## 5 Discussion  
### Assessing the Results  
Each technique offers unique strengths and limitations, and different methods may be more appropriate depending on the specific needs and contexts faced by corporate decision-makers. Statistical methods often focus on hypothesis testing and the identification of clear \begin{table}
\begin{tabular}{c c c c} \hline \hline
**From/To** & **0** & **1** & **Total** & **\% Correct** \\ \hline
0 & 18 & 9 & 28 & 86.74 \\ \hline
1 & 8 & 6 & 14 & 73.55 \\ \hline Total & 15 & 8 & 23 & 76.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of the SVM. \begin{table}
\begin{tabular}{c c} \hline \hline Positive class & 1 \\ \hline Number of observations in the training set & 137 \\ \hline Bias & 0.000 \\ \hline Number of support vectors & 65 \\ \hline \multirow{6}{*}{Features used (independent variables)} & Website optimization (WOPT) \\  & Social media marketing (SMM) \\  & Email marketing (EMAIL) \\ \cline{1-1}  & Content marketing (CMA) \\ \cline{1-1}  & Customer relationship management (CRM) \\ \cline{1-1}  & Online advertising (OA) \\ \cline{1-1}  & Data management and analytics (DMA) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Details of the SVM classifier. causal relationships, while data mining approaches emphasize pattern recognition and the discovery of hidden structures within the data. Statistical methods, such as regression analysis, are often grounded in well-established theoretical frameworks and offer interpretable, transparent models. They excel at testing hypotheses, inferring causal relationships, and providing confidence intervals that help assess the reliability of the findings. These characteristics can make statistical methods well-suited to situations where understanding the underlying mechanisms is important. However, these methods sometimes struggle with capturing complex, nonlinear relationships and may require strong assumptions about the data distribution. They can be sensitive to outliers and may not perform as effectively when working with very large or unstructured datasets. Data mining methods including techniques such as decision trees, neural networks, and clustering algorithms tend to focus on revealing hidden structures, patterns, and associations within the data. They do not typically require strict assumptions about the underlying distributions and can handle more complex, large-scale datasets. These methods are often more flexible and can uncover insights that traditional statistical techniques might miss. At the same time, data mining approaches can be more difficult to interpret, which can create challenges for decision-makers who need to understand the rationale behind a recommendation. Additionally, some data mining techniques may require extensive fine-tuning and computational resources, and their results can vary depending on the hyper parameter settings and data preprocessing steps. Optimally, an integrated approach that combines the strengths of both statistical and data mining methods can yield more comprehensive insights. Such a strategy might involve using statistical methods to establish reliable baselines, validate core assumptions, and ensure interpretability, while simultaneously applying data mining techniques to detect patterns and relationships that might not be captured otherwise. The results of these complementary methods can be combined, weighted, or merged into a unified framework or ensemble model that incorporates interpretability, robustness, and pattern recognition capabilities. This hybrid methodology can enhance both the reliability and practical utility, offering decision-makers a more thorough understanding of the data's characteristics and trends. A balanced, integrated approach would likely be the most appealing choice. Statistical methods alone offer clarity and theoretical grounding, but they sometimes fail to capture the complexity and hidden relationships within large, diverse datasets.