# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Data-Driven Decision Support in SaaS
### Machine Learning Methods
The results indicated that both game quality and perceived quality stabilized at high levels regardless of initial quality. However, the speed of convergence depended on starting conditions, with lower initial quality (\(x_{0}\) = 0.4) taking 20 periods to converge compared to 14 periods for \(x_{0}\) = 0.6. Interestingly, when players initially overestimated quality, perceived quality followed a \"high-low-high\" trajectory. The authors of [27] employed an iterative clustering approach starting with PCA for dimensionality reduction and K-means clustering, later switching to Uniform Manifold Approximation and Projection (UMAP) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for more effective handling of sparse and non-normal data distributions. After four iterations, the final model identified six meaningful personas (e.g., Uploaders, TV Builders), which were validated through interview-based triangulation. Earlier clustering failed due to overlapping behaviors and sparse data, but the refined method succeeded in differentiating user types by combining behavioral data with qualitative insights. At last, in the study [28] a more traditional machine learning approach was adopted for behavioral customer segmentation to predict subscription conversion by evaluating several classifiers, including Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, RF, LR, SVC, and XGBoost. Among these, XGBoost achieved the highest performance, with an accuracy of 79%, precision of 80%, recall of 76%, and an F1-score of 78%. Cross-validation further confirmed the generalizability of the model with a mean score of 78.5%. However, the model exhibited a slightly higher rate of false negatives than false positives, a relevant trade-off when considering conversion sensitivity. Studies [30; 32; 36] utilized machine learning and statistical modeling to personalize user experience and predict outcomes. Study [30] employed Lasso regression [49] to personalize free trial lengths, showing a 6.8% subscription increase compared to a 30-day baseline. Simpler uniform policies like a 7-day trial still yielded a 5.59% gain. Among alternatives, Lasso outperformed random forests and causal forests, which struggled with overfitting and poor personalization. Similarly, a study [36] implemented logistic regression, random forest, and gradient boosting for dynamic pricing in usage-based models. While logistic regression achieved perfect accuracy on simulated data, real-worldapplicability favored gradient boosting and random forest, with accuracies around 0.77 and 0.75, respectively. Study [32] leveraged hierarchical logistic regression within the Cross Industry Standard Process for Data Mining (CRISP-DM) framework to predict customer retention and cross-buying. Retention prediction reached 93.7% accuracy (F1: 94.38%), while cross-buying was less predictable (accuracy: 78.9%, F1: 20.60%). Finally, in the work presented in [38], the authors used a hierarchical ensembled machine learning framework for predicting CLV in B2B SaaS companies. The CLV prediction was framed as a lump sum regression task rather than a traditional time-series forecasting problem, enabling the use of rich supervised learning models like LightGBM, XGBoost, LASSO regression, KNN, and Auto ARIMA. To handle limited and drifting historical data, a two-step hierarchical model was built: first predicting over a short time horizon, then mapping it to a longer horizon. They also adopted an ensemble approach, segmenting customers based on key features (like size) and fitting different models for different groups (LightGBM for smaller customers, LASSO for enterprises). In performance tests, the lump sum regression models outperformed traditional time-series models by 2 to 5 times across metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and symmetric mean absolute percentage error (SMAPE), with LightGBM giving the best results. Few researchers, however, opted to utilize several methods that rely on statistical, econometric or heuristic models.