Analytical product

--- name: analytics-product description: "Analytics de produto : PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto." risk: none source: community date_added: '2026-03-06' author: renat tags: - analytics - product - metrics - posthog - mixpanel tools: - claude-code - antigravity - cursor - gemini-cli - codex-cli --- # 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 | <