Application Of Data Mining Technology In Telecom Customer Churn

In the highly competitive and saturated telecom market,the telecom industry is suffering from the huge loss brought by the high turnover rate,so it is urgent to solve the problem of customer loss.If the past transaction data of customer can be analyzed to identify customers with loss tendency in advance,the loss caused by customer churn can effectively avoided by finding out the reasons for customer churn and taking targeted retention measures.Data mining can discover unknown knowledge and information which could help us make decision through the exploration of massive historical data.Data mining plays an important role in telecom enterprises' accurate marketing and identification of user fraud.In this paper,Data mining is used to settle a matter of customer churn.Such data mining tools as Python and SPSS Modeler are used to discuss the establishment of telecom customer churn prediction model by Logistic regression,random forest and LightGBM algorithm.The grid search algorithm and random search algorithm are used to tune the parameters of the model,taking regard F1-Score and AUC as a model evaluation indicator.The comparison results show that the integrated learning algorithm LightGBM is better than the others.Its AUC is 0.852 and F1-Score is 0.5911.After predicting the likelihood of customer churn,the retention measures are developed in the form of customer segments.The K-means clustering algorithm is used to construct the customer segmentation model,and the elbow method is used to determine the optimal cluster number K.Combining the experience to analyze the characteristics of the lost customer group,the customer is divided into affordable family,online family and the service-sensitive family.Based on the consumption characteristics of different lost customer groups,an effective retention strategy can be developed.