Customer Churn Analysis

Business Analyst & Data Analyst Portfolio Project

Domain: Telecom | CRM Analytics  |  Role: Business Analyst / Data Analyst  |  Year: 2025

G Yasaswini  |  yashaswini2519@gmail.com  |  linkedin.com/in/g-yasaswini

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Tools & Technologies

Category	Tool / Technology
Data Analysis & EDA	Python — Pandas, NumPy, Matplotlib, Seaborn
Database & SQL	MySQL Workbench — data cleaning, querying, segmentation
Business Intelligence	Power BI — interactive dashboard with drill-through & slicers
Notebook Environment	Jupyter Notebook
Version Control	Git & GitHub

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Project Overview

This project analyses customer churn patterns for a telecom subscription business using Exploratory Data Analysis (EDA), SQL-based querying, and a rule-based risk scoring framework. The objective is to identify high-risk customers and deliver actionable, revenue-focused retention recommendations — without relying on machine learning.

The methodology mirrors the analytical workflow used by Business Analysts and Data Analysts in telecom and subscription industries: moving from raw data to business decision.

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Business Problem Statement

Customer churn directly impacts revenue, customer lifetime value (LTV), and long-term business growth. This analysis was scoped around four core business questions:

• Why are customers leaving? — Root cause analysis of key churn drivers
• Which customers are most at risk? — Segmentation and scoring model
• Who should we prioritise for retention? — Ranked outreach target lists
• What is the financial impact? — Quantified monthly revenue at risk

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Key Performance Indicators

Metric	Value	Context
Total Customers	7,043	Full dataset analysed
Customers Churned	1,869	26.5% overall churn rate
Monthly Revenue at Risk	$139,131	Based on avg charge of $74.44 per churned customer
Avg Tenure — Churned	18 months	vs. 38 months for retained customers

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Key Findings

Churn by Contract Type

Contract Type	Churn Rate	Insight
Month-to-month	42.7%	15x more likely to churn than 2-year contract customers
One year	11.3%	Moderate risk
Two year	2.8%	Lowest churn — strongest retention signal

Churn by Customer Tenure

Tenure	Churn Rate
0 – 11 months	48.3%
12 – 23 months	29.5%
24 – 35 months	22.0%
36 – 47 months	19.5%
48 – 59 months	15.0%
60 – 71 months	8.3%

The first 12 months represent the highest-risk window. Nearly 1 in 2 new customers churn. Retention efforts should be front-loaded at onboarding.

Monthly Charges vs. Churn

Segment	Average Monthly Charge
Churned Customers	$74.44
Retained Customers	$61.27

Higher-paying customers are significantly more likely to churn, suggesting a perceived value gap in premium and fiber optic plans.

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Rule-Based Churn Risk Scoring Model

A transparent, interpretable 3-factor scoring model designed to be understood and actioned by non-technical business stakeholders — no machine learning required.

Risk Factor	Condition	Score
Contract Risk	Month-to-month contract	+1
Tenure Risk	Tenure under 12 months	+1
Price Risk	Monthly charges above dataset median	+1

Total Score Range: 0 (Low Risk) → 3 (Critical Risk)

Risk Score Validation

Risk Score	Customers	Actual Churn Rate	Interpretation
0 — Low Risk	1,546	3.2%	Stable, long-tenure customers
1 — Low-Medium	2,305	12.7%	Monitor — flag for billing changes
2 — High Risk	2,422	40.7%	Priority retention outreach
3 — Critical Risk	770	70.5%	Immediate intervention required

Score 3 customers are 22x more likely to churn than Score 0. This segment (10.9% of customers) drives the majority of monthly revenue loss.

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SQL Analysis

All data validation, cleaning, and analysis was performed in MySQL Workbench across three structured query files:

File	Queries
datacleaning.sql	Total record count · NULL checks across 4 critical fields · DELETE for missing customer_id or churn · DISTINCT churn value validation
churnanalysis.sql	Overall churn rate with % · Churn rate by contract type · Churn rate by tenure group (6 buckets) · Churn rate by internet service type · Avg monthly charges by churn status · Monthly revenue at risk by risk tier
risk_segmentation.sql	Threshold-based High / Medium / Low risk assignment · Segment distribution with % of total · Actual churn rate validation per segment

Note: churnanalysis.sql and risk_segmentation.sql use two complementary segmentation approaches. The threshold-based SQL model and the 3-factor Python scoring model are both documented and cross-referenced within the files.

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Power BI Dashboard

An interactive business dashboard built with six components, enabling non-technical stakeholders to self-serve insights:

Component	Description
KPI Cards	Total customers, churn rate, churned count at a glance
Risk Distribution	Donut chart — High / Medium / Low risk segment breakdown
Churn by Internet Service	Bar chart — fiber optic users show the highest churn rate
Churn by Contract Type	Bar chart — month-to-month contract dominates churn volume
High-Risk Customer Table	Drill-through table with CustomerID, contract, tenure, charges
Interactive Slicers	Filter by contract type, internet service, and gender

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Business Recommendations

Recommendation	Target Segment	Expected Outcome
Offer contract upgrade incentives (e.g. discounted annual plan)	Month-to-month customers, first 6 months	Reduce the 42.7% month-to-month churn rate
Launch early-tenure retention outreach programme	Customers with tenure < 12 months, Risk Score 2–3	Address the highest-risk churn window
Pricing review for fiber optic plans	Fiber optic + high monthly charges segment	Close the perceived value gap driving churn
Proactive support and loyalty benefits	Senior citizens (41.7% churn rate)	Improve retention in an underserved segment
Deploy risk score as onboarding early warning	All new customers from day one	Enable proactive, data-driven intervention

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Project Workflow

Phase	Activity	Tool
1	Data Cleaning & Validation — 11 missing values resolved via business logic	Python + SQL
2	Exploratory Data Analysis — distributions, correlations, segment breakdowns	Python (Pandas, Seaborn)
3	Root Cause Analysis — key churn drivers identified across all dimensions	Python + SQL
4	Rule-Based Risk Scoring Model — 3-factor model, no ML dependency	Python + SQL (CASE)
5	Power BI Dashboard — 6 interactive components with slicers and drill-through	Power BI
6	Business Insights & Recommendations — stakeholder-ready output	Report / README

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Repository Structure

customer-churn-analysis/
│
├── README.md
├── data/
│   └── churn.csv                        # Raw dataset (7,043 records, 33 features)
├── notebooks/
│   └── churn_analysis.ipynb             # EDA, data cleaning, and risk scoring model
├── sql/
│   ├── datacleaning.sql
│   ├── churnanalysis.sql
│   └── risk_segmentation.sql
├── dashboard/
│   └── Customer_Churn_Dashboard.pbix    # Power BI file
└── outputs/
    └── charts, scoring outputs, segment tables

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This portfolio project demonstrates Business Analyst and Data Analyst competencies including exploratory data analysis, SQL querying, rule-based modelling, Power BI dashboarding, and data-driven business recommendations — applicable to BA and DA roles across telecom, fintech, and subscription industries.