# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 6 Discriminant Telecom Customer Loss Model
### Empirical Analysis for the Discriminant Model
#### 6.1.2 Analysis of Discriminant Model  
The discriminant model's eigenvalues were analyzed to identify the discriminating judgment power of the function. Then, [PERSON] lambda discriminant test was applied to confirm the significance of the discriminant function, i.e., whether the discriminant function was valid or not. Afterward, [PERSON]'s linear discriminant function was used for the telecom customer loss prediction equation, indicating the key factors (F1, F2, F3, F4, F5, and F6) that could influence the telecom customer churn. Finally, an accuracy test was conducted for the discriminant function to investigate the accuracy of the discriminant equation. (1) Eigenvalues of the discriminant function  
The discriminant model was used in the analysis. In the table below, when the discriminant model's eigenvalue is higher, the model's discriminating judgment power is higher. The last column represents the canonical correlation coefficient, while the results reveal an acceptable range due to the discriminant function's eigenvalue (0.030) and canonical correlation (0.171) (Table 17). In the Table 17, \"a\" means that the former canonical discriminant function was used in the analysis. \begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Ingredient**} \\  & **1** & **2** \\ \hline China Unicom’s SMS quantity & 0.596 & \(-\)0.106 \\ China Mobile’s SMS quantity & 0.570 & 0.035 \\ China Telecom’s SMS quantity & 0.295 & \(-\)0.034 \\ China Unicom’s MMS quantity & 0.011 & 0.614 \\ CRBT & \(-\)0.120 & 0.685 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Component score coefficient matrix. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**The Function**} & **The Eigenvalues** & **Variance** & **Accumulative** & **Canonical** \\  & **Eigenvalues** & **Percentage** & **Percentage** & **Correlation** \\ \hline \(1\) & 0.030a & 100 & 100 & 0.171 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Eigenvalues. Wilks' lambda is the ratio of the within-group sum of squares to the total sum of squares. The value is one when the group means for all observations are equal; it is close to zero when the within-group variation is small compared to the total variation. Thus, a large Wilks' lambda value indicates that the means of each group are more or less equal; a small Wilks' lambda value shows that the means of each group are different. It can be seen from Table 18 that the first discriminant function explained 97.1% of all variations. Moreover, the value of Sig. was 0.000 \(<\) 0.05, meaning that this discriminant function was significantly established. The established [PERSON] discriminant equation was as follows, which was adapted from [PERSON] [6]:  
Y1 = \(-16.592-1.518\times\) F1 + 0.257\(\times\) F2 + 0.588\(\times\) F3 + 6.021 \(\times\) F4 \(-\) 0.712 \(\times\) F5 \(-\) 1.051 \(\times\) F6 + 5.963 \(\times\) Gender. Y2 = \(-4.810-0.100\times\) F1 + 0.176 \(\times\) F2 + 0.135 \(\times\) F3 + 0.291 \(\times\) F4 \(-\) 0.211 \(\times\) F5 \(-\) 0.020 \(\times\) F6 + 6.397 \(\times\) Gender. The discriminant model indicates the top factors that could be used to forecast the telecom customer churn. The classification is considered to be Y1 if the result is one, revealing customer churn. If the result is zero, the classification is Y2, suggesting customer retention. Accuracy test for discriminant function  
One hundred random samples from the dataset were chosen to conduct the accuracy test. The results are shown in Table 20. Half of them were lost customers, and half were retained customers. The one hundred random samples were imported into the telecom customer churn discrimination model. Then, the predicted customers churn results were obtained to judge the prediction accuracy rate of the model.