{"id":25239360,"url":"https://github.com/gokulgowthams/customer-risk-analysis","last_synced_at":"2026-01-20T16:02:36.424Z","repository":{"id":252412913,"uuid":"840369247","full_name":"GokulGowthamS/Customer-Risk-Analysis","owner":"GokulGowthamS","description":null,"archived":false,"fork":false,"pushed_at":"2025-03-17T10:54:22.000Z","size":8861,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T19:43:19.821Z","etag":null,"topics":["data-visualization","powerbi"],"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/GokulGowthamS.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}},"created_at":"2024-08-09T14:48:07.000Z","updated_at":"2025-03-22T12:03:03.000Z","dependencies_parsed_at":null,"dependency_job_id":"4c0f8c30-e89f-4ce9-b6ed-8d38dbc82a3d","html_url":"https://github.com/GokulGowthamS/Customer-Risk-Analysis","commit_stats":null,"previous_names":["gokulgowthams/customer-risk-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/GokulGowthamS/Customer-Risk-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GokulGowthamS%2FCustomer-Risk-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GokulGowthamS%2FCustomer-Risk-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GokulGowthamS%2FCustomer-Risk-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GokulGowthamS%2FCustomer-Risk-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/GokulGowthamS","download_url":"https://codeload.github.com/GokulGowthamS/Customer-Risk-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GokulGowthamS%2FCustomer-Risk-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28606299,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-20T14:45:23.139Z","status":"ssl_error","status_checked_at":"2026-01-20T14:44:16.929Z","response_time":117,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":["data-visualization","powerbi"],"created_at":"2025-02-11T18:27:35.689Z","updated_at":"2026-01-20T16:02:36.408Z","avatar_url":"https://github.com/GokulGowthamS.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer Retention and Risk Analysis Dashboard for Internet Service Provider\n\nThis README provides an overview of the Customer Retention and Risk Analysis Dashboard designed for an Internet Service Provider (ISP). The dashboard leverages Microsoft Power BI to deliver actionable insights into customer behavior, retention rates, and potential churn risks, enabling data-driven decision-making to enhance customer loyalty and reduce churn.\n\nThe purpose of this dashboard is to:\n\nMonitor Customer Retention: Track and analyze customer retention rates over time, identifying trends and areas of concern.\nIdentify High-Risk Customers: Pinpoint customers at high risk of churning based on key indicators such as service usage, customer support interactions, and demographic factors.\nOptimize Retention Strategies: Provide insights to refine customer engagement strategies, reduce churn, and improve overall customer satisfaction.\n\nKey Features\n1. Customer Overview\nCustomer Demographics: Visualizes customer data segmented by age, gender, location, and whether they have partners or dependents. This helps in understanding the demographic profile of your customer base.\nAccount Information: Displays key details such as contract type, payment method, monthly charges, and tenure as a customer.\n2. Churn Analysis\nChurn Rate Over Time: Tracks the churn rate across different time periods, identifying spikes or declines in customer retention.\nHigh-Risk Customer Identification: Highlights customers at risk of churning based on factors like low service usage, frequent complaints, or long resolution times.\nChurn Drivers: Analyzes factors contributing to churn, such as dissatisfaction with services, billing issues, or technical problems.\n3. Service Usage Analysis\nService Adoption: Shows the distribution of customers across different services (e.g., internet, streaming, tech support), helping to identify popular and underutilized services.\nMultiple Service Users: Identifies customers who subscribe to multiple services, as these customers are typically more loyal and less likely to churn.\n4. Customer Support Analysis\nTicket Analysis: Tracks the number of support tickets (administrative and technical) raised by customers, helping to identify common issues and areas for improvement.\nResolution Time: Measures the average time taken to resolve customer issues, with an emphasis on minimizing delays that could lead to dissatisfaction.\n5. Predictive Risk Modeling (Optional)\nChurn Prediction: Utilizes machine learning models to predict which customers are most likely to churn in the near future, allowing proactive engagement.\nRisk Scoring: Assigns a risk score to each customer based on their behavior, service usage, and interaction history.\n\nThe dashboard is powered by data from the following sources:\n\nCustomer Database: Contains demographic details, account information, and churn status.\nService Usage Logs: Tracks customer interactions with various services provided by the ISP.\nCustomer Support System: Records support tickets and resolution details.\n\nThis dashboard is a powerful tool for ISP stakeholders to gain a deeper understanding of customer behavior, identify risks, and implement strategies to improve retention. Regular use will help maintain a loyal customer base and drive long-term business success.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgokulgowthams%2Fcustomer-risk-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgokulgowthams%2Fcustomer-risk-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgokulgowthams%2Fcustomer-risk-analysis/lists"}