Customer Churn Analysis
Analyze customer behavior and identify the patterns most strongly associated with churn risk.
BUSINESS CONTEXT
Why this project matters
A subscription business is losing customers faster than expected. The retention team needs a clear view of which customer segments are churning, what behaviors show early warning signs, and where intervention efforts should focus first.
PROJECT OBJECTIVE
What you are expected to accomplish
Investigate the customer base, calculate churn patterns across meaningful segments, and turn the analysis into practical retention recommendations that a business stakeholder could act on.
DATASET OVERVIEW
Expected dataset structure
Expect a customer-level table covering subscriptions, billing behavior, support activity, and whether the customer churned within a recent evaluation window.
Main entities / rows
One row per customer account
Target variable
churned (Yes/No)
customer_id
stringUnique identifier for each customer account.
tenure_months
integerNumber of months the customer has been active.
contract_type
categoricalMonthly, yearly, or multi-year contract plan.
monthly_charges
floatCurrent recurring subscription charge.
support_tickets_last_90d
integerRecent support interactions that may signal friction.
auto_pay
booleanWhether the customer uses automatic payment.
internet_service
categoricalPrimary service package used by the customer.
churned
binaryTarget flag indicating whether the customer left.
Data quality issues to expect
- Tenure or billing fields may contain missing values for newer accounts or migration cases.
- Categorical service labels may be inconsistently capitalized across source systems.
- A few customers may have duplicate records because of plan changes or billing merges.
QUESTIONS TO ANSWER
Focus the work around meaningful decisions
- 01
Which contract types show the highest churn rate?
- 02
How does churn differ across tenure bands and monthly charge levels?
- 03
Are customers with more recent support interactions churning at a higher rate?
- 04
Which service combinations or customer segments appear most retention-sensitive?
- 05
What practical actions could reduce churn in the highest-risk segments?
PROJECT ROADMAP
Move through the case study in a practical sequence
- 01
Step 01
Understand the retention goal
Clarify how churn is defined and which business decision the analysis will support.
- 02
Step 02
Inspect the customer table
Check record counts, data types, missingness, and whether each row truly represents one customer.
- 03
Step 03
Clean the dataset
Resolve duplicates, standardize categories, and handle missing values in a documented way.
- 04
Step 04
Profile churn with EDA
Measure overall churn and compare it across contract, tenure, billing, and service segments.
- 05
Step 05
Build useful segment features
Create tenure bands, charge buckets, or service-count features that sharpen the analysis.
- 06
Step 06
Optional baseline modeling
If you choose, test a simple classifier to compare feature importance against your EDA findings.
- 07
Step 07
Communicate business recommendations
Summarize the strongest churn patterns and suggest realistic retention actions.
TASKS / MILESTONES
Concrete work to complete
- Calculate the overall churn rate and confirm the analysis period.
- Inspect missing values and document which fields need cleaning before analysis.
- Group churn by contract type, tenure band, and payment method.
- Compare monthly charges for churned versus retained customers.
- Analyze churn by support-ticket frequency and service package combinations.
- Create 2-3 segment features that make the patterns easier to explain.
- Write final retention recommendations tied to specific segments or behaviors.
SUGGESTED VISUALIZATIONS
Visuals worth creating
- Churn rate by contract type bar chart
- Tenure distribution split by churn status
- Monthly charges box plot by churn status
- Service-segment comparison chart
- Heatmap of churn rate across tenure and charge bands
DELIVERABLES
What the learner should produce
- Cleaned analysis-ready customer dataset
- Notebook or report documenting the workflow
- 5-8 useful visualizations tied to churn questions
- Segment-level churn insights
- Final retention recommendations in plain business language
SUCCESS CRITERIA
What strong completion looks like
- The project clearly defines churn and the grain of the dataset.
- The analysis identifies meaningful churn differences across customer segments.
- Cleaning choices and assumptions are documented rather than hidden.
- Recommendations follow directly from the evidence in the analysis.
GUIDANCE
Support when you need a nudge
Reveal practical hints, checkpoints, and framing help without exposing a full finished solution.
Complete this project when the work is yours
Use this only after you have worked through the roadmap, milestones, and your own analysis.
KEY LEARNING OUTCOMES
Skills reinforced by this project
- Translate a retention question into a structured analysis workflow.
- Use EDA to find actionable customer-risk patterns.
- Handle missing values and noisy categories in a business dataset.
- Present churn insights in terms a non-technical team can use.
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