## 3 Results
### Application of the Methodology
#### 3.2.2 Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **Ps** & **P9** & **P10** & **P11** & **P12** \\ \hline \multirow{9}{*}{Anti-image covariance} & P1 & 0.965 & 0.037 & 0.027 & \(-\)0.009 & 0.007 & 0.020 & \(-\)0.015 & \(-\)0.094 & 0.004 & 0.071 & 0.037 & \(-\)0.073 \\  & P2 & 0.037 & 0.952 & 0.008 & 0.072 & \(-\)0.043 & \(-\)0.076 & 0.020 & \(-\)0.062 & 0.010 & 0.072 & 0.006 & 0.001 \\  & P3 & \(-\)0.27 & \(-\)0.008 & \(-\)0.643 & \(-\)0.254 & \(-\)0.075 & \(-\)0.069 & 0.012 & 0.048 & 0.148 & \(-\)0.100 & 0.048 & 0.007 \\  & P4 & \(-\)0.009 & 0.072 & \(-\)0.254 & 0.546 & \(-\)0.109 & \(-\)0.032 & \(-\)0.077 & \(-\)0.024 & \(-\)0.103 & 0.111 & 0.039 & \(-\)0.041 \\  & P5 & 0.007 & \(-\)0.043 & \(-\)0.075 & \(-\)0.109 & 0.542 & \(-\)0.091 & \(-\)0.078 & \(-\)0.175 & \(-\)0.061 & 0.047 & \(-\)0.010 & 0.046 \\ Anti-image & P6 & 0.020 & \(-\)0.076 & \(-\)0.069 & \(-\)0.032 & \(-\)0.091 & 0.529 & \(-\)0.240 & \(-\)0.005 & \(-\)0.016 & \(-\)0.006 & \(-\)0.031 & 0.055 \\  & P7 & \(-\)0.015 & 0.020 & 0.012 & \(-\)0.077 & \(-\)0.078 & \(-\)0.240 & \(-\)0.520 & \(-\)0.043 & \(-\)0.076 & \(-\)0.002 & 0.002 & \(90.66\times 10^{5}\) \\  & P8 & \(-\)0.094 & \(-\)0.069 & 0.048 & \(-\)0.024 & \(-\)0.175 & \(-\)0.005 & \(-\)0.043 & \(0.096 & \(-\)0.098 & \(-\)0.053 & \(-\)0.028 & 0.041 \\  & P9 & 0.004 & 0.012 & 0.148 & \(-\)0.103 & \(-\)0.061 & \(-\)0.016 & \(-\)0.076 & \(-\)0.098 & 0.474 & \(-\)0.166 & \(-\)0.119 & \(-\)0.064 \\  & P10 & 0.071 & 0. The proposed method was applied by two minor companies that are dedicated to the sale of school supplies. These companies, within their stock, offer many products, such as markers, notebooks, pens, scissors, etc. These companies during the pandemic were financially compromised since their line of business was based on the direct interaction with the consumer [40]. By applying the method and defining each of the factors that affected their clientele, these companies decided to scale to eCommerce. Three months after the application, the effectiveness was measured through a quick survey of the owners of the company; in this survey, they were asked to indicate the result achieved vs. the expected result. In the two companies, they established that the result achieved was between 70 and 75% vs. the 80% expected result after three months of execution. With these data, it was identified that the effectiveness of the application of the method was 87.5%. For the evaluation of the results, a machine learning algorithm was applied, with the objective to train an AI model and compare the results obtained from the factorial analysis. The algorithm applied was a decision tree, where the 12 questions posed in the survey and the corresponding data were considered, and to this, the general query on the frequency of use of eCommerce was added. The validity of the models was checked through cross-validation, a very common verification method. N-fold cross-validation divides the training set into N parts, using N-1 parts to train and one part to test. It repeats this N times and finally calculates the result. The dataset with the survey responses was configured for validation as follows: The total data corresponding to 723 surveys were used in two groups, where 70% of the data were used for training, and the remaining 30%, for tests. To measure this quality, precision was used, which expressed the percentage of values that were correctly classified. The training dataset that corresponded to 70% considered 506 people, and the 30% of test data were derived from 217 surveys, which corresponded to the most representative population, as calculated in the above sections. When analyzing the model training dataset, the results presented in Table 14 were obtained. The total number of instances was 506, of which 75.4941% were classified as correct and 24.5059% as incorrect instances. The absolute error was 32.3%, and the relative root square error was 72.38%.