Modeling and Application of Customer Lifetime Value in Online Retail

Jasek, Pavel; Vrana, Lenka; Sperkova, Lucie; Smutný, Zdeněk; Kobulsky, Marek

doi:10.3390/informatics5010002

Cited by 28 publications

(23 citation statements)

References 60 publications

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“…The status quo model was added to represent the naïve approach to calculating CLV, and to make the other individual approaches easier to compare. Nevertheless, the chosen models satisfy the following conditions of suitability, which are related to the purchase environment and the customer relationships typical of e-commerce (Jasek et al, 2018):  Non-contractual relation: Customers are not contractually bound, and it is only up to them whether and when they make a purchase from the given retailer.…”

Section: Selection Of Models For Comparison and Their Descriptionmentioning

confidence: 99%

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Predictive Performance of Customer Lifetime Value Models in E-Commerce and the Use of Non-Financial Data

Jasek¹,

Vrana²,

Sperkova³

et al. 2019

Prague Economic Papers

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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.

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Section: Selection Of Models For Comparison and Their Descriptionmentioning

confidence: 99%

“…The second reason is a limited ability of these companies to process large amounts of internal and external data into the form usable by the selected model. This research methodically builds on our previous research presented in Jasek et al, 2018. The remainder of the article proceeds as follows.…”

Section: Introductionmentioning

confidence: 99%

Predictive Performance of Customer Lifetime Value Models in E-Commerce and the Use of Non-Financial Data

Jasek¹,

Vrana²,

Sperkova³

et al. 2019

Prague Economic Papers

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is no study addressing all the models entirely. In previous research studies (Jasek, Vrana, Sperkova, Smutny, & Kobulsky, 2018, 2019, the authors focused on representative models of different model families suitable in an e-commerce environment and their evaluation and comparison. The authors also stated that the results of Markov chain model and Vector autoregressive model which use additional non-financial data about customer behaviour (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Often mentioned is the division of CLV models into contractual, semi-contractual and noncontractual relations between customer and companies (Estrella-Ramón et al, 2013). In the case of online shopping environment, CLV models should consider the non-contractual relation of the B2C relationship, non-membership of the customers in the relationship, alwaysa-share factor, continuity of the purchases and variable-spending environment (see Jasek et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Selected Probabilistic Customer Lifetime Value Models in Online Shopping

Jasek

Vrana

Sperkova

et al. 2019

Journal of Business Economics and Management

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The selection of a suitable customer lifetime value (CLV) model is a key issue for companies that are introducing a CLV managerial approach in their online B2C relationship stores. The online retail environment places CLV models on several specific assumptions, e.g. non-contractual relationship, continuous purchase anytime, variable-spending environment. The article focuses on empirical statistical analysis and predictive abilities of selected probabilistic CLV models that show very good results in an online retail environment compared to different model families. For comparison, eleven CLV models were selected. The comparison has been made to the online stores’ datasets from Central and Eastern Europe with annual revenues of hundreds of millions of euros and with almost 2.3 million customers. Probabilistic models have achieved overall good and consistent results on the majority of the studied transactional datasets, with BG/NBD and Pareto/NBD models that can be considered stable with significant lifts from the baseline Status quo model. Abe's variant of Pareto/NBD have underperformed multiple criterions and would not be fully useful for the studied datasets without further improvements. In the end, the authors discuss the deployment implications of selected CLV models and propose further issues for future research to address.

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Business Analytics in Customer Lifetime Value: An Overview Analysis

Dogan,

Hiziroglu,

Pisirgen

et al. 2024

WIREs Data Min & Knowl

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In customer‐oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision‐making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state‐of‐the‐art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan—one of the study's significant contributions—was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.

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Modeling and Application of Customer Lifetime Value in Online Retail

Cited by 28 publications

References 60 publications

Predictive Performance of Customer Lifetime Value Models in E-Commerce and the Use of Non-Financial Data

Predictive Performance of Customer Lifetime Value Models in E-Commerce and the Use of Non-Financial Data

Comparative Analysis of Selected Probabilistic Customer Lifetime Value Models in Online Shopping

Business Analytics in Customer Lifetime Value: An Overview Analysis

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