## 3 Research Methodology  
### Method Overview  
The current study leverages descriptive and predictive statistics using a series of classification models assessment for user engagement classification levels. The empirical investigation is incorporated in four phases:  
The first phase responds to RO1, RO2, and RO3, providing predictive statistical analysis to explore the relations among user engagement metrics and generate a set of decision-making rules for marketers [21]. The second phase replies to RO4, offering the statistical analysis of the data mining processes that takes place and identifying the most dominant classifiers' performance. The models refer to some of the best-performing classifiers, including DTs classifiers, NB, and k-NN. Following, the classification models are applied to predict the behavior of the dataset features [21; 22; 23; 24; 25]. The third phase examines the RO5, RO6, and RO7, highlighting the performance of the most dominant classifier appliance on user interaction data to reveal potential patterns through a series of engagement level occurrences [21; 22; 23; 24; 25]. Finally, the fourth phase responds to RO8 and RO9, showing the classified instances' graphical representation along with their classification performance. This phase indicates how the dependent and independent variables are related and score high classification accuracy regarding the different numbers of class features formatting while generating a series of decision-making rules [21; 22; 23; 24; 25]. The overall assessment highlights the significant role of the ML method in creating a set of rules that are able, when used properly, to predict the metrics that can have an impact on user engagement. Also, it shows that DTs are a legitimate choice for such classification due to their simplicity, interpretability, ease of use, interpretability, lack of a need for feature scaling, ability to handle both numerical and categorical data, generalizability, and flexibility, which are the strong points that make them unique and useful [21; 22; 23; 24; 25]. The research methodology follows a structured, seven-stage flowchart, as demonstrated in Figure 3. Each stage of the methodology is based on the previous one, establishing a systematic information and process flow. Below is a detailed breakdown of each step, specifying the inputs and outputs at each stage (Figure 3) [21; 22; 23; 24; 25; 26]. * Data collection refers to the stage of user engagement data collection from the Google Analytics platform. * Data preprocessing incorporates stages of cleaning, normalization, and handling of missing data. * Data storage and management refers to data handling and storage in a database in order to extract valuable information patterns. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hypothesis No** & **Metrics** & **Assumption** \\ \hline
1. & Event count and sessions & Higher values of event count correlate with increased session engagement \\
2. & Purchase revenue and sessions & Number of sessions with higher engagement leads to increased purchase revenue \\
3. & Transactions and conversions & A larger number of transactions is associated with an increased conversion rate \\
4. & Bounce rate and purchase revenue & High values of bounce rate negatively impact purchase revenue \\
5. & Event count and conversions & Increased number of event interactions within sessions cause higher conversion rates \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hypotheses. * Model selection and data splitting refers to the selection process of ML models such as decision trees (DTs), Naive Bayes (NB), and k-nearest neighbors (k-NN), and dataset partitioning in training, validation, and testing sets. * Model training and evaluation represent the training process with performance metrics like classification accuracy and area under the curve (AUC) evaluation. * Insights and analysis summarize generated information and patterns.