# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 2 Related Work  
The current work constitutes a scoping review of research to assist SaaS vendors in decision-making based on the data that SaaS businesses generate. Therefore, the aim is to identify research efforts that perform literature reviews on the same subject. To the best of our knowledge, there is no exact type of review found in the literature. However, there are literature reviews that are related to the subject, yet with different focus. For example, the authors in [9] performed a review on machine learning methods for churn prediction covering the years between 2015 and 2023. Although their work provided insights on the use of ML for churn prediction highlighting the trade-offs of different ML methods, the focus was only on churn prediction, and it did not address other use cases such as user segmentation or customer lifetime value prediction which are critical to support business owners on decision-making. Moreover, the review focused on telecommunications, finance and online gaming industries, regardless of the type of businesses and the adoption of cloud-based models and SaaS specifically. To this end, our work constitutes a review tailored to support businesses that operate based on the SaaS cloud model providing a more holistic approach by covering a wide range of use cases, important to retain a strong customer base. Similarly, the authors in [10] conducted a general review of decision analytics approaches for cloud computing. It's also worth noting that their work was published more than 10 years ago, in 2014, and therefore it does not cover current research efforts. The authors focused on decision support for cloud computing for problems like service selection and pricing, rather than discussing decision support for user retention and engagement. Moreover, there was no description of the systematic review research methodology and no information or statistics regarding the number of papers included in their review. Moreover, the authors considered papers with methods that were not data-driven, like heuristics or ontology-based. The review presented in [11] addressed a broad range of different topics AI, ML, Business Intelligence (BI) and SaaS at a high level without going into detail. Regarding user behavior, authors discussed prediction strategies and personalization of user experience, however, without going into specific insights and recommendations. A systematic review methodology was also missing. Another paper [5] published in the same year (2014) covered the technical challenges and perspectives for SaaS such as multi-tenancy and scalability. That review also lacked a research methodology and did not mention how the reviewed approaches were selected. Performance comparisons were not included, and only a qualitative analysis was performed. Lastly, there was no focus on user interaction subjects like user retention and churn. Finally, the research presented in [11] addressed AI adoption for BI in SaaS. Although it was mentioned that a literature review was conducted, only 8 papers were included in their analysis. To this end, compared to the existing bibliography, the current work:  
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Discusses of challenges the SaaS vendors face regarding maintaining a core user base like churn, user engagement and user retention. Moreover, the contributions of this work can be summarized in the following distinct points:  
* A comprehensive review of research initiatives aiming to support decision-making for SaaS providers, expanding beyond single-task approaches to address broader strategic goals. * An overview of various data inputs utilized across studies, identifying frequently used data sources and highlighting cases where multiple kinds of data were integrated for more robust insights. * A comparative evaluation of employed machine learning techniques, analyzing their relative strengths, weaknesses, and performance in different SaaS decision-making contexts.