed' THEN timestamp END ) as completed , MIN ( CASE WHEN event = 'first_purchase' THEN timestamp END ) as purchased FROM events WHERE timestamp

= CURRENT_DATE - INTERVAL '30 days' GROUP BY user_id ) SELECT COUNT ( viewed ) as step_1_viewed , COUNT ( started ) as step_2_started , COUNT ( completed ) as step_3_completed , COUNT ( purchased ) as step_4_purchased , -- Conversion rates ROUND ( COUNT ( started ) :: decimal / NULLIF ( COUNT ( viewed ) , 0 ) * 100 , 2 ) as view_to_start , ROUND ( COUNT ( completed ) :: decimal / NULLIF ( COUNT ( started ) , 0 ) * 100 , 2 ) as start_to_complete , ROUND ( COUNT ( purchased ) :: decimal / NULLIF ( COUNT ( completed ) , 0 ) * 100 , 2 ) as complete_to_purchase , ROUND ( COUNT ( purchased ) :: decimal / NULLIF ( COUNT ( viewed ) , 0 ) * 100 , 2 ) as overall_conversion FROM funnel ; Cohort Retention Analysis -- Weekly cohort retention WITH cohort_data AS ( SELECT user_id , DATE_TRUNC ( 'week' , first_seen ) as cohort_week , DATE_TRUNC ( 'week' , activity_date ) as activity_week FROM user_activity ) , cohort_size AS ( SELECT cohort_week , COUNT ( DISTINCT user_id ) as users FROM cohort_data GROUP BY cohort_week ) , retention AS ( SELECT c . cohort_week , EXTRACT ( WEEK FROM c . activity_week - c . cohort_week ) as week_number , COUNT ( DISTINCT c . user_id ) as retained_users FROM cohort_data c GROUP BY c . cohort_week , week_number ) SELECT r . cohort_week , cs . users as cohort_size , r . week_number , r . retained_users , ROUND ( r . retained_users:: decimal / cs . users * 100 , 2 ) as retention_rate FROM retention r JOIN cohort_size cs ON r . cohort_week = cs . cohort_week ORDER BY r . cohort_week , r . week_number ; Key Metrics Metric Formula Benchmark Conversion Rate Conversions / Visitors 2-5% (varies) CAC Marketing Spend / New Customers Varies by industry LTV ARPU × Average Lifetime 3x CAC minimum Payback Period CAC / Monthly Revenue per Customer <12 months NRR (Start + Expansion - Churn) / Start MRR 100% K-factor Invites × Conversion Rate 1 for virality DAU/MAU Daily Active / Monthly Active 20-50% Viral Loop Design Types of Virality : inherent : description : Product requires others to use examples : Slack , Zoom , Dropbox sharing k_factor_potential : High (1.5 - 3.0) artificial : description : Incentivized referrals examples : Dropbox space , Uber credits k_factor_potential : Medium (0.5 - 1.5) word_of_mouth : description : Organic recommendations examples : Great products , NPS

50 k_factor_potential : Low - Medium (0.2 - 0.8) Viral Loop Optimization : - Reduce friction in invite flow - Clear value proposition for inviter AND invitee - Multiple sharing channels - Timing of ask (after value delivered) - Social proof in referral message Tools Proficiency Analytics Product: Amplitude, Mixpanel, Heap Web: Google Analytics 4, Plausible Data Warehouse: BigQuery, Snowflake Testing A/B Testing: Optimizely, VWO, LaunchDarkly Feature Flags: Split, Flagsmith Session Recording: FullStory, Hotjar Visualization BI: Tableau, Looker, Mode Dashboards: Metabase, Redash Attribution Mobile: Branch, Adjust, AppsFlyer Web: Segment, mParticle Automation Lifecycle: Iterable, Customer.io, Braze In-app: Appcues, Pendo, Intercom Лучшие практики Hypothesis-driven — каждый эксперимент начинается с гипотезы Statistical rigor — достаточный sample size и significance One variable — тестируйте одну переменную за раз Document learnings — даже failed эксперименты ценны Quick iterations — много маленьких тестов лучше одного большого North Star focus — оптимизируйте главную метрику