Customer Lifetime Value (CLV) Estimation in Subscription-Based SaaS Business Models

Accurate estimation of Customer Lifetime Value (CLV) is critical for strategic decision making, capital allocation, and customer relationship management in subscription-based Software-as-a-Service (SaaS) business models. Traditional probabilistic models, such as the Beta-Geometric/NBD (BG/NBD) and Gamma-Gamma frameworks, often struggle to capture complex non-linear feature interactions, time-varying covariates, and dynamic churn hazards characteristic of modern cloud enterprise platforms. Conversely, standard machine learning regressions frequently fail to account for right-censored event data and dynamic contract renewals. This paper presents a novel hybrid estimation framework that integrates non-linear predictive architectures, temporal attention networks, and deep survival analysis with classical probabilistic discounting. Utilizing an empirical B2B SaaS dataset spanning 2015 to 2021 comprising 142,000 enterprise accounts, our model achieves superior performance over legacy models, reducing Root Mean Squared Error (RMSE) by 28.4% and Mean Absolute Percentage Error (MAPE) by 19.2% in long-term CLV projections. We demonstrate how combining high-dimensional behavioral usage telemetry with contract expansion vectors enhances early churn detection and revenue yield forecasting, leveraging advanced big data paradigms to drive enterprise business intelligence. Furthermore, we establish the theoretical and empirical underpinnings of dynamic cohort valuation under varying macroeconomic discount regimes.