Customer transaction databases contain relevant information that is related to the implementation of customer relationship management practice. In this study, we use transaction records of non-contract type customers of a domestic internet phone company to estimate individual customer relationship hazard rates, analyze customer lifetime length and lifetime value, and construct customer relationship hazard forecast models for different types of customers. The empirical results indicate that the MSE and error rate are 0.000077 and 1.30631% for the training set, and 0.002469 and 3.636028% for the testing data set by using the back propagation neural network technique to estimate customer relationship hazard rates. Both highlight good performance. The medians of average customer relationship length and average remaining relationship length are 9.14 and 1.97 months respectively. The average customer value is about 964 dollars which is equivalent to 615 million dollars in total. Customers are separated into two groups by using autocorrelation functions and partial autocorrelation functions. The customer relationship hazard rate forecasting models for the two groups are AR(1) and white noise models, respectively.
|Translated title of the contribution||A Data Mining Study on Customer Transaction Data Base-Using Non-contracted Customers of a Domestic I-Phone Company as an Example|
|Original language||Chinese (Traditional)|
|Number of pages||17|
|Publication status||Published - 2005|
- Customer Relationship Management
- Neural Network