End-to-End Customer Retention Modeling
Build a defensible churn-prediction workflow from framing and leakage checks through final evaluation and stakeholder communication.
BUSINESS CONTEXT
Why this project matters
A subscription SaaS company wants to prioritize retention outreach, but the team can contact only a limited number of at-risk customers each week. A useful model must respect prediction timing, intervention capacity, and the cost of missed churn.
PROJECT OBJECTIVE
What you are expected to accomplish
Create a reproducible predictive workflow that identifies useful churn risk while documenting its limits, threshold decision, and recommended retention action.
DATASET OVERVIEW
Expected dataset structure
Expect customer-level subscription data with engagement, billing, support, plan, and marketing fields. The learner must decide which signals are valid at prediction time.
Main entities / rows
One row per active customer at a defined scoring date
Target variable
churned within the chosen prediction horizon (Yes/No)
customer_id
stringIdentifier; useful for joins but normally not a predictor.
signup_date
dateAccount start used for time-valid features.
plan_type
categoricalCurrent subscription plan.
monthly_fee
floatRecurring charge, potentially skewed.
sessions_last_30_days
integerRecent engagement.
support_tickets_last_90_days
integerRecent support activity.
payment_failures_last_90_days
integerHistorical payment friction.
last_login_date
datePotential recency feature.
country
categoricalCustomer geography.
contract_type
categoricalCommitment structure.
churned
binaryTarget outcome.
Data quality issues to expect
- Missing values and inconsistent categories require documented treatment.
- Skewed numeric fields and date-derived opportunities need validation.
- Post-cancellation fields or outcome-time notes can leak the target.
QUESTIONS TO ANSWER
Focus the work around meaningful decisions
- 01
What counts as churn, when is the prediction made, and what action follows?
- 02
Which features are available at that time?
- 03
Which metric and threshold fit intervention capacity and error costs?
- 04
Where does the selected model fail?
PROJECT ROADMAP
Move through the case study in a practical sequence
- 01
Step 01
Frame the decision
Write the target, row grain, scoring time, horizon, capacity, costs, and primary metric.
- 02
Step 02
Audit and explore
Inspect schema, target balance, missingness, distributions, identifiers, leakage risks, and useful relationships.
- 03
Step 03
Protect the test set
Justify random or chronological splitting and keep final test data out of development.
- 04
Step 04
Establish baselines
Compare a simple business baseline and logistic-regression baseline before complex models.
- 05
Step 05
Preprocess safely
Use training-fitted Pipeline and ColumnTransformer decisions for numeric and categorical data.
- 06
Step 06
Engineer valid features
Propose time-valid engagement, recency, and rate features and check availability.
- 07
Step 07
Compare models
Compare a sensible subset with consistent cross-validation, metric, complexity, and interpretability notes.
- 08
Step 08
Tune and choose a threshold
Tune only on development data, then choose an operating point aligned with outreach capacity.
- 09
Step 09
Final evaluation and errors
Evaluate once on test data, inspect errors and subgroups, and compare with baseline.
- 10
Step 10
Reproduce and communicate
Document seeds, features, configuration, raw-row inference flow, and stakeholder recommendation.
TASKS / MILESTONES
Concrete work to complete
- Write a problem-framing note before modeling.
- Produce a concise EDA and leakage audit.
- Create a protected test set and development workflow.
- Build baseline and pipeline-based candidate models.
- Compare models with cross-validation and modest tuning.
- Analyze probabilities, thresholds, confusion matrix, errors, and subgroups.
- Write a README and retention memo with assumptions and limitations.
SUGGESTED VISUALIZATIONS
Visuals worth creating
- Target balance and segment charts
- Feature distributions and missingness summary
- Model-comparison table
- Precision-recall or threshold-capacity chart
- Confusion matrix and subgroup error summary
DELIVERABLES
What the learner should produce
- Problem-framing note
- EDA and leakage audit
- Reproducible preprocessing/model pipeline
- Model comparison and threshold analysis
- Final protected-test report
- Error analysis
- README and stakeholder retention memo
SUCCESS CRITERIA
What strong completion looks like
- Prediction time and target are explicit.
- No obvious leakage and a protected test set are maintained.
- A baseline and fair model comparison are included.
- Pipeline preprocessing is reproducible and training-fitted.
- Metric and threshold reflect business action and capacity.
- Final conclusions communicate evidence and limits rather than a headline score.
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
- Frame and evaluate a complete tabular classification workflow.
- Prevent leakage through valid splits and preprocessing.
- Compare models, thresholds, and errors honestly.
- Communicate a reproducible retention recommendation.
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