Neural Network Based Parameter Estimation Method for the Pareto/NBD Model
This work addresses a domain-specific problem in customer behavior modeling for businesses, offering an incremental improvement by combining existing neural network methods with the Pareto/NBD model to enhance efficiency and scalability for big data analysis.
The paper tackles the limitation of the Pareto/NBD model, which is restricted to in-sample predictions, by proposing a neural network-based extension for out-of-sample parameter estimation, resulting in improved predictability for repeat purchases at individual and aggregate levels when embedding the model's likelihood function into the loss function.
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.