Product & Economic Baseline

The process begins with establishing a baseline across key metrics: ARPU, ARPPU, LTV, retention (D1/D7/D30), progression velocity, and churn points. Core gameplay loops, economy flows, friction points, and monetization triggers are analyzed. A formalized model of the current balance and player behavior is constructed.

Player Segmentation
& Behavioral Modeling

Players are segmented by playstyle, progression pace, spending behavior, and sensitivity to difficulty. ML models are trained on historical data to predict churn risk, conversion probability, payment likelihood, and expected LTV. Segmentation becomes dynamic and continuously updated in real time.

ML-Driven Dynamic Balancing

Difficulty, rewards, drop rates, timers, and offers are moved into a configuration-driven system. ML algorithms adapt balance parameters per segment and lifecycle stage, keeping players within an optimal “flow window” without sharp difficulty spikes or progression devaluation.

LiveOps Control & Experimentation

The system supports continuous A/B and multi-armed bandit experimentation. Decisions are driven by statistically significant impact on LTV rather than short-term ARPU alone. All changes are deployed through safe LiveOps mechanisms without requiring client updates.

Predictability
& Economy Protection

ML models are used not only for optimization but also for risk control: preventing inflation, exploits, pay-to-win escalation, and retention degradation. The economy remains stable and predictable even under aggressive LiveOps scenarios.

Measurable Impact

The methodology delivers 15–25% LTV growth, 10–20% uplift in payment conversion, and 5–10% churn reduction. LiveOps evolves from manual tuning to a scalable ML-driven system, enabling sustainable metric growth and long-term product scalability.