Predicting Purchase Propensity After a Campaign (5% Positives)

You previously ran a marketing campaign to 10,000 customers and observed 500 purchases (5% positive rate). You now want to build a model to score customers for the next campaign so you can target those most likely to purchase under a fixed budget.

Design an end-to-end approach that includes:

1. Baseline Model and Features

• Start with logistic regression. Describe:
  • Feature engineering: numeric handling, categorical encoding, scaling, missing values, interactions.
  • Data splitting strategy (temporal/stratified), pipelines, and prevention of leakage.

2. Class Imbalance

• Compare class_weight, random over/under-sampling, and SMOTE. State which metric(s) you’ll optimize and why (e.g., PR-AUC, recall at fixed precision, cost-sensitive loss).

3. Thresholding, Calibration, and Budgeted Targeting

• Explain ranking vs. classification thresholds, probability calibration (Platt scaling or isotonic), and how to choose top-N customers to target under a fixed budget.

4. Validation and Monitoring

• Describe stratified cross-validation, how you will report confidence intervals, and how you will monitor post-deployment drift and business lift.

5. Feature Selection and Interpretability

• List at least two feature selection methods (e.g., L1 penalty, mutual information, recursive feature elimination) and how you would guard against overfitting while preserving interpretability.