Document Type

Thesis or dissertation

Date of this Version

6-30-2021

Advisor

Peter Fader

Abstract

Overall, the paper explored the discrete buy-till-you-die process by incorporating covariate effects and non-parametric priors. In doing so, the paper hopes to enrich the space of customer lifetime value modelling. Using a simulation-based approach, the paper found that models with non-parametric priors is capable of adequately picking up simplified parametric distribution shapes without drastically overfitting and offer some predictive improvement in cases where a multi-modal distribution exists. In addition, the paper also found that models missing covariate specification, when such effect is present, may generate systematic upward bias to the parameter values. Such bias will lead to a bad aggregate level model performance even when such covariates are missing in the future while also causing the model to underpredict individual and aggregate conversions when covariates are in fact present. The paper also performed a market simulation that showed how the covariate effect extracted from the models can help firms better perform targeted marketing to improve its ROI.

Keywords

Customer Lifetime Value, Marketing, Dirichlet Process, Covariate Marketing Mixes, Probability Models

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Date Posted: 10 August 2021

 

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