Abstract This study addresses customer churn prediction in contractual utility services by applying survival analysis models, which provide time-to-event insights beyond traditional machine learning approaches. Churn is defined as subscription cancellation within a given period. Unlike classification models that only indicate whether churn will occur, survival models estimate hazard rates, capturing how churn risk evolves over time. Our objectives are twofold: (1) to compare two survival models—the Cox Proportional Hazards (CPH) model and the Aalen Additive (AA) model—in identifying key drivers of churn, and (2) to demonstrate their interpretability in predicting churn timing for more effective customer intervention strategies. Experiments with data from a gas utility company show that survival models can successfully predict customer churn across products and contract types. By estimating individual-level risk profiles, these models highlight customers most likely to leave, enabling segmentation based on churn likelihood and timing. This provides actionable insights for designing targeted retention efforts. Overall, the study demonstrates the added value of survival analysis in churn prediction: it not only forecasts whether customers are at risk but also when churn is likely, supporting timely, tailored strategies that reduce attrition and strengthen customer retention.

Abstract A social media firestorm (SMF) refers to a sudden surge of negative reactions, criticism, or controversy on social media platforms, typically triggered by a specific event, statement, or action. Such firestorms can affect individuals, organizations, or brands, with potential reputational and financial consequences if not addressed appropriately. This paper elaborates on an SMF scale inspired by the Saffir-Simpson hurricane scale, adopting a structured approach to SMF measurement and management. The scale defines three measurable dimensions: width (reach or scope), height (intensity of negative sentiment), and duration of peak activity (the shark-fin shape). To provide preliminary validation, an artificial intelligence-based approach was applied to selected real-world firestorm cases. The findings suggest that the framework represents a first step toward a fully validated scale, offering an initial basis for assessing the potential impact of SMFs and supporting more structured organizational responses to digital crises.

Abstract This study introduces a predictive framework for estimating television episode viewership using machine learning and natural language processing applied to over 25,000 TV scripts. By analyzing linguistic and emotional features embedded in dialogue, the research identifies content patterns linked to audience viewership. Multiple regression models, including OLS, Lasso, Ridge, Elastic Net, Gradient Boosting, and XGBoost, are trained to forecast next-episode viewership, explaining up to 50% of variance at the genre level and 41% at the series level. These findings suggest that early-stage script analysis can offer actionable insights for media development and marketing teams. Rather than viewing scripts solely as creative artifacts, this research highlights their potential as data assets for content strategy, allowing for more informed decisions in greenlighting, promotion, and brand alignment.