# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 5 Discussion  
In response to RQ1, the analysis revealed that churn prediction is the most frequently pursued objective in decision support systems in SaaS settings. Studies consistently emphasized the importance of identifying early at-risk users in order to interfere with data-driven strategies, therefore improving subscription continuance and reducing revenue loss. Beyond churn, researchers also contributed significantly through user segmentation, which enables the grouping of customers into meaningful behavioral or demographic clusters. This supports personalized product development, targeted marketing, and more responsive customer service. Other studies focused on improving user engagement and predicting CLV, which are key indicators for long-term business sustainability. Additionally, strategic decisions around pricing optimization, free trial policies, and onboarding practices were offered so that SaaS providers are aided in aligning service design with actual user behavior and expectations. Regarding RQ2, this survey revealed that for the effectiveness of decision-making in SaaS environments, diverse and rich datasets must be used. In most studies, behavioral data, such as clickstream logs, feature usage, and session frequency, emerged as foundational. These were often combined with transactional and subscription-related information, including billing history and account tenure, to provide a fuller picture of user engagement. Studies also made use of demographic and firmographic characteristics, allowing for segmentation by industry, company size, or geographic location. In some cases, sentiment data from support tickets or survey responses were incorporated to capture users' subjective experiences. A few studies employed synthetic or simulated data to test hypothetical scenarios and optimize intervention strategies before deployment. Altogether, the results underscore the importance of integrating both quantitative system usage metrics and qualitative feedback for comprehensive decision support. Addressing RQ3, it was found that machine learning techniques play a central role in nearly all the examined implementations. RF and XGBoost were the most commonly used algorithms, and they consistently delivered strong performance across various evaluation metrics such as AUC, precision, recall, and F1-score. These models were particularly effective in handling high-dimensional, tabular datasets and offered a balance of accuracy and interpretability. In more complex scenarios requiring temporal modeling or behavioral pattern recognition, hybrid and ensemble models outperformed traditional classifiers. For example, combinations of LSTM networks with LightGBM or structured metadata inputs captured sequential user behavior more effectively than static models alone. While simpler models like LR and DT provided useful baselines, their performance was often eclipsed by ensemble or deep learning approaches. Notably, some studies considered the environmental impact and scalability of different algorithms, highlighting the relevance of computational efficiency and carbon footprint in model selection. Overall, the choice of method was shown to depend on specific task requirements, data characteristics, and operational constraints such as latency or model transparency. Finally, in response to RQ4, a wide variety of output types were presented to inform and guide SaaS providers. The importance of visual analytics was highlighted, including ROC curves, feature importance rankings, and user behavior maps, which helped non-technical stakeholders interpret model outcomes. Business-oriented metrics such as CLV, MRR, and ROI linked predictions to financial outcomes while simulation tools allowed providers to experiment with trial durations or pricing adjustments before actual implementation, enhancing the strategic value of the DSSs.