• Journal of Global Scholars of Marketing Science
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• v.16 no.1
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• pp.13-26
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• 2006

In this research, we develope a procedure for detecting a random non-stationarity to the individual's purchasing rate in a stationary NED model. On this purpose, we derive the likelihood ratio statistic for a testing null and alternative hypotheses defined as whether there is no significant structural change in a stationary NED model or any. Where the structural change comes from a random non-stationarity(marketing mix activities or seasonality, for example) to the individual's purchasing rate. We also apply the developed method to a panel data for a frequently purchased good. This research could be a solution to include the non-stationarity in a stationary NED model. We also expect that the developed model could give a signal for an early detection of significant changes in marketing environment, and a mean for a measurement of the effects of marketing mix activities.

• Journal of Global Scholars of Marketing Science
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• v.11
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• pp.129-146
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• 2003

This study explores whether a NBD type of model can be applied to characterize the underlying frequency distribution of online consumer's visit behavior. In this study, the following two research questions are addressed: (1) How can we characterize the underlying distribution pattern(s) of the number of repeat i i visits to a site? (2) How can consumer's Internet usages and his/her demographics affect the average number of visits to the site? Through the empirical investigation, this study found that NBD models are directly applicable to characterize the underlying distribution of visit frequency on the Internet. Furthermore, this study addresses some managerial implications for understanding how site visits are determined. Especially this study highlights the relationship between repeated visits and the visitors' Internet Usages and demographics. The proposed models are estimated and validated by online panel data that covers more than 1000 different sites and has 800,000 observations.