# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Data-Driven Decision Support in SaaS
### Main Focus
Their study investigated how integrating these two types of data can enhance prediction accuracy compared to models that rely solely on sequential or aggregated data while also addressing the challenge of predicting  
Figure 2: Main focus of the reviewed papers (RQ1). churn in non-contractual contexts (for example in mobile games), where players could leave without explicit signals. Additionally, the research conducted in [25] focused on enhancing retention analysis in freemium role-playing games (RPGs) by modeling players' motivation, progression, and churn. Particularly, it aimed to understand how in-game behaviors (engagement, collaboration, and achievement) at different levels influence dropout rates, and how these interdependencies could be leveraged to predict player retention more accurately. As player retention in the video game industry is the key to its ongoing prosperity, work in [26] explored how video game operators can retain players in subscription-based games by dynamically adjusting the quality of the game over time. The study developed a dynamic programming model that considered players' memory of past service experiences and network externalities (the phenomenon where the value or utility of a product or service increases as more people use it) to determine the optimal data-driven decisions to maximize long-term profits. In order to better understand and adapt to clients' needs in SaaS services it is important to categorize them into groups to be able to provide them personalized attention. An iterative mixed-method approach for creating user personas in the context of B2B SaaS products was presented in [27]. That work also aimed to demonstrate how the generated user personas could be practically applied as an indicator to improve the design, development, and prioritization of features in a B2B SaaS product. Another research on customer segmentation was presented in [28], particularly exploring how behavioral customer segmentation and app usage analysis can be leveraged to predict customer interest in premium subscriptions and identify key influencing factors to improve subscription conversion rates for mobile apps. User conversion from free trials to paid subscriptions was the main focus of [29], examining how marketing interactions (ads, messages, emails, etc.) and their type of content along with free-trial usage behaviors could influence users' decisions. Further insights regarding free trials were showcased in [30]. The study highlighted the importance of free trials as a customer acquisition strategy in the SaaS industry and analyzed how customer acquisition, retention and profitability were affected by the duration of free trials to discover their optimal design. Establishing a long-term relationship with customers requires keeping them engaged and satisfied with the services they are getting. The authors in [31] examined how visual and functional aspects of user interface (UI) design could influence user experience (UX). Advancing further on the subject of customer loyalty, the authors in [32] followed a data-driven approach to discovering the determinants of customer loyalty in the B2B SaaS industry. That study explored how transactional and behavioral data (like platform engagement, and communication frequency) could be leveraged to predict customer loyalty without relying on traditional surveys. The research presented in [33] proposed the design of an adaptive negotiation mechanism to allow providers to interact with clients and dynamically manage service quality, user satisfaction, and resource costs in cloud environments. A very impactful factor in keeping customers from churning is whether these individuals discover the \"aha moment\" and acknowledge the return on their investment in SaaS services. The \"aha moment\" refers to the point when users understand the value of the software product, which is crucial for enabling customer activation and retention. The authors of [34] focused on identifying the \"aha moment\" of B2B SaaS customers using process mining techniques.