# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 5 Discussion
Furthermore, user segmentation and persona modeling enabled more detailed targeting of user engagement and marketing strategies, while process mining techniques uncovered critical activation patterns such as the discovery of an \"aha moment\". Some studies also focused on model deployment strategies, emphasizing cloud integration and scalability. By bridging predictive analytics with tangible business insights and strategic tools, DSS outputs were shown to have a direct impact on operational efficiency, customer satisfaction, and long-term profitability. Moreover, deploying such models as churn prediction ones through the cloud offers the possibility of providing Machine Learning as Service (MLaaS) models specialized for supporting decision-making in SaaS. On the same page, research [54] presented an MLaaS solution for marketing that contained a churn prediction model tested in retail and e-commerce settings. To synthesize the findings discussed in this review and provide a clearer understanding of the research landscape, Table 6 presents the main insights and findings corresponding to each research question (RQ1-RQ4) addressed in this study. Table 6 offers a compact overview of the focus areas, types of data used, applied machine learning methods, and the nature of decision support outputs in SaaS environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Focus** & **Data** & **Proposed Method** & **Output** \\ \hline \multirow{4}{*}{[18]} & \multirow{4}{*}{Churn prediction} & Usage behavior, customer profile, financial & \multirow{4}{*}{Random Forest} & Simulation/What-If \\  & & & & Analysis, Business Impact \\  & & & profile, financial & & Metrics, Model \\  & & & & Deployment and \\  & & & & Integration \\ \hline
[24] & Churn prediction & Usage behavior & Hybrid Model (LSTM Hidden State) & Model Deployment \\  & & & (LSTM Hidden State) & and Integration \\ \hline
[12] & Churn prediction &
\begin{tabular}{c} Usage behavior, customer profile, \\ transactional/business metrics \\ \end{tabular} & XGBoost & Model Deployment \\  & & & & and Integration \\ \hline
[14] & Churn prediction & \
\begin{tabular}{c} Usage behavior, \\ transactional/business metrics \\ \end{tabular} & Random Forest &
\begin{tabular}{c} Visualizations, Business \\ Impact Metrics \\ \end{tabular} \\ \hline
[22] & Churn prediction & Usage behavior & XGBoost & Visualizations \\ \hline
[19] & Churn prediction & \begin{tabular}{c} Usage behavior, \\ customer profile \\ \end{tabular} & \
\begin{tabular}{c} Hybrid Model (SVM \\ + Naive Bayes) \\ \end{tabular} &
\begin{tabular}{c} Business Impact Metrics, \\ Model Deployment \\ and Integration \\ \end{tabular} \\ \hline
[25] & Churn prediction & \
\begin{tabular}{c} Usage behavior, \\ customer profile \\ \end{tabular} & Cox Regression &
\begin{tabular}{c} Visualizations, \\ Simulation/What-If \\ Analysis, Segmentation \\ and Persona Modeling, \\ Business Impact Metrics \\ \end{tabular} \\ \hline
[13] & Churn prediction &
\begin{tabular}{c} Usage behavior, \\ customer profile, \\ transactional/business metrics \\ \end{tabular} & Decision Trees & Visualizations \\ \hline
[20] & Churn prediction &
\begin{tabular}{c} Customer support \\ \end{tabular} & Neural Networks & Visualizations \\ \hline
[15] & Churn prediction & \
\begin{tabular}{c} Usage behavior, customer \\ profile, customer support \\ \end{tabular} & XGBoost &
\begin{tabular}{c} Model Deployment \\ and Integration \\ \end{tabular} \\ \hline
[8] & Churn prediction &
\begin{tabular}{c} Usage behavior, \\ transactional/business metrics, customer profile \\ \end{tabular} & Neural Networks & Business Impact Metrics \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cumulative table, findings of RQ1–RQ4.