{"id":31177854,"url":"https://github.com/aarushishukla3/aarushi_shukla_sql_chinook_analysis","last_synced_at":"2025-09-19T14:32:12.964Z","repository":{"id":312278206,"uuid":"1046953300","full_name":"aarushishukla3/Aarushi_Shukla_SQL_Chinook_Analysis","owner":"aarushishukla3","description":"This project leverages SQL-driven data analysis on the Chinook music database to uncover customer trends, sales performance, and genre preferences.","archived":false,"fork":false,"pushed_at":"2025-08-29T13:54:14.000Z","size":2244,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-29T16:42:01.576Z","etag":null,"topics":["mysql-database","powerpoint","sql","word"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aarushishukla3.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2025-08-29T13:51:31.000Z","updated_at":"2025-08-29T13:55:54.000Z","dependencies_parsed_at":"2025-08-29T16:52:07.170Z","dependency_job_id":null,"html_url":"https://github.com/aarushishukla3/Aarushi_Shukla_SQL_Chinook_Analysis","commit_stats":null,"previous_names":["aarushishukla3/aarushi_shukla_sql_chinook_analysis"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/aarushishukla3/Aarushi_Shukla_SQL_Chinook_Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aarushishukla3%2FAarushi_Shukla_SQL_Chinook_Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aarushishukla3%2FAarushi_Shukla_SQL_Chinook_Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aarushishukla3%2FAarushi_Shukla_SQL_Chinook_Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aarushishukla3%2FAarushi_Shukla_SQL_Chinook_Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aarushishukla3","download_url":"https://codeload.github.com/aarushishukla3/Aarushi_Shukla_SQL_Chinook_Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aarushishukla3%2FAarushi_Shukla_SQL_Chinook_Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":275952433,"owners_count":25558702,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-19T02:00:09.700Z","response_time":108,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["mysql-database","powerpoint","sql","word"],"created_at":"2025-09-19T14:31:09.010Z","updated_at":"2025-09-19T14:32:12.939Z","avatar_url":"https://github.com/aarushishukla3.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Aarushi_Shukla_SQL_Chinook_Analysis\n\nProject Summary – Chinook Analysis\n\nThis project dives into the Chinook music database to uncover sales performance, customer behavior, and market opportunities using SQL-based data analysis techniques.\n\nData Quality Check\n\nIdentified duplicates in Customer and Track tables; handled null values using COALESCE.\n\nEnsured data integrity by retaining valid business cases, creating a clean dataset for deeper analysis.\n\nKey Insights \u0026 Trends\n\nTop-Selling Tracks \u0026 Artists (USA): War Pigs by Cake leads the charts; Rock dominates with 53% of sales. Nirvana and The Doors remain high-demand artists.\n\nCustomer Demographics: Majority from the USA, followed by Canada, Brazil, and France → highlights geographical segmentation.\n\nRevenue \u0026 Invoice Trends: Highest revenue comes from Prague (Czech Republic), Mountain View (USA), and London (UK) → strong regional performance clusters.\n\nCustomer Segmentation:\n\nTop customers from Czech Republic, Ireland, India, and Brazil drive the most revenue.\n\nLong-term customers show higher frequency, basket size, and average order value, proving stronger loyalty and retention.\n\nChurn Analysis: Only 1.72% churn rate, but 59 inactive customers need re-engagement strategies.\n\nGenre Performance: Rock, Alternative \u0026 Punk, and Metal are the highest revenue generators.\n\nAffinity Analysis: Strong cross-sell opportunities → Rock \u0026 Metal often bought together; albums like Mezmerize and Are You Experienced are popular combos.\n\nStrategic Recommendations\n\n🎸 Focus on High-Performing Genres – Spotlight Rock, Alternative \u0026 Punk, Metal in campaigns; create genre-specific bundles.\n\n🌍 Regional Strategy – Offer premium packages in high-value markets (Canada, Germany) and discount-led campaigns in growth markets (Brazil, France).\n\n💡 Retention \u0026 Loyalty – Launch reward programs, early-access deals, and personalized offers to reduce churn and re-engage inactive customers.\n\n🔗 Smart Cross-Selling – Use affinity insights to build bundles and recommendations (genre, artist, and album-based).\n\n📊 Campaign Impact Tracking – Continuously monitor pre-, during-, and post-campaign KPIs to double down on high-impact strategies.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faarushishukla3%2Faarushi_shukla_sql_chinook_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faarushishukla3%2Faarushi_shukla_sql_chinook_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faarushishukla3%2Faarushi_shukla_sql_chinook_analysis/lists"}