# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Data-Driven Decision Support in SaaS
### Machine Learning Methods  
Regarding RQ3, most of the studies relied solely on machine learning methods to conduct their research and generate their expected results. Among the papers focused on churn prediction, Random Forest (RF) [39; 40; 41] was one of the most widely used methods, appearing in studies [8; 13; 14; 15; 16; 17; 23; 24]. In the study [18], RF with 64 estimators achieved an excellent performance of 98.8% accuracy, 0.997 Area Under the Curve (AUC), and strong F-measures (0.989 non-churn, 0.981 churn). In studies [8; 14], RF also stood out with 87% testing accuracy, while being resistant to noise and overfitting. A study [13] reported exceptionally strong RF results as well, with F1-score of 92.6% and recall of 91.6%, while a study [15] found that RF had the highest precision (0.80) among the models tested, though recall lagged behind XGBoost. On the contrary, RF underperformed in the study [12], with an AUC of ~0.5, and adding PCA (Principal Component Analysis) did not have any impact on the results. In work [23], RF achieved AUC > 0.99 and 97% accuracy, outperforming all other models across various labeling schemes like sliding windows and activity quartiles while in the study [24] RF was used as a baseline combining sequential and aggregated data, where it achieved an AUC of 0.72, which was notably lower than the hybrid Long Short-Term Memory (LSTM) models, indicating its limitations in modeling temporal dynamics in sequential data. In the study [17], RF showed only moderate success with AUC-ROC ~0.65, though its performance improved slightly when paired with SMOTE-Tomek for class imbalance handling. Figure 3 presents the number of papers that successfully employed specific machine learning algorithms. Thus, it contains the number of papers to which the specific machine learning algorithm noted the best results. RF was successfully used in more works than any other machine learning algorithm. Extreme Gradient Boosting (XGBoost) [42] was tested in studies [12; 15; 16], consistently delivering top-tier results. In research [12], XGBoost achieved the highest AUC (0.7941) and, after threshold tuning, improved sensitivity to 74%. Study [15] reported ROC AUC of 0.86, recall of 0.85 and F1-score of 0.82, outperforming other models such as Random Forests and Logistic Regression and identified ticket resolution time and license age as key features. Study [16] found XGBoost and Logistic Regression achieved comparable AUC (~60%), but XGBoost was less profitable than logistic regression when measured by Expected Maximum Profit. XGBoost and Gradient Boosting Decision Trees (GBDT), were also used in [22] with XGBoost emerging as the best-performing model with 98.8% AUC, while GBDT followed with 93.4% AUC. Decision Trees (DT) were used in studies [8; 13; 14; 16; 19]. In work [13], DT was the best performer using Chi-squared-selected features, with 88.2% F1-score and 94.4% recall. In contrast, studies [8; 14] revealed DT's tendency to overfit, achieving perfect training accuracy but only 76% on test data. DT also served as a baseline in [16; 19], with moderate performance compared to ensemble and hybrid models. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Usage** & \multicolumn{2}{c}{**Transactional/**} & \multirow{2}{*}{**Customer**} & \multirow{2}{*}{**Financial**} & \multirow{2}{*}{**Customer**} & \multirow{2}{*}{**Satisfaction**} & \multirow{2}{*}{**Survey/Interview**} & \multirow{2}{*}{**Marketing**} \\  & **Behavior** & & & & & & & & \\  & **Metrics** & & & & & & & \\ \hline
[16] & ✓ & ✓ & ✓ & & & ✓ & & & \\
[28] & ✓ & ✓ & & & & ✓ & & & \\
[35] & ✓ & & & & & ✓ & ✓ & ✓ & \\
[17] & ✓ & ✓ & ✓ & ✓ & & ✓ & & \\
[36] & & ✓ & & & ✓ & & & & \\
[34] & ✓ & ✓ & & & & & & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Logistic Regression (LR) [43] was applied in studies [8; 12; 13; 15; 16; 17], mostly as a baseline due to its interpretability. Its AUC ranged from 0.71 to 0.74 in studies [8; 12], and it consistently offered stable, if lower, performance.