|
摘要(英)
|
As deregulation and new competitors open
up the telecommunications industry, the cellular phone market has
become more competitive than ever. To survive or maintain an advantage
in such a competitive marketplace, many telecommunications companies
are turning to data mining techniques to resolve such challenging
issues as fraud detection, customer retention, and prospect profiling.
In this thesis, we focused on developing and applying data mining
technique to support the churn prediction. Constrained by limited
customer profiles and general demographics, the proposed approach
applied a decision tree induction technique (i.e., C4.5) to discover
a classification model for churn predication solely based on the call
records. To deal with the training data with a highly skewed distribution
on decisions (i.e., around 2% churners and 98% non-churners), a multi-expert
strategy was adopted. The empirical results showed that the proposed
technique was effective in predicting at-risk cellular phone customers
(i.e., potential churners). The proposed technique could identify
50.64% churners by selecting 10.03% of the population, and 68.62%
churners by selecting 29.00% of the population.
|