# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 7 Logistic Regression Model of Telecom Customer Churn Prediction  
It can be seen from Table 21 that a total of 19 items, such as the Total fee receivable for the month, are independent variables. Moreover, filter_$, which means the customer is lost or retailed, is the dependent variable for binary logistic regression analysis to build the customer loss prediction model. When filter_$ is one suggests that the customer is lost. When filter_$ is 0, it suggests that the customer will be retained. Based on these results, we can estimate whether or not a customer will stay with a telecommunications service provider based on the information in the dataset. The model formula is: \(\ln(p/1-p)=-2.056-0.002\times\) Total fee receivable for the month \(-\) 0.308 \(\times\) Fixed monthly cost \(-\) 0.077 \(\times\) Local fee + 0.023 \(\times\) Roaming fee + 0.041 \(\times\) Unicom network fee + 0.031 \(\times\) Fee with China Mobile + 0.032 \(\times\) Fee with fixed-line + 0.003 \(\times\) China Unicom SMS quantity + 0.004 \(\times\) China Mobile SMS quantity + 0.003 \(\times\) China Telecom SMS quantity + 0.009 \(\times\) China Unicom MMS quantity + 0.238 \(\times\) CRBT \(-\) 0.539 \(\times\) Total monthly traffic MOU \(-\) 0.016 \(\times\) Total monthly caller MOU \(-\) 0.057 \(\times\) Total monthly called MOU + 0.559 \(\times\) Total local MOU + 0.039 \(\times\) Total local called MOU + 0.548 \(\times\) Total long-distance MOU + 0.510 \(\times\) Total Roaming MOU (where \(p\) represents the probability that filter_$ is 1, which indicates that the customer will be lost. Furthermore, 1-\(p\) represents the probability that filter_$ is 0, which indicates that the customer will be retained).  
According to the parameter test, it can be seen that the regression coefficient of the total fee receivable for the month was \(-\)0.002, but this was not significant, since z = \(-\)0.402 and \(p\) = 0.688 \(>\) 0.05. This suggests that the total fee receivable for the month will not affect filter_$. Thus, hypothesis 1 was rejected, meaning that the total monthly fee receivable does not positively impact customer loss. The regression coefficient of the fixed monthly cost was \(-\)0.308, which was significant, since z = \(-\)11.564 and \(p\) = 0.000 \(<\) 0.05, suggesting that the fixed monthly cost will have a significant negative impact on the customer churn. Moreover, the dominance ratio (OR value) was 0.735, suggesting that when the fixed monthly cost increases by one unit, the decrease in Y is 0.735 times. Thus, hypothesis 2 was rejected, suggesting that the monthly fixed cost does not positively impact customer loss. The summary analysis showed that Unicom's network fee, China Mobile's network fee, fixed-line fee, China Unicom's SMS quantity, China Mobile's SMS quantity, CRBT, total local MOU, total long-distance MOU, and total roaming MOU have a significant favorable influence on the customer churn. On the other hand, the fixed monthly cost, local fee, total monthly traffic MOU, total monthly caller MOU, and total monthly called MOU significantly negatively impact the customer churn. However, the total fee receivable for the month, roaming fee, China Telecom's SMS quantity, and total local called MOU do not affect the customer churn. Therefore, H1, H2, H3, H4, H8, H9, H10, and H13 were rejected, while H5, H6, H7, H11, and H12 were confirmed. \begin{table}
\begin{tabular}{l l l l} \hline
**Test Results** & **Customer Retain** & **Customer Loss** & **Sum** \\ \hline Total & 50 & 50 & 100 \\ Successful prediction & 36 & 39 & 75 \\ Failure prediction & 14 & 11 & 25 \\ The accuracy rate & 0.720 & 0.780 & 0.750 \\ \hline \end{tabular}
\end{table} 
Table 20: Discriminant result checklist—adapted from [PERSON] [6]. In Table 22, the model's overall prediction accuracy is shown to be 93.94%, and the model's fit is acceptable. The logistic regression analysis and hypothesis tests show that expense, SMS, and call information factors influence customer churn.