Ancestry Marketing sends millions of lifecycle and promotional emails each month across Ancestry subscriptions and DNA-related offers. Your task is to build a model that predicts whether a recipient will convert within 7 days of receiving an email so the team can prioritize high-value audiences and improve campaign efficiency.
You are given one row per delivered email from the last 12 months, with historical user behavior and campaign metadata available at send time.
| Feature Group | Count | Examples |
|---|---|---|
| User engagement | 12 | opens_last_30d, clicks_last_30d, sessions_last_14d, days_since_last_visit |
| Subscription & account | 10 | current_plan, tenure_days, prior_trial_flag, auto_renew_status |
| DNA & product signals | 8 | dna_kit_registered, tree_size, hints_viewed_30d, records_viewed_30d |
| Campaign metadata | 9 | campaign_type, send_hour_local, channel_segment, discount_pct |
| Geography & device | 6 | country, state, device_type, email_client |
A good solution should achieve strong ranking quality for campaign targeting: ROC-AUC >= 0.80, PR-AUC >= 0.35, and precision in the top decile >= 0.30. The model should also produce interpretable feature importance for marketers.