Predictive performance of customer lifetime value models in e-commerce and the use of non-financial data

Abstract

The article contributes to the knowledge of customer lifetime value (CLV) models, where extensive empirical analyses on large datasets from online stores are missing. Based on this knowledge, practitioners can decide about the deployment of a particular model in their business and academics can design or enhance CLV models. The article presents predictive performance of selected CLV models: the extended Pareto/NBD model, the Markov chain model, the vector autoregressive model and the status quo model. Six large datasets of medium and large-sized online stores in the Czech Republic and Slovakia are used for a comparison of the predictive performance of the models. Online stores have annual revenues in the order of tens of millions of euros and more than one million customers. The comparison of CLV models is based on selected evaluation metrics. The results of some of the models which use additional non-financial data on customer behaviour – the Markov chain model and the vector autoregressive model – do not justify the effort which is needed to collect such data. The advantages and disadvantages of the selected CLV models are discussed in the context of their deployment.