# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 5 Discussion
### Insights and Strategic Recommendations  
From many of the reviewed systems derived various actionable predictions and recommendations, such as identifying customers at high risk of churning, estimating potential revenue loss, or advising on optimal pricing strategies. One of the most consistent themes across research was the use of machine learning to flag customers likely to churn. Studies such as [14] concluded that newer B2B customers are particularly vulnerable to churn, while those with low monthly payments often exhibit higher loyalty, suggesting that random forests are well-suited for B2B SaaS due to their interpretability and scalability. Pricing strategies also play a pivotal role in user retention. For instance, research [36] concluded that a significant percentage of users prefer transparent billing and proposed a dynamic pricing formula that factors in base rates, usage metrics, and risk adjustments. The study recommended gradient boosting for complex pricing environments where traditional models may fall short. Additionally, research [32] revealed that multi-product subscriptions improved retention but cautioned against over-reliance on bundling, noting regional variations in loyalty. Further supporting pricing adjustments, research [8] identified that high fees combined with low session activity strongly correlate with churn, suggesting that SaaS providers should consider flexible pricing for low-engagement users. Additionally, other researchers proposed best-practice policies, like trial durations, onboarding flows, and pricing tactics, to help SaaS providers align product decisions with user behavior and retention trends. Study [30] recommended defaulting to 7-day trials unless segmentation suggests longer ones, while authors of [31] highlighted that intuitive UI design can boost satisfaction and increase loyalty by 41%. Additionally, work [26] advised gradual quality adjustments to better match user perceptions, and work [33] set performance benchmarks such as 90% acceptability rates in negotiation systems. Onboarding strategies, according to [34], should center around key activation points, while the work presented in [37] encouraged CRM-style engagement, including intranet banners, to maintain user involvement. In [22] the authors demonstrated that early onboarding metrics are strong predictors of long-term retention. The study achieved an 88.75% recall rate in predicting post-tutorial churn, underscoring the importance of monitoring early user behavior. Similarly, the work presented in [20] linked negative sentiment in support tickets to churn, advocating for sentiment analysis alongside usage logs to create a risk assessment framework. In another study [19], the authors found that low API or integration usage and infrequent support interactions are reliable churn signals, making these metrics essential for prioritizing retention efforts. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Focus** & **Data** & **Proposed Method** & **Output** \\ \hline \multirow{3}{*}{[35]} & User & Usage behavior, customer & \multirow{3}{*}{Comparative} & Visualizations, Model \\  & satisfaction/user & support, satisfaction, & & analysis & Deployment and \\  & loyalty & survey/interview & & Integration \\ \hline \multirow{3}{*}{[32]} & User & Usage behavior, & \multirow{3}{*}{Logistic Regression} & Segmentation and \\  & satisfaction/user & transactional/business & & & Persona Modeling, \\  & loyalty & metrics, marketing & & Business Impact Metrics \\ \hline \multirow{3}{*}{[27]} & User segmentation & Usage behavior, & \multirow{3}{*}{UMAP and HDBSCAN} & Segmentation and \\  & & survey/interview & & \\ \hline \multirow{3}{*}{[28]} & \multirow{3}{*}{User segmentation} & Usage behavior, & \multirow{3}{*}{XGBoost} & Segmentation and \\  & & transactional/business & & & Persona Modeling \\ \cline{1-1}  & & metrics & & & \\ \hline \hline \end{tabular}
\end{table}