# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Data-Driven Decision Support in SaaS
### Data Sources  
In order to compare and evaluate algorithms for predicting churn in SaaS businesses, researchers chose to utilize various types of data sources for their experiments. As per the research question RQ2, the most common category among studies [8; 12; 13; 14; 15; 16; 17; 18; 19] is usage behavior data. All those studies incorporate some form of user interaction or system activity logs like logins, sessions, file uploads, application usage, number of users and actions performed. Another common type of data used in [8; 12; 13; 14; 16; 17] is transactional and business metrics including monetary values (monthly charges, total expenses, amount spent), subscription status and billing information. Additionally, temporal data, contractual data and customer lifecycle indicators appeared in [12; 13; 15; 17; 18; 19] concerning customer tenure, days to expiry, subscription age and monthly snapshots or observation windows while in studies [8; 16; 17; 18; 19] customer demographics and attributes were also utilized (company size, region, industry, onboarding status or business age). The authors in [18] also used billing and loyalty program data like loyalty program status and billing cycles. At last, customer support interaction data were used in [15] (support tickets, resolution times) and [16] (support case logs) while customer satisfaction metrics such as Net Promoter Score (NPS) and call quality were specifically included in [17]. Furthermore, regarding churn prediction, the approach followed in [20] used support and subscription metadata while the study [21] focused on clickstream data combined with dynamic user profiles (subscription status, tenure, demographics). Studies that focused on predicting churn in games featuring a subscription model as in [22; 23; 24], shared a strong emphasis on event logs, session data, and gameplay behavior and chose to utilize behavioral data (gameplay actions, tutorial engagement, session metrics, clicks, purchases, logins) to design their churn predicting models. Focusing on improving retention of freemium online games, the study [25] conducted its research by utilizing player behavioral data (engagement, achievements, collaboration, progress, dropouts), demographic and contextual data (gender of game characters, geographic location, time of play), and some game-specific metrics. Moreover, the data used in [26] was derived from a simulated dataset containing synthetic data (simulated quality levels, utility functions, and cost structures). Data in [27] are derived from surveys, interviews and web behavioral data (time spent per page, clicks per feature, aggregated over time) and are used in customer segmentation algorithms. On the same page, work [28] used demographic features (age, gender, state), behavioral features (engagement level, time spent, number of screens viewed, clicks, sessions) and transactional features (maximum budget, maximum return on investment, number of purchases) for customer segmentation. For the purpose of discovering drivers of user loyalty and improving user satisfaction and engagement, studies [29; 30; 31; 32; 34; 37] leveraged mainly behavioral and usage data (login frequency, feature usage, interaction patterns to understand user engagement, product adoption over time). However, in [35; 37] interview and survey data were used, providing context on user perceptions, satisfaction, and demographic characteristics. In studies [34; 35], system logs and event data were used and specifically in [35] logs are used alongside direct feedback like surveys and support tickets. Marketing, free trial, and transactional data (ad impressions, message types, marketing variables and trial duration variations) are utilized by researchers in [30; 33] while in [36] simulated data were employed (transaction volumes, volatility, user-centric metrics, user profiles, negotiation dynamics) to explore hypothetical user behaviors and negotiation dynamics in scalable, controlled environments.