BrightCart, an e-commerce retailer sending 8-10 million promotional emails and push notifications per month, wants to use predictive analytics to improve targeting. The marketing team currently sends broad campaigns with low conversion and wants a model that predicts which customers are likely to purchase within 7 days of receiving an offer.
You are given a historical campaign dataset where each row represents a customer-campaign impression. The goal is to predict whether the customer will convert after receiving the message.
| Feature Group | Count | Examples |
|---|---|---|
| Customer profile | 8 | age_bucket, region, loyalty_tier, acquisition_channel |
| Purchase history | 12 | orders_30d, avg_order_value, days_since_last_purchase, category_affinity_score |
| Engagement | 10 | email_open_rate_90d, push_click_rate_30d, sessions_7d, wishlist_adds_30d |
| Campaign metadata | 7 | channel, discount_pct, campaign_type, send_hour, product_category |
| Derived temporal features | 5 | day_of_week, weekend_flag, days_since_last_campaign, campaign_frequency_14d |
A good solution should improve campaign targeting enough to support selective sends. Aim for ROC-AUC >= 0.82, PR-AUC >= 0.38, and precision in the top 20% scored customers >= 0.30. The model should also produce interpretable feature importance for marketers.