This paper presents a marketing analytics framework that operationalizes subscription pricing as a dynamic, guardrailed decision system, uniting multivariate demand forecasting, segment-level price elasticity, and churn propensity to optimize revenue, margin, and retention. The approach blends seasonal time-series models with tree-based learners, runs Monte Carlo scenario tests to map risk envelopes, and solves a constrained optimization that enforces business guardrails on customer experience, margin floors, and allowable churn. Validated across heterogeneous SaaS portfolios, the method consistently outperforms static tiers and uniform uplifts by reallocating price moves toward segments with higher willingness-to-pay while protecting price-sensitive cohorts. The system is designed for real-time recalibration via modular APIs and includes model explainability for governance and compliance. Managerially, the framework functions as a strategy playbook that clarifies when to shift from flat to dynamic pricing, how to align pricing with CLV and MRR targets, and how to embed ethical guardrails, enabling durable growth without eroding customer trust.

翻译：本文提出一种营销分析框架，将订阅定价构建为动态的护栏决策系统，通过整合多元需求预测、细分层级价格弹性与流失倾向性，实现收入、利润与留存率的协同优化。该方法融合季节性时间序列模型与树基学习器，运行蒙特卡洛情景测试以绘制风险边界，并通过求解约束优化问题来实施客户体验、利润底线及允许流失率等商业护栏。在异构SaaS产品组合中的验证表明，该方法通过将价格调整重新分配至支付意愿较高的细分群体，同时保护价格敏感客群，持续超越静态分级定价与统一提价策略。系统采用模块化API设计以支持实时重校准，并包含模型可解释性模块以满足治理与合规要求。在管理层面，该框架可作为战略指南，明确何时从固定定价转向动态定价、如何将定价与客户生命周期价值及月度经常性收入目标对齐，以及如何嵌入伦理护栏，从而在维护客户信任的前提下实现可持续增长。