# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Data-Driven Decision Support in SaaS
### Machine Learning Methods
In detail, study [34] deployed Process Mining [50], specifically Heuristic Miner and Fuzzy Miner, to track user behaviors leading to \"Aha! moments\". Through data cleaning and retention clustering, it identified key activation actions by reducing model complexity by 62%. However, manual data processing was time-intensive. Linear Mixed Models (LMMs) were utilized in [37] to evaluate usage continuance. It accounted for repeated measures and individual client variability through fixed and random effects. For existing clients, the model achieved an RMSE of 5.23, though it struggled with new clients (RMSE: 13.93), indicating a gap in generalization. Activation measures (banners) significantly increased usage (+5.2%). PLS-SEM (Partial Least Squares Structural Equation Modeling) was employed in [31] to validate the effect of UI design on satisfaction and loyalty. The results showed strong model reliability ([PERSON]'s alpha: 0.837-0.860) and high explanatory power (R\({}^{2}\) - 0.50). All hypotheses (UI design \(\rightarrow\) Satisfaction \(\rightarrow\) loyalty) were statistically supported. Focusing on the topic of customer segmentation, the study [25] introduced a joint modeling framework that combined Generalized Linear Mixed Models (GLMMs) [51] to track players' in-game behavioral patterns with a Shared Parameter Model (SPM) [52] that linked these behavioral groups to the probability of dropout. This integrated approach significantly outperformed traditional models like Cox regression, improving retention prediction by 63.2% at higher player levels and reducing RMSE for lifetime engagement by 25.5%. Authors in [29] took a more econometric route, applying a Dynamic Probit Model [53] with copula-based corrections for endogeneity. It incorporated exponentially decaying goodwill stocks to model user touchpoints and usage effects. In this study regularization via Lasso/Ridge regression was also utilized, addressing multicollinearity and improving model stability. The study achieved a 9.3% error reduction in predictions and key findings emphasized that consumer-initiated touchpoints and persuasive content boosted conversions to premium users, while firm-initiated ads had negative effects. Study [33] introduced the AQUAMan negotiation mechanism, combining quantile estimation, opponent modeling, and surplus redistribution to manage service acceptability. Under both normal and extreme loads (up to 2500 users/min), it maintained high acceptability rates (95%+ and 93.3%, respectively) while keeping costs and overhead manageable. At last, study [35] outlined a range of qualitative (interviews, questionnaires, incident reports) and quantitative (A/B testing, in-product surveys and online ads, crowdfunding and crowdsourcing platforms, click-based user data collection) methods deployed for collecting and integrating customer feedback, as well as insights into their performance across several companies. This study concluded that there's no single perfect approach and that the choice and success of methods depend heavily on the company's size, the structure of the product offering (custom vs. SaaS), the type of customers (B2B vs. B2C), the stage of development and the internal communication and data integration structure. Table 2 summarizes the results regarding the use of machine learning in the reviewed approaches for SaaS. Specifically, Table 2 includes the proposed ML algorithm for the reviewed papers along with the compared methods. Results are split into two categories, indicating the most commonly used metrics and miscellaneous across the reviewed papers. Additionally, information about the used datasets and employed validation methods is included. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.