Counting your customers: Compounding customer’s in-store decisions, interpurchase time and repurchasing behavior

Wu, Chih-Wei; Chen, Hsiu-Li

doi:10.1016/s0377-2217(99)00326-4

Cited by 39 publications

(12 citation statements)

References 19 publications

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“…First, we include ÔRecencyÕ, which represents the number of days that passed between the last transaction and the end of our observation period. Customers who recently purchased are more likely to be active than customers who shopped a long time ago (Wu and Chen, 2000). Most previous studies find that the lower the value of recency, the higher the probability that a customer stays loyal.…”

Section: Interpurchase Time and Related Inputsmentioning

confidence: 99%

See 1 more Smart Citation

Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

Buckinx

Poel

2005

European Journal of Operational Research

329

187

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Section: Interpurchase Time and Related Inputsmentioning

confidence: 99%

“…The first criterion provides an indication of a customerÕs loyalty (Wu and Chen, 2000) and potential profitability. The second attribute ensures that the time between customer visits is regular.…”

Section: Behaviourally Loyal Clientsmentioning

confidence: 99%

Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

Buckinx

Poel

2005

European Journal of Operational Research

329

187

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“…For instance, to the best of our knowledge, this is only the second empirical validation of the Pareto/NBD model -the first being Schmittlein and Peterson (1994). (Other researchers (e.g., Reinartz andKumar 2000, 2003;Wu and Chen 2000) have employed the model extensively, but do not report on its performance in a holdout period.) We find that both models yield very accurate forecasts of future purchasing, both at the aggregate level as well as at the level of the individual (conditional on past purchasing).…”

Section: Discussionmentioning

confidence: 99%

“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model

Fader

Hardie

Lee³

2005

Marketing Science

349

237

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"Counting Your Customers" the Easy Way: An Alternative to the Pareto/NBD ModelToday's managers are very interested in predicting the future purchasing patterns of their customers, which can then serve as an input into "lifetime value" calculations. Among the models that provide such capabilities, the Pareto/NBD "Counting Your Customers" framework proposed by Schmittlein, Morrison, and Colombo (1987) is highly regarded. But despite the respect it has earned, it has proven to be a difficult model to implement, particularly because of computational challenges associated with parameter estimation.We develop a new model, the beta-geometric/NBD (BG/NBD), which represents a slight variation in the behavioral "story" associated with the Pareto/NBD, but it is vastly easier to implement. We show, for instance, how its parameters can be obtained quite easily in Microsoft Excel. The two models yield very similar results in a wide variety of purchasing environments, leading us to suggest that the BG/NBD could be viewed as an attractive alternative to the Pareto/NBD in most applications.

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“…For example, Moe and Fader (2004) and Wu and Chen (2000) argued that the historical purchasing behavior can strongly influence the future purchasing behavior in ecommerce. Lemon et al (2002) argued that the frequency of past purchases is positively correlated to the possibility of future purchasing.…”

Section: Purchase Behaviormentioning

confidence: 99%

Online-Purchasing Behavior Forecasting with a Firefly Algorithm-based SVM Model Considering Shopping Cart Use

Tang

Wang

et al. 2017

EURASIA J MATH SCI T

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Due to the complexity of the e-commerce system, a hybrid model for onlinepurchasing behavior forecasting is developed to predict whether or not a customer makes a purchase during the next visit to the online store based on the previous behaviors, i.e., online-purchasing behavior. The proposed model makes contributions to literature from two perspectives: (1) a classification model is proposed based on the "hybrid modeling" concept, in which an effective artificial intelligence (AI) technique of support vector machine (SVM) is employed for classification forecasting and further extended by introducing the promising AI optimization tool of firefly algorithm (FA), to solve the crucial but tough task of parameters selection, i.e., the FA-based SVM model; (2) an appropriate predictor set is carefully designed especially considering online shopping cart use which was otherwise neglected in existing models, apart from other common online behaviors, e.g., clickstream behavior, previous purchase behavior and customer heterogeneity. To verify the superiority of the proposed model, an online furniture store is focused on as study sample, and the empirical results statistically support that the proposed FA-based SVM model considering online shopping cart use significantly beat all benchmarking models (with other popular classification methods and/or different predictor sets) in terms of prediction accuracy.

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Counting your customers: Compounding customer’s in-store decisions, interpurchase time and repurchasing behavior

Cited by 39 publications

References 19 publications

Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model

Online-Purchasing Behavior Forecasting with a Firefly Algorithm-based SVM Model Considering Shopping Cart Use

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