analytics-product

# ANALYTICS-PRODUCT — Decida com Dados ## Overview Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto. Ativar para: configurar tracking de eventos, criar funil de conversao, analise de cohort, retencao, DAU/MAU, feature flags, A/B testing, north star metric, OKRs, dashboard de produto. ## When to Use This Skill - When you need specialized assistance with this domain ## Do Not Use This Skill When - The task is unrelated to analytics product - A simpler, more specific tool can handle the request - The user needs general-purpose assistance without domain expertise ## How It Works ``` [objeto]_[verbo_passado] Correto: user_signed_up, conversation_started, upgrade_completed Errado: signup, click, conversion ``` ## Analytics-Product — Decida Com Dados > "In God we trust. All others must bring data." — W. Edwards Deming --- ## Eventos Essenciais Da Auri ```python AURI_EVENTS = { # Aquisicao "user_signed_up": {"props": ["source", "medium", "campaign"]}, "onboarding_started": {"props": ["step_count"]}, "onboarding_completed": {"props": ["time_to_complete", "steps_skipped"]}, # Ativacao "first_conversation": {"props": ["intent", "response_time"]}, "aha_moment_reached": {"props": ["trigger", "session_number"]}, "feature_discovered": {"props": ["feature_name", "discovery_method"]}, # Retencao "conversation_started": {"props": ["intent", "user_tier", "device"]}, "conversation_completed":{"props": ["messages_count", "duration", "rating"]}, "session_started": {"props": ["days_since_last", "platform"]}, # Receita "upgrade_viewed": {"props": ["trigger", "current_tier"]}, "upgrade_started": {"props": ["target_tier", "trigger"]}, "upgrade_completed": {"props": ["tier", "plan", "revenue"]}, "subscription_canceled": {"props": ["reason", "tier", "tenure_days"]}, "payment_failed": {"props": ["attempt_count", "error_code"]}, } ``` ## Implementacao Posthog (Python) ```python from posthog import Posthog import os posthog = Posthog( project_api_key=os.environ["POSTHOG_API_KEY"], host=os.environ.get("POSTHOG_HOST", "https://app.posthog.com") ) def track(user_id: str, event: str, properties: dict = None): posthog.capture( distinct_id=user_id, event=event, properties=properties or {} ) def identify(user_id: str, traits: dict): posthog.identify( distinct_id=user_id, properties=traits ) ## Uso: track("user_123", "conversation_started", { "intent": "business_advice", "device": "alexa", "user_tier": "pro" }) ``` --- ## Funil De Ativacao Auri ``` Visita landing page (100%) | [meta: 40%] Clicou "Experimentar" (40%) | [meta: 70%] Completou cadastro (28%) | [meta: 60%] Fez primeira conversa (17%) <- AHA MOMENT | [meta: 50%] Voltou no dia seguinte (8.5%) | [meta: 40%] Usou 3+ dias na semana (3.4%) | [meta: 20%] Converteu para Pro (0.7%) ``` ## Otimizando O Funil ``` Para cada drop-off > benchmark: 1. Identificar: onde exatamente o usuario sai? 2. Entender: por que? (session recordings, surveys) 3. Hipotese: qual mudanca poderia melhorar? 4. Testar: A/B test com amostra estatisticamente significante 5. Medir: 2 semanas minimo, p-value < 0.05 6. Aprender: mesmo se falhar, entende-se o usuario melhor ``` --- ## Analise De Cohort (Retencao Semanal) ```python def calculate_cohort_retention(events_df): """ events_df: DataFrame com colunas [user_id, event_date, event_name] Retorna: matriz de retencao [cohort_week x week_number] """ import pandas as pd first_session = events_df[events_df.event_name == "session_started"] \ .groupby("user_id")["event_date"].min() \ .dt.to_period("W") sessions = events_df[events_df.event_name == "session_started"].copy() sessions["cohort"] = sessions["user_id"].map(first_session) sessions["weeks_since"] = ( sessions["event_date"].dt.to_period("W") - sessions["cohort"] ).apply(lambda x: x.n) cohort_data = sessions.groupby(["cohort", "weeks_since"])["user_id"].nunique() cohort_sizes = cohort_data.unstack().iloc[:, 0] retention = cohort_data.unstack().divide(cohort_sizes, axis=0) * 100 return retention ``` ## Benchmarks De Retencao (Assistentes De Voz) | Semana | Pessimo | Ok | Bom | Excelente | |--------|---------|-----|-----|-----------| | W1 | <20% | 20-35% | 35-50% | >50% | | W4 | <10% | 10-20% | 20-30% | >30% | | W8 | <5% | 5-12% | 12-20% | >20% | --- ## Definindo A North Star Da Auri ``` Framework: 1. O que cria valor real para o usuario? -> Conversas que geram insight/acao 2. O que prediz crescimento de longo prazo? -> Usuarios com 3+ conv/semana 3. Como medir? -> "Weekly Active Conversationalists" (WAC) North Star: WAC (Weekly Active Conversationalists) Definicao: Usuarios com >= 3 conversas na semana que duraram >= 2 minutos Meta Ano 1: 10.000 WAC Meta Ano 2: 100.000 WAC ``` ## Dashboard North Star ```python def calculate_north_star(db): wac = db.query(""" SELECT COUNT(DISTINCT user_id) as wac FROM conversations WHERE created_at >= NOW() - INTERVAL '7 days' AND duration_seconds >= 120 GROUP BY user_id HAVING COUNT(*) >= 3 """).scalar() return { "wac": wac, "wow_growth": calculate_wow_growth(db, "wac"), "target": 10000, "progress": f"{wac/10000*100:.1f}%" } ``` --- ## Feature Flags Com Posthog ```python def is_feature_enabled(user_id: str, feature: str) -> bool: return posthog.feature_enabled(feature, user_id) if is_feature_enabled(user_id, "new-onboarding-v2"): show_new_onboarding() else: show_old_onboarding() ``` ## Calculadora De Significancia Estatistica ```python from scipy import stats import numpy as np def ab_test_significance( control_conversions: int, control_visitors: int, variant_conversions: int, variant_visitors: int, confidence: float = 0.95 ) -> dict: control_rate = control_conversions / control_visitors variant_rate = variant_conversions / variant_visitors lift = (variant_rate - control_rate) / control_rate * 100 _, p_value = stats.chi2_contingency([ [control_conversions, control_visitors - control_conversions], [variant_conversions, variant_visitors - variant_conversions] ])[:2] significant = p_value < (1 - confidence) return { "control_rate": f"{control_rate*100:.2f}%", "variant_rate": f"{variant_rate*100:.2f}%", "lift": f"{lift:+.1f}%", "p_value": round(p_value, 4), "significant": significant, "recommendation": "Deploy variant" if significant and lift > 0 else "Keep control" } ``` --- ## 6. Comandos | Comando | Acao | |---------|------| | `/event-taxonomy` | Define taxonomia de eventos | | `/funnel-analysis` | Analisa funil de conversao | | `/cohort-retention` | Calcula retencao por cohort | | `/north-star` | Define ou revisa North Star Metric | | `/ab-test` | Calcula significancia de A/B test | | `/dashboard-setup` | Cria dashboard de produto | | `/okr-template` | Template de OKRs para produto | ## Best Practices - Provide clear, specific context about your project and requirements - Review all suggestions before applying them to production code - Combine with other complementary skills for comprehensive analysis ## Common Pitfalls - Using this skill for tasks outside its domain expertise - Applying recommendations without understanding your specific context - Not providing enough project context for accurate analysis ## Related Skills - `growth-engine` - Complementary skill for enhanced analysis - `monetization` - Complementary skill for enhanced analysis - `product-design` - Complementary skill for enhanced analysis - `product-inventor` - Complementary skill for enhanced analysis ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.