# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 8 Discussion  
The data are mainly from three major Chinese telecom operators: China Mobile, China Unicom, and China Telecom. This study aimed to use factor analysis to investigate the business characteristics of telecom clients and to build a discriminant model and a logistic regression model to predict telecom client churn. We showed how the Fisher discriminant equations and logistic regression analysis could be applied to build a telecom customer churn prediction model and achieve better evaluation metrics results for accuracy. After comparison, we suggest that the logistic regression approach performs better when building a telecom customer churn prediction model, with an accuracy rate of 93.94%. Today's market is getting more competitive [41]. Telecom companies must make critical decisions and develop effective retention methods to avoid customer churn, as retaining existing customers is much less expensive [2]. In a competitive environment, retaining customers is critical. The telecom customer churn prediction model constructed \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & **Regression** & **Standard** & \multirow{2}{*}{**z Value**} & **Wald \(\chi^{2}\)** & \multirow{2}{*}{_p Value_} & **OR** & **OR Value** \\  & **Coefficients** & **Error** & & & & **Value** & **95\% CI** \\ \hline Total fee receivable for & \(-0.002\) & \(0.005\) & \(-0.402\) & \(0.162\) & \(0.688\) & \(0.998\) & \(0.988\)–\(1.008\) \\ the month & & & & & & & \\ Fixed monthly cost & \(-0.308\) & \(0.027\) & \(-11.564\) & \(133.734\) & \(0.000\) & \(0.735\) & \(0.698\)–\(0.774\) \\ Local fee & \(-0.077\) & \(0.010\) & \(-7.979\) & \(63.665\) & \(0.000\) & \(0.926\) & \(0.908\)–\(0.943\) \\ Roaming fee & \(0.023\) & \(0.021\) & \(1.051\) & \(1.104\) & \(0.293\) & \(1.023\) & \(0.981\)–\(1.067\) \\ Unicom network fee & \(0.041\) & \(0.010\) & \(3.988\) & \(15.906\) & \(0.000\) & \(1.041\) & \(1.021\)–\(1.062\) \\ Fee with China Mobile & \(0.031\) & \(0.009\) & \(3.639\) & \(13.243\) & \(0.000\) & \(1.032\) & \(1.014\)–\(1.049\) \\ Fee with fixed line & \(0.032\) & \(0.010\) & \(3.254\) & \(10.590\) & \(0.001\) & \(1.032\) & \(1.013\)–\(1.052\) \\ China Unicom SMS quantity & \(0.003\) & \(0.001\) & \(3.466\) & \(12.014\) & \(0.001\) & \(1.003\) & \(1.001\)–\(1.004\) \\ China Mobile SMS quantity & \(0.004\) & \(0.001\) & \(7.168\) & \(51.379\) & \(0.000\) & \(1.004\) & \(1.003\)–\(1.005\) \\ China Telecom SMS quantity & \(0.003\) & \(0.006\) & \(0.511\) & \(0.261\) & \(0.609\) & \(1.003\) & \(0.992\)–\(1.014\) \\ China Unicom MMS quantity & \(0.009\) & \(0.002\) & \(4.250\) & \(18.058\) & \(0.000\) & \(1.009\) & \(1.005\)–\(1.013\) \\ CRBT & \(0.238\) & \(0.031\) & \(7.599\) & \(57.740\) & \(0.000\) & \(1.268\) & \(1.193\)–\(1.348\) \\ Total monthly traffic MOU & \(-0.539\) & \(0.252\) & \(-2.143\) & \(4.592\) & \(0.032\) & \(0.583\) & \(0.356\)–\(0.955\) \\ Total monthly caller MOU & \(-0.016\) & \(0.006\) & \(-2.711\) & \(7.348\) & \(0.007\) & \(0.984\) & \(0.973\)–\(0.996\) \\ Total monthly called MOU & \(-0.057\) & \(0.023\) & \(-2.529\) & \(6.395\) & \(0.011\) & \(0.945\) & \(0.904\)–\(0.987\) \\ Total local MOU & \(0.559\) & \(0.252\) & \(2.217\) & \(4.916\) & \(0.027\) & \(1.749\) & \(1.067\)–\(2.867\) \\ Total local called MOU & \(0.039\) & \(0.022\) & \(1.812\) & \(3.284\) & \(0.070\) & \(1.040\) & \(0.997\)–\(1.086\) \\ Total long-distance MOU & \(0.548\) & \(0.251\) & \(2.182\) & \(4.763\) & \(0.029\) & \(1.730\) & \(1.057\)–\(2.830\) \\ Total Roaming MOU & \(0.510\) & \(0.254\) & \(2.010\) & \(4.041\) & \(0.044\) & \(1.665\) & \(1.013\)–\(2.736\) \\ Intercept & \(-2.056\) & \(0.121\) & \(-16.974\) & \(288.116\) & \(0.000\) & \(0.128\) & \(0.101\)–\(0.162\) \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Binary logistic regression prediction accuracy rate.