# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 5 Discussion
### Selection of Optimal Machine Learning Model for Decision-Making
SaaS platforms featuring dynamic pricing models may also benefit from reinforcement learning to adjust prices in response to user behavior and network effects over time. Finally, for the choice of the appropriate machine learning model, deployment and sustainability constraints should be considered. For SaaS vendors with limited engineering capacity or requiring real-time predictions integrated into CRM systems, models like Random Forest, XGBoost, or LightGBM can be deployed via REST APIs or embedded in cloud ML pipelines (as in AWS SageMaker). Additionally, in energy-sensitive environments, vendors should prefer computationally lightweight models and monitor carbon emissions, as studies showed large environmental variances between architectures. Although the primary focus of the reviewed papers was on the performance and utility of data-driven decision support systems in SaaS, interpretability was also a key consideration in several of the examined studies. For instance, paper [15] employed Logistic Regression, a model valued for its transparency, and visualized its coefficients to help stakeholders understand how different usage factors affect churn predictions. Likewise, the paper [16] emphasized explainability by analyzing feature importance in Random Forest and Logistic Regression models to reveal key churn drivers, even when predictive performance was modest. Other studies such as [12; 13; 22] also used interpretable models (such as Decision Trees) or feature importance plots to support decision-making with understandable insights. Figure 4 illustrates the recommended actions for selecting the optimal ML model based on the specific use case and context as derived from the results of the reviewed papers. However, explainability techniques such as SHAP (SHapley Additive exPlanations) [55] or LIME (Local Interpretable Model-agnostic Explanations) [56] were not employed by the reviewed papers. SHAP is a method used to explain the output of any machine learning model by quantifying the contribution of each input feature to a specific prediction. It utilizes the concept of Shapley values from game theory to determine the importance of each feature. LIME is an interpretation technique used to explain the predictions of any black box machine learning model. LIME approximates a complex model's prediction with a simpler, interpretable model, focusing on individual predictions rather than the model as a whole. The choice between SHAP and LIME depends on the specific needs of the task. SHAP is ideal when accuracy, consistency, and fairness are priorities, especially in regulated settings or with tree-based models like XGBoost. LIME, on the other hand, is useful for quick, model-agnostic explanations during development, and works well with various data types. Its simplicity makes it great for explaining results to non-technical users. Ultimately, the adoption of such techniques could significantly improve stakeholder confidence in automated decisions and support error diagnosis, bias detection, and regulatory compliance. Moreover, explicit considerations of algorithmic fairness, privacy protection, or ethical implications seem to be largely absent. Future research should address those gaps by incorporating such explainability techniques into machine learning models and consider the incorporation of privacy-preserving techniques such as differential privacy [57], federated learning [58] and secure multi-party computation, which can enable model training on sensitive user data without compromising user confidentiality. Additionally, incorporating algorithmic fairness assessments into the development pipeline could prevent discriminatory outcomes such as biased churn predictions. As SaaS solutions increasingly influence strategic decisions like marketing, pricing, or retention investments, it is critical to ensure that automated recommendations do not reinforce inequalities or introduce unfair treatment.