Your question is Predict Live-Service Game Retention. Take a moment with it on the right.
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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.
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.
| Feature Group | Count | Examples |
|---|---|---|
| Session behavior | 14 | session_count_48h, total_minutes_48h, avg_session_length, last_session_gap_hours |
| Progression | 10 | levels_completed, tutorial_completion_pct, deaths_per_level, quest_completion_rate |
| Economy & monetization | 8 | soft_currency_earned, hard_currency_spent, first_purchase_flag, ad_views |
| Social & engagement | 6 | guild_joined, friends_added, chat_messages, push_opt_in |
| Device & acquisition | 7 | platform, country_tier, install_source, device_ram_gb |
| Temporal features | 5 | install_hour_local, weekend_install, day1_day2_play_delta, recency_hours |
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.