monetization

This study investigates the interplay between monetization strategies and player retention in South Korea’s mature mobile gaming market, alongside associated ethical risks. Method: Through hands-on gameplay, structured video analysis, and qualitative coding of 40 top-grossing titles, the research systematically identifies prevalent design patterns. Contribution/Results: It establishes the first monetization strategy taxonomy tailored to mature markets, revealing three dominant, high-prevalence patterns—time-limited progression, conflict-driven mechanics, and social dynamics engineering. The analysis uncovers critical regulatory gaps, including insufficient transparency, inadequate probability disclosure (e.g., gacha odds), and exploitative use of competitive pressure. Based on these findings, the study proposes actionable policy recommendations: cross-jurisdictional regulatory coordination and targeted safeguards for vulnerable players. The framework advances theoretical understanding of ethical game governance and informs sustainable, socially responsible commercial practices in interactive entertainment.

This paper addresses the challenge of accurately identifying demand under substantial temporal fluctuations and absent cost-variation information, focusing on the French railway industry. We systematically evaluate the economic performance of revenue management (RM) strategies using a novel identification framework that integrates time-series relative price changes, consumer rational expectations, and firms’ weak optimality conditions in pricing. Our methodology combines structural econometric modeling, counterfactual demand estimation, endogenous price treatment, and censoring-handling techniques to overcome identification issues arising from sales cutoffs and the lack of exogenous price variation. Results show that current RM practices significantly outperform uniform pricing but still incur a 16.7% revenue loss relative to theoretically optimal dynamic pricing. This study provides the first empirical quantification of RM’s net economic value in a real-world industrial setting and reveals its critical role in aggregating and processing information under demand uncertainty.

This paper investigates cooperative pricing between a platform and independent sellers in a revenue-sharing Bertrand game. Addressing the limitation of conventional models that neglect distributional collaboration, we develop an extended model wherein the revenue-sharing ratio is endogenously determined and sellers possess outside options. Using game-theoretic analysis and Nash equilibrium characterization, we establish— for the first time—that under specific cost structures and revenue-sharing parameters, introducing independent sellers can simultaneously increase both the incumbent manufacturer’s profit and consumer surplus, thereby overturning the conventional efficiency–profit trade-off. We further derive necessary and sufficient conditions for the existence and uniqueness of multiple equilibrium types, and demonstrate that the revenue-sharing mechanism exerts a non-monotonic effect on market efficiency, firm profits, and social welfare. Our findings provide theoretical foundations and actionable insights for cooperative pricing and revenue-allocation mechanism design in platform economies.

This paper studies profit-optimal mechanism design for exclusive, non-rival digital content (e.g., paid subscriptions) exhibiting bidirectional positive and negative network effects. Buyers possess private, heterogeneous values that depend on adoption scale. We propose the first explicit dominant-strategy mechanism tailored to bidirectional network externalities, extending the Bayesian optimal auction framework to settings with network effects. By generalizing virtual valuation and leveraging monotonicity analysis, we derive closed-form allocation and pricing rules. Theoretically, our mechanism rationalizes real-world practices—including voluntary donations, community subsidies, and exclusive bidding—by uncovering their underlying incentive structures. Empirically, it precisely replicates core monetization features of platforms such as Patreon and Substack. Under individual rationality and incentive compatibility constraints, the mechanism significantly increases the platform’s expected revenue.

This paper identifies a fundamental bias in estimating average treatment effects (ATE) in continuous-parameter A/B tests—particularly price experiments—arising from interference among market participants. In pricing contexts, conventional estimators of profit change expectations can exhibit sign reversal, leading firms to adopt profit-damaging pricing policies. To address this, we propose a lightweight debiasing method requiring only equal partitioning of experimental units. We are the first to systematically characterize the “sign reversal” phenomenon and prove its ubiquity in two-sided markets and multi-category commission pricing. Through structural modeling and differential analysis, we derive an explicit closed-form expression for the bias and theoretically demonstrate that the classical estimator can indeed flip sign. Empirical evaluations across diverse market settings confirm that our method consistently restores correct decision directionality, thereby ensuring reliable causal inference.

This paper addresses the multi-objective optimization of revenue, profit, and customer retention in dynamic subscription pricing. Methodologically, it proposes a real-time pricing decision framework incorporating business-critical hard constraints. The framework integrates clustered price elasticity modeling, churn propensity prediction, and seasonality-aware tree-ensemble demand forecasting, coupled with Monte Carlo risk simulation and constrained multi-objective optimization; modular APIs enable real-time policy recalibration. Its key contribution lies in the first explicit incorporation of hard constraints—including customer experience thresholds, minimum gross margin requirements, and acceptable churn rates—into the pricing optimization model, yielding interpretable, adjustable, and ethically grounded pricing policies. Validated across diverse SaaS scenarios, the framework significantly outperforms static and uniform pricing strategies: it increases prices for high-willingness-to-pay users while shielding price-sensitive segments, thereby achieving a balanced trade-off between revenue growth and user trust.

Existing retail simulators struggle to model the cross-stage influence of sellers’ early decisions on final purchases through multi-stage interactions. This work proposes RetailSim—the first end-to-end retail simulation framework—that integrates a diverse product space, persona-driven large language model agents, and multi-round buyer-seller interactions to holistically model the entire purchase journey from persuasion to transaction. RetailSim enables high-fidelity simulation of cross-stage dependencies for the first time, supporting both sales strategy evaluation and buyer persona inference. Its validity is established through dual verification: human behavioral fidelity assessment and meta-evaluation against established economic principles. Experiments successfully reproduce key empirical regularities—including demographic purchasing disparities, price-demand relationships, and heterogeneous price elasticities—demonstrating the framework’s practical utility for strategy testing.

This paper addresses the dynamic pricing problem in appointment-based services featuring multi-tiered, multi-temporal, and multi-window substitutable options (e.g., varying time slots, prices, and capacity levels). We propose a unified framework integrating hierarchical discrete choice modeling with dynamic pricing. Our approach innovatively employs decision trees to drive interpretable market segmentation and segment-specific parametric choice models—explicitly incorporating reference price effects. We further design an efficient heuristic algorithm for scalable pricing optimization. An A/B test conducted on an Amazon business line demonstrated a 19% improvement in core metrics; the solution was fully deployed starting Q4 2023, enabling rapid iteration of new services. To our knowledge, this is the first work to jointly integrate interpretable segmentation, behavior-aware choice modeling, and scalable pricing optimization—significantly enhancing demand forecasting accuracy and revenue performance in complex appointment settings.

This study addresses the challenges of extreme demand volatility, delayed pricing responses, and misalignment between short-term revenue and long-term profitability during major fashion e-commerce promotions. To tackle these issues, the authors propose a high-frequency “predict–optimize” automated pricing system that breaks away from traditional weekly decision cycles by operating at a minute-level granularity. The system achieves the first industrial-scale deployment of daily multi-objective dynamic pricing in large-scale e-commerce settings, combining gradient-boosted tree models for daily demand forecasting with a multi-objective optimization framework to generate real-time pricing strategies that jointly maximize long-term profit and net merchandise value. Evaluated across 23 A/B tests in 12 Zalando markets from 2023 to 2024, the system delivered approximately 6% higher profit while maintaining sales volume and has since been fully deployed for promotional pricing.

This paper addresses revenue allocation imbalance in subscription-based platforms caused by collusive fraud between users and creators. We propose a mechanism-design-driven, manipulation-resistant solution. First, we formalize three manipulation-resistance axioms, exposing fundamental incentive-incompatibility flaws in prevalent proportional allocation rules. Building on this analysis, we design ScaledUserProp—a novel allocation mechanism that eliminates fraudulent incentives at their source. Our approach integrates mechanism design theory with computational complexity analysis and conducts empirical evaluation on both real-world and synthetic streaming datasets. Results demonstrate that ScaledUserProp achieves superior manipulation resistance while preserving fairness, outperforming existing allocation rules across all key metrics. Crucially, it requires no machine learning–based fraud detection models, ensuring full interpretability and practical deployability.