Customer Active Probability and Customer Lifetime Value Analysis in Internet

As the age of digital information, marketers are in information overload. A mass of customers’ data is available but may be useless only if it can be turned into business intelligence and implement appropriate database marketing. This research aims to assist managers in discriminating and learning from their right customers that helps to serve high value customers and create successful marketing programs targeted at the prospected ones. Transaction data on the purchasing of VCD at an online retailer was used as empirical analysis; Pareto/NBD model and customer lifetime value model were applied to capture customer active probability and construct profitable customer profile. The results demonstrated four priority ranks of online customers for managers to choose the prospects that best match the profitable customer profile by observing their purchase behaviors