Business Context

NovaForge Studios operates a live-service mobile RPG with 8M monthly active players. The growth team wants a model that predicts whether a new player will still be active on day 7 so they can trigger CRM campaigns, in-game offers, and onboarding interventions.

Dataset

The training data is built at the player-install level using behavior from the first 48 hours after install. The label is whether the player is retained on day 7.

• Size: 1.2M player installs over 6 months, 50 features
• Target: Binary — retained on day 7 (1) vs not retained (0)
• Class balance: 28% retained, 72% churned before day 7
• Missing data: ~12% missing in acquisition metadata, ~6% missing in device specs, sparse missingness in social features for players who never unlocked them

Success Criteria

A good solution should achieve strong ranking quality for intervention targeting, with ROC-AUC >= 0.82, PR-AUC >= 0.58, and top-decile lift >= 2.2 on a strictly future holdout set.

Constraints

• Predictions must be generated in a daily batch for up to 500K new installs
• Marketing and game design need feature-level explanations for why players are flagged
• The model should be robust to changing acquisition mix and live-ops events
• Retraining budget supports weekly retraining, not continuous online learning

Deliverables

1. Define the target and leakage-safe feature window
2. Build and justify a retention prediction model
3. Describe preprocessing and feature engineering choices
4. Evaluate the model with business-relevant metrics and thresholding
5. Explain how you would deploy, monitor, and retrain the model