Although most data mining (DM) models are complex and general in nature, the implementation of such models in specific environments us often subject to practical constraints (e.g. budget constraints) or thresholds (e.g. only mail to customers with an expected profit higher than the investment...

We studied the problem of optimizing the performance of a DSS for churn prediction. In particular, we investigated the beneficial effect of adding the voice of customers through call center emails – i.e. textual information - to a churn prediction system that only uses traditional marketing...

CRM gains increasing importance due to intensive competition and saturated markets. With the purpose of retaining customers, academics as well as practitioners find it crucial to build a churn prediction model that is as accurate as possible. This study applies support vector machines in a...

Data mining involves extracting interesting patterns from data to create and enhance decision support systems. Whereas in the early days of data mining, some tasks already relied on statistical and operations research methods such as linear programming and forecasting, data mining methods...

Data mining involves extracting interesting patterns from data to create and enhance decision support systems. Whereas in the early days of data mining, some tasks already relied on statistical and operations research methods such as linear programming and forecasting, data mining methods...