Customer Churn Analytics Dashboard

End-to-end analytics pipeline and executive dashboard for analyzing customer churn for a subscription-based telecom service. Uses the Telco Customer Churn dataset with a SQL → R → Tableau stack: raw data modeled and cleaned in SQL, transformed and aggregated in R, and visualized in a Tableau dashboard for churn patterns, high-risk segments, and revenue at risk.

Tableau Dashboard GitHub Repo

Demo

Preview

Customer Churn Tableau dashboard with KPIs and segment views — Executive Tableau dashboard: churn rate, revenue at risk, and segment breakdowns (contract type, tenure, internet service). Heatmaps highlight high-risk combinations for targeted retention.

Problem & Context

The goal was to build a reproducible analytics pipeline from raw Telco churn data to dashboard-ready outputs, identify churn patterns and high-risk segments (contract type, tenure, services), quantify revenue at risk (MRR from churned or at-risk customers), and deliver an executive-facing Tableau dashboard with clear KPIs and actionable insights. The pipeline separates ingest (SQL), transform (R), and visualize (Tableau) for maintainability.

What It Does

SQL layer — Staging tables, cleaning and normalization, KPI views for churn rate and revenue at risk
R layer — Extract from SQLite, build KPI and driver tables, export CSVs for Tableau (DBI, RSQLite, dplyr, tidyr, readr)
Tableau dashboard — KPI summary, churn by segment (contract, tenure, internet, payment), Contract × Tenure and Contract × Internet heatmaps, MRR at risk views
Documentation — report.md for methodology and reproducibility

Tech Stack

SQL SQLite R RSQLite DBI dplyr tidyr readr Tableau

Key Takeaways

Combining SQL for modeling and cleaning, R for aggregation and export, and Tableau for storytelling produces a clear path from raw data to executive-ready insights. The dashboard answers which segments churn most and where revenue is at risk.

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