# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 6 Conclusions and Future Work
In order to ensure that developed DSSs remain effective, monitoring mechanisms should be established. Ensuring training data is up to date and machine learning models maintain good accuracy levels is crucial. Batch training of machine learning models may be a good starting point but will not be sustainable in the long term as data volume increases. To this end, periodically batch training of the models and/or real-time training incrementally updating the models could be explored in order to ensure data integrity. Therefore, MLaaS solutions [60] specifically designed for supporting decision-making in SaaS are of great importance since they can provide monitoring and updating mechanisms ready to use assuring the efficiency of DSSs. Meanwhile, the environmental impact of machine learning implementations emerged as another critical consideration. Comparative analysis revealed that complex ensemble methods and deep neural networks can incur higher computational costs and carbon emissions than simpler model alternatives, underscoring the importance of balancing predictive performance with sustainability, particularly for SaaS companies managing significant computational workloads. Future research, except for seeking greener architectures, could also emphasize clearly explaining the trade-offs between accuracy metrics and environmental footprint. Informing users of energy consumption and carbon emissions can create awareness and lead to more environmentally friendly model selection decisions like smaller architectures and the use of techniques like early stopping and transfer learning. As decision-making increasingly relies on automated systems, ensuring the explainability, fairness, and transparency of predictions, particularly in churn risk assessments, will be critical. SaaS vendors need to have a clear view of what is happening in their business at any time and act towards improving user experience and ultimately increasing KPIs that are vital for their business. Therefore, it's important that DSSs justify their recommended decisions and provide clear guidance on what actions should be taken. Towards this direction, DSSs can utilize interpretable machine learning models to justify their decision-making processes. Post hoc methods for non-interpretable models such as SHAP values [55] and LIME [56] can be used to provide explanations on models' predictions allowing vendors to understand what influences the decisions. While both SHAP and LIME offer valuable insights, they serve distinct purposes. For SaaS providers, SHAP may be preferable for strategic decision-making and bias detection, while LIME could better support customer-facing teams needing immediate, case-specific explanations. Natural language UI could also be explored to provide an easier and friendlier way to provide guidance to SaaS vendors. Large Language Models (LLMs) could be employed to understand user's intent and explain recommended actions, for example for reducing churn or increasing user engagement. Complicated tools and UIs are not the best choice for supporting SaaS professionals. They need to stay ahead of the competition and act fast and efficiently. Providing clear explanations in natural language and suggesting a set of actions to perform could adjust better to busy schedules and offer clarity. Additionally, future systems may benefit from refined personalization strategies driven by context-aware recommendations, dynamically adapting to each user's lifecycle stage and dynamic profile. In particular, the DSSs would recommend a personalized course of actions tailored to each user's holistic profile which will be dynamically updated. Ultimately, the development of intelligent, adaptive, and sustainable decision support systems will be a key differentiator for SaaS providers seeking to build resilient, customer-centric businesses in an increasingly competitive and dynamic marketplace. Conceptualization, E.M.; methodology, E.M.; formal analysis, E.M. and G.C. ; investigation, G.C. ; resources, G.C. ; data curation, E.M. and G.C.