{"id":31740407,"url":"https://github.com/joaquinmoron/marketing-case","last_synced_at":"2026-04-15T07:34:21.310Z","repository":{"id":317589972,"uuid":"1068072360","full_name":"joaquinmoron/marketing-case","owner":"joaquinmoron","description":"Caso de Marketing — cohortes, LTV y retención con Python + SQL (modelo analítico y KPIs).","archived":false,"fork":false,"pushed_at":"2025-10-01T20:17:30.000Z","size":10,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-10-09T10:18:39.798Z","etag":null,"topics":["cohort-analysis","ltv","marketing","marketing-analytics","pandas","python","retention","sql"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/joaquinmoron.png","metadata":{"files":{"readme":"README.md/README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-10-01T20:13:42.000Z","updated_at":"2025-10-01T20:18:24.000Z","dependencies_parsed_at":"2025-10-09T10:18:39.715Z","dependency_job_id":null,"html_url":"https://github.com/joaquinmoron/marketing-case","commit_stats":null,"previous_names":["joaquinmoron/marketing-case"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/joaquinmoron/marketing-case","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joaquinmoron%2Fmarketing-case","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joaquinmoron%2Fmarketing-case/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joaquinmoron%2Fmarketing-case/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joaquinmoron%2Fmarketing-case/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/joaquinmoron","download_url":"https://codeload.github.com/joaquinmoron/marketing-case/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joaquinmoron%2Fmarketing-case/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31831843,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-15T07:17:56.427Z","status":"ssl_error","status_checked_at":"2026-04-15T07:17:30.007Z","response_time":63,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cohort-analysis","ltv","marketing","marketing-analytics","pandas","python","retention","sql"],"created_at":"2025-10-09T10:18:36.727Z","updated_at":"2026-04-15T07:34:21.305Z","avatar_url":"https://github.com/joaquinmoron.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Marketing Case — SQL + Python (Olist)\n\nAnálisis de cohortes, retención y LTV con **Python** y **SQL** usando el dataset público de Olist.\n\n## 🎯 Objetivo\n- Limpiar y preparar datos de ventas.\n- Construir **cohortes** (adquisición/retención) y **LTV** simple.\n- Modelar en **SQL** (staging → facts/dims → vistas KPI).\n- Separar **clientes nuevos vs. recurrentes** y su revenue.\n\n## 📂 Estructura\n\n## 🐍 Python (carpeta `python/`)\n- `01_eda.py` → limpieza de órdenes  \n- `02_items.py` → agregación de ítems  \n- `03_payments.py` → consolidación de pagos  \n- `04_customers.py` → validación de clientes  \n- `05_fact_orders.py` → construcción de `fact_orders`  \n- `06_cohortes_ltv.py` → cohortes (retención) y LTV simple\n\n\u003e **Nota:** los CSV intermedios se guardan en `data_processed/` (local, no se sube).\n\n## 🗄️ SQL (carpeta `sql/`) — orden sugerido\n1. `01_load_raw.sql`  \n2. `02_build_fact_orders.sql`  \n3. `03_dim_date.sql`  \n4. `04_views.sql`  \n5. `05_kpi.sql`  \n6. `06_new_returning.sql`\n\nEsto crea `dim_date`, consolida `fact_orders` y genera vistas/KPIs (AOV, items/order, nuevos vs. recurrentes).\n\n## ▶️ Cómo correr\n1. Clonar el repo.  \n2. (Opcional) Crear entorno:\n   ```bash\n   python -m venv .venv\n   .venv\\Scripts\\activate   # Windows\n   pip install -r requirements.txt  # si corresponde\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoaquinmoron%2Fmarketing-case","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjoaquinmoron%2Fmarketing-case","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoaquinmoron%2Fmarketing-case/lists"}