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