# Data-Driven Decision Support in SaaS Cloud-Based Service Models
###### Abstract
This makes it harder to gather and analyze all of their data in one place ([[https://www.fortunebusinessinsights.com/software-as-a-service-saas-market-10](https://www.fortunebusinessinsights.com/software-as-a-service-saas-market-10)]([https://www.fortunebusinessinsights.com/software-as-a-service-saas-market-10](https://www.fortunebusinessinsights.com/software-as-a-service-saas-market-10)) 222, assessed on 1 June 2025). The growing use of AI in SaaS platforms introduces new benefits, like automation and personalization. However, this also leads to higher infrastructure costs due to expensive computing resources like GPUs, high-volume data usage, and the need for highly skilled professionals to build and maintain AI systems ([[https://www.paddle.com/blog/saas-market-report-q1-2025](https://www.paddle.com/blog/saas-market-report-q1-2025)]([https://www.paddle.com/blog/saas-market-report-q1-2025](https://www.paddle.com/blog/saas-market-report-q1-2025)), assessed on 1 June 2025). These trends in the SaaS industry indicate that SaaS providers face increasingly complex decisions. As a result, the role of data-driven decision support systems becomes critical in helping SaaS vendors make informed, timely, and cost-effective choices. At the core of modern SaaS ecosystems, vast amounts of data are generated, regarding user behavior, subscriptions and system performance [7]. SaaS data can be leveraged for deriving insights and identifying patterns to support decision-making for SaaS providers. For example, machine learning (ML) algorithms can be employed to create churn prediction models for user churn prediction [8]. Given that information, SaaS providers can act with a specific incentive to reengage those users. To this end, the aim of this survey is to review research in data-driven decision support in SaaS applications. In particular, the goal of this work is to identify research that utilizes data to support SaaS vendors towards reducing churn and increasing user engagement, satisfaction and loyalty. Although related works are already present in the literature [9; 10; 11], this study stands out by offering a comprehensive perspective on leveraging SaaS data to support decision-making for SaaS vendors to sustain user loyalty and satisfaction. Unlike prior research that often focuses narrowly on specific tasks such as churn prediction, this work provides a holistic examination of strategies to maintain a stable user base. Specifically, the diverse objectives of existing approaches are analyzed, as the variety of employed data sources, and the comparative effectiveness of different machine learning methods on multiple occasions. Moreover, the ways the outputs of these approaches, ranging from visualizationsto actionable insights, are presented to SaaS vendors to facilitate spot-on decision-making, are also discussed. The rest of the paper is structured as follows: Section 2 presents related works and highlights the contributions of this paper. Section 3 summarizes the research methodology, while Section 4 presents the results. Discussions and conclusions are provided in Sections 5 and 6, respectively.