# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Data-Driven Decision Support in SaaS  
### Main Focus  
The SaaS model is mainly offered by subscription which is the main source for revenue generation for such businesses. Having a strong user base is crucial for the viability of SaaS businesses. Therefore, it is very important for SaaS to maintain a small percentage of users that cancel their subscriptions (churn). For SaaS businesses, even a small reduction in churn can lead to significant profit growth and a healthier and long-term business. For this reason, many researchers focus on predicting churn in SaaS businesses and on identifying the best performing machine learning algorithms for each case. Figure 2 summarizes the results regarding RQ1. As can be seen in Figure 1, most of the reviewed papers (53.57%) focused on churn prediction. This comes as no surprise since high churn rates cannot sustain a SaaS business model. The churn rate is the number of users that leave the SaaS divided by the total number of users. Churn prediction refers to predicting if a user is about to churn. The authors in [12,13] focused on applying and testing  
Figure 1: PRISMA flow diagram. various algorithms fed with usage data to evaluate their performance in churn prediction, in the context of SaaS, while research presented in [8; 14; 15; 16; 17] followed the same principle but shifted their focus towards the Business to Business (B2B) SaaS business model. All those studies aimed to point out key features that churning customers possess and offer SaaS providers actionable insights for improving their customer retention strategies. Further research on this topic included in [18; 19] focused on comparing multiple machine learning algorithms to figure out which one was best performing for churn prediction in the SaaS cloud service model. The authors in [18] developed a churn prediction system to predict customer attrition for cloud-based service providers (CSPs), attempting an early identification of customers at risk of canceling subscriptions. Churn prediction in Customer Relationship Management systems (CRM) using hybrid machine learning models was addressed in [19]. Apart from the comparison between machine learning methods, the authors in [20] chose to use machine learning to analyze and monitor customer satisfaction from support tickets and predict whether a client is going to renew their cloud service subscription. Accordingly, the research presented in [21] experimented with the creation and application of a hybrid machine learning framework designed for real-time prediction of user events in SaaS products such as subscription cancellation, user interactions, and task abandonment during user sessions. Several research efforts dealt with predicting churn and improving retention specifically for online games based on the SaaS model. The strategy employed in [22] aimed to identify churners in earlier stages of a mobile freemium game, even after the tutorial phase, using minimal early-stage user data, featuring a Transformer-based architecture (FT-Transformer) tailored for tabular data. The research presented in [23] focused on predicting player churn and disengagement within a 14-day window in a freemium online strategy game using machine learning. Specifically, it aimed to identify the most effective machine learning techniques and labeling approaches for early detection of players at risk of leaving the game, enabling proactive retention strategies. In [23] the authors distinguished between churn (permanent departure) and disengagement (reduced activity), providing a comprehensive comparison of methods tailored to the gaming industry. The authors of [24] extended their research to a larger time span, focusing on improving churn prediction in casual freemium games by combining sequential (temporal) and aggregated (static) data using different neural network architectures.