# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Business Impact Metrics  
Metrics like CLV, MRR and ROI can be used to quantify churn effects. This enables providers to understand the financial implications of retention efforts and prioritize high-value customer segments. For instance, study [18] reported cost savings per retained customer and revenue protection through churn reduction, while the work in [14] demonstrated ROI improvements via dynamic pricing strategies. Early churn detection and associated acquisition cost reductions were highlighted in study [19] and study [25] showcased a revenue boost from collaboration-driven monetization models. Furthermore, studies such as [8; 21] focused on reducing negative MRR churn and increasing intervention acceptance through A/B testing, respectively. The prediction of CLV and the application of marginal ROI formulas were addressed in the work [38], whereas the study [30] illustrated how optimized free trial strategies could enhance both retention and revenue. In addition, study [26] linked quality investment costs to gains in network-driven retention, and study [16] evaluated predictive accuracy in relation to carbon emissions. Studies like [36] connected usage-based billing models to a marked increase in customer satisfaction, while [32] quantified retention differences across countries, providing further insights into localization strategies. Table 5 summarizes the business metrics employed by the reviewed approaches. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Segmentation Groups** & **Description** \\ \hline
[25] & Demographics (gender, geography) and engagement levels & Groups people according to demographics and engagement levels \\
[23] & Activity patterns (e.g., “economy overview usage”) & Classifies players by activity patterns, using loyalty markers to target interventions \\
[38] & Customer Lifetime Value (CLV) & Segments customers by CLV, focusing on prioritizing enterprise clients for retention \\
[27] & B2B user personas (e.g., “Data Sellers”) & Creates user personas with pain points and workflow metrics to guide product development \\
[28] & Behavioral clusters (e.g., education-focused users) & Identifies behavioral clusters, such as education-focused users, for targeted marketing \\
[32] & Relationship length and cross-selling dependency & Segments by relationship length \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Segmentation and persona modeling (RQ4). Model Deployment and Integration  
Model deployment integrates predictive models into SaaS platforms via APIs or cloud pipelines, enabling automated churn prediction and alignment with CRM features. Building on this, CRM and marketing tools use these insights to personalize campaigns based on churn risk or user behavior. Additionally, toolkits and interactive systems support scenario testing and onboarding analysis, helping teams refine strategies and make data-driven decisions. For instance, the work in [30] integrated RESTful APIs and the CBAR platform to deliver JSON-formatted real-time risk scores, enhancing both the speed and accuracy of predictions. Centralized Bank Account Register (CBAR) not only ranked subscriptions by risk and value but also enabled targeted actions based on live insights. Similarly, study [12] proposed a standalone app that supported dynamic thresholding and feedback loops, allowing continuous model refinement, and introduced a tool offering live churn predictions for real-time decision-making. This study also highlighted declining login activity as an early warning sign and recommends dynamic threshold adjustment based on marketing capacity. Several other studies proposed additional enhancements. As shown in [21] an AWS SageMaker pipeline was utilized to support both batch and real-time featurization, enabling flexible data processing, and triggered chat pop-ups for high-risk users to foster immediate engagement.