The Complaint Data Application Research Based On Data Mining

In the era of big data, vast amounts of important data information which is useful for business and management decision is contained in big data. As large enterprises, how to effectively use remained data resources,and maximize their commercial value, is the key to maintain the core competitiveness of enterprises in the new economic normality. Complaint data can not be ignored in each communications company and plays a key role on the customer relationship management and the company’s sustainable development. However, faced with the large amount of complaints of mobile communication, it is of great significance for the companies to know how to effectively maintain the existing customer base, good customer care, customer management, customer promotion, prevention of loss of customers.The paper analysis mobile companies’s customer complaint data by data mining techniques,and analysis mobile communications complaints data by clustering analysis which is carried out by comparing the different values of k. The comparing greatly improves the advantages of clustering and achieve a good clustering performance. Mobile communications industry knows specific problems which arise in clusters among reasonable response options for each cluster, launch the appropriate package deals and selection by cluster analysis,. Express decision tree offers an effective and reasonable classification for customer complaint data.Analyzing each property and putting forward effective solutions will greatly reduce customer churn degrees. The cost of retaining the so-called old customers is far higher than opening up new customers. Reasonable classification of the customer complaints, and making the appropriate response measures can effectively prevent the loss of customers. Using association rules to prediction analyze the complaints data can achieve more reasonable, high confidence goals.And Mobile Communications can effectively deal with the potential complaints Correlation predictions can further enhance the quality of service and reduce the number of customer complaints. The decreasing of the number of complaints indicates the growth of the company’s performance,and the promotion of the company’s core competitiveness.The software classifies customers and establishes targeted package combinations towards different customer groups;The data mining software is effectively predicting customer churn and effective retention for the loss of the large customer; the introduction of more selling than other carriers packages to attract potential consumers. Experiment SPSS Clementine data mining software concludes clustering, decision trees and association rules three algorithms, and the experimental results demonstrate the feasibility and effectiveness of the algorithm, provides a research ideas and methods of analysis for the mobile company customer relationship management, what’s more,research content has certain theoretical significance and practical value.