Deterministic and stochastic Customer Lifetime Value models. Evaluating the impact of ignored heterogeneity in non-contractual contexts

Abstract

. His preferred subjects are marketing decision support models and systems, interactive marketing, and customer relationship management.ABSTRACT This article presents a panorama of Customer Lifetime Value (CLV) modelling and focuses on the two main categories of CLV calculation methods: the deterministic approach and the stochastic approach. The fi rst adopts simplifi ed calculations that ignore heterogeneity in customers ' retention and / or churn rates within a cohort. It produces formulae that can be easily used by managers and solves a greater number of managerial problems. The second approach brings much more precision to CLV calculations by thoroughly considering retention and / or churn rate heterogeneity, but measures them as actuarial variables and not as responses to marketing effort. We derive formulae to compute expected residual transactions conditional on customer purchase profi les for deterministic models in non-contractual contexts, and suggest solutions to evaluate forecasting error as compared to stochastic models.

Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values

“…21 Most studies mainly distinguish between deterministic and probabilistic models, making a point of the former being more suitable for individual calculations, while the latter are more adequate for estimating CLV at the cohort level, because they take into account the heterogeneity of the customer base as a whole. 22 Aside from the modeling approach, the customer base is generally regarded as having two dimensions, the type of contract and transaction occasions. The first dimension describes the relationship with the customer, which is either con- tractual or non-contractual.…”

“…11 When CLV is computed, it is often assumed that the customer base is homogeneous, which has been shown to be invalid. 22,23 Although most studies focus on estimating the mean value of CLV it is widely acknowledged in the literature that the variance of CLV is more important. 12,24 To account for this McCarthy et al proposed a novel way to derive, predict and validate the variance of CLV using a combination of stochastic models.…”

A marketing view of the customer value: Customer lifetime value and customer equity

“…We have developed a classification of CLV and CE models by combining and updating several criteria taken into account by previous reviews about this topic (see for example Calciu (2009), Fader and Hardie ( 2009), , Kumar and George (2007) and Villanueva and Hanssens (2007)). Therefore, we offer a global and integral view of CLV-CE models that serves as a guide with key requirements for developing these types of models.…”

“…Deterministic or stochastic analysis? (Calciu, 2009;Villanueva & Hanssens, 2007 identify six types of models that researches have usually used to examine CLV components (acquisition, retention and expansion or cross-selling). These models are: RFM models, probability models, econometric models, persistence models (multivariate time series analysis), computer science models (data mining, machine learning and nonparametric statistics) and growth and diffusion models.…”

Measuring Customer Equity in Noncontractual Settings Using a Diffusion Model: An Empirical Study of Mobile Payments Aggregator

“…For the former, the average customer lifetime value is calculated through financial data and is multiplied by the number of customers to obtain customer equity. As no individual customer lifetime value is involved in this top-down approach, it is also called an aggregate-level approach [3,9,25,26]. For the latter, the lifetime value of each customer is calculated based on individual transaction data.…”