  Whether stochastic or parametric, the Pareto/NBD model can only be utilized
for an in-sample prediction rather than an out-of-sample prediction. This
research thus provides a neural network based extension of the Pareto/NBD model
to estimate the out-of-sample parameters, which overrides the estimation burden
and the application dilemma of the Pareto/NBD approach. The empirical results
indicate that the Pareto/NBD model and neural network algorithms have similar
predictability for identifying inactive customers. Even with a strong trend
fitting on the customer count of each repeat purchase point, the Pareto/NBD
model underestimates repeat purchases at both the individual and aggregate
levels. Nonetheless, when embedding the likelihood function of the Pareto/NBD
model into the loss function, the proposed parameter estimation method shows
extraordinary predictability on repeat purchases at these two levels.
Furthermore, the proposed neural network based method is highly efficient and
resource-friendly and can be deployed in cloud computing to handle with big
data analysis.
