Modeling and Application of Customer Lifetime Value in Online Retail

Pavel Jasek,Lenka Vrana,Marek Kobulsky,Lucie Sperkova,Zdenek Smutny

doi:10.3390/informatics5010002

Abstract

This article provides an empirical statistical analysis and discussion of the predictive abilities of selected customer lifetime value (CLV) models that could be used in online shopping within e-commerce business settings. The comparison of CLV predictive abilities, using selected evaluation metrics, is made on selected CLV models: Extended Pareto/NBD model (EP/NBD), Markov chain model and Status Quo model. The article uses six online store datasets with annual revenues in the order of tens of millions of euros for the comparison. The EP/NBD model has outperformed other selected models in a majority of evaluation metrics and can be considered good and stable for non-contractual relations in online shopping. The implications for the deployment of selected CLV models in practice, as well as suggestions for future research, are also discussed.

Highlights

The segmentation of customers according to their customer lifetime value (CLV) enables companies to adequately build long-term relationships with customers and effectively manage investments into marketing tools
The second phase included the selection and justification of choice of CLV models suitable for use by e-commerce companies engaged in online shopping
This article aims to empirically compare the predictive ability and quality of selected CLV models on the basis of statistical metrics. This part presents the results of each model for every dataset by the selected performance metrics of Forecast vs Actual, mean absolute error (MAE) on a customer level, mean absolute percentage error (MAPE) on a weekly basis, and sensitivity for identifying 10% of the most profitable customers

Summary

Introduction

The segmentation of customers according to their customer lifetime value (CLV) enables companies to adequately build long-term relationships with customers and effectively manage investments into marketing tools. The high level of competition, especially in online shopping, drives companies to spend their financial resources on marketing activities as efficiently as possible, which can be helped by implementing a CLV model that uses available historical data to estimate customer value. In their effort to introduce CLV as a decision-making basis for marketing management, companies operating an online store face the issue of selecting the appropriate CLV model that would be suitable for their kind of business. This progress enabled a departure from the established patterns, such as brand equity, transaction and product centricity, and a shift towards a customer-centric approach in relationship management [21,22], in which the customer is a valuable intangible asset of the company [23,24,25,26]

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