# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Data-Driven Decision Support in SaaS
### Machine Learning Methods
In work [16], LR delivered strong profit-based performance, leading in Expected Maximum Profit and Top Decile Lift when paired with usage-based features. In the research [17], LR initially had 97% accuracy but 0% recall until SMOTE-Tomek was applied, improving recall to 57% (but at the cost of reduced precision). Support Vector Machines (SVM) featured in studies [8; 13; 14; 16; 19]. SVMs often struggled--studies [8; 14] reported 100% training accuracy but just 63% test accuracy, indicating severe overfitting. However, the study [19] successfully employed a hybrid model combining SVM with Naive Bayes, achieving 95.67% accuracy, 94.3% precision, and 95.65% recall, outperforming other classifiers. In work [16], SVM delivered strong profitability metrics, rivaling LR in Expected Maximum Profit. Neural Networks (NN) (including MLPs and deep learning) appeared in [8; 14; 15; 18; 20; 23; 24]. A study [18] reported 96.5% accuracy for a Multilayer Perceptron (MLP), which, while lower than RF and AdaBoost, still offered valuable comparative insights. In works [8; 14], deep neural networks (including TensorFlow models) achieved around 82% test accuracy, with one model identifying subtle churn signals like low session frequency and high pricing. MLP was again tested in [15], performing adequately but trailing ensemble methods like XGBoost and Gradient Boosting Machine (GBM). In [24], a non-sequential NN using only aggregated features yielded lower performance (AUC not specified), underperforming compared to LSTM-based hybrids. In research [20], sentiment modeling with LSTM networks produced learned sentiment features from support ticket data, which when combined with metadata features, led to a noticeable +5% increase in accuracy and the highest recall, critical for predicting subscription renewals. Single-layer neural networks were employed in [23] achieving AUC = 0.987, though their performance degraded over time due to staleness, making them less ideal for evolving user behavior. AdaBoost [44] and other boosting techniques such as GBM [45] were included in studies [15; 18]. AdaBoost in [18] was the second-best performer with 98.4% accuracy and 0.995 AUC, while in [15], boosting methods (especially XGBoost) consistently outperformed simpler classifiers across recall, AUC, and F1-score. Figure 3: Machine learning methods proposed to use by the reviewed papers. Naive Bayes [46], though rarely a top performer, was evaluated in [8; 14; 19]. It performed poorly when used alone in [8; 14] (accuracy ~69-71%), but in [19], it formed part of the high-performing hybrid model with SVM. Transformer-based models [47] were explored in the study [22]. The FT-Transformer, built for tabular data with embedded categorical and numerical features, achieved 86.79% accuracy, 88.75% recall, and 94.9% AUC--a strong improvement over older works (AUC ~71-77%), though it was still outperformed by XGBoost (AUC 98.8%, F1 94.81%, accuracy 94.8%) in the same study. Hybrid Models were explored in [19], where an SVM-Naive Bayes combination led to superior performance across all key metrics, indicating the strength of ensemble learning strategies, especially in reducing false positives/negatives for CRM applications. Additionally, ensemble and hybrid architectures were core in studies [20; 21; 24]. Hybrid LSTM architectures combining sequential and static aggregated data outperformed single-source models [24]. Study [21] introduced the BBE-LSWCM ensemble, integrating LightGBM, BiLSTM, and LR to achieve 60% better decile lift (DL1 = 3.197) and 25% higher AUROC than standalone BiLSTM, while maintaining reasonable latency. A modular fusion of LSTM-learned sentiment features and handcrafted metadata yielded the highest accuracy and recall, demonstrating the value of combining deep learning with traditional features [20]. The researchers in [26] employed a dynamic programming model to optimize player retention over time alongside a Q-learning algorithm [48] to simulate offline decision-making and identify convergence paths for both actual and perceived quality.