This article examines the application of dynamic difficulty algorithms to optimize player retention and monetization metrics in free-to-play projects through an empirical study conducted within a gaming environment. The fact that key indicators of a project’s viability in the F2P industry, such as D1/D7/D30 retention, directly correlate with LTV and operating profit, makes the research relevant. Traditional static difficulty curves give rise to the “difficulty paradox” — boredom or frustration that accelerates churn. In contrast, DDA promises to keep the player in Csíkszentmihályi’s “flow” zone by balancing challenge and skill. This study aims to demonstrate, on causal data, the effect of algorithmically adaptive difficulty on user retention and revenue. The novelty of the work lies in a large-scale randomized controlled experiment that combines the segmentation of “at-risk” and “core-spender” cohorts, as well as an A/B-testing and RCT methodology, to evaluate DDA as a scalable product parameter rather than merely a UX enhancement. The main findings show that night-by-night decreasing difficulty for the “at-risk” subgroup increases D30 retention by 3 percentage points, yields, on average, one additional day of play and ten more rounds per user per month, and an LTV uplift of $ 0.08 per user, where IAP and 21% by advertising generate 79% of the increase. The effect is heterogeneous: the “core-spender” segment primarily exhibits a financial response, whereas “frustrated” players increase their play activity without significant growth in spending. A comparative analysis revealed that simple heuristics offer a baseline uplift, while classical ML models can ensure up to a 20% retention growth. Additionally, RL agents and hybrid fuzzy-RL solutions can retain players longer at comparable computational costs. At the same time, generative LLM-based controllers open up prospects for unifying DDA approaches. This article will be helpful to game-product analysts, personalization-system developers, and monetization managers in the video-game industry.

dynamic difficulty adjustment, player retention, free-to-play, flow, algorithmic personalization, LTV, A/B testing, machine learning

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