Predicting online-purchasing behaviour

DOI10.1016/j.ejor.2004.04.022zbMath1064.90022OpenAlexW1964378344MaRDI QIDQ1780768

Wouter Buckinx, Dirk Van den Poel

Publication date: 13 June 2005

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2004.04.022

zbMATH Keywords

ClassificationForecastingMarketingE-commerceBusiness intelligenceCustomer relationship managementClickstream data

Mathematics Subject Classification ID

Consumer behavior, demand theory (91B42) Marketing, advertising (90B60)

Related Items (5)

Quantitative models for direct marketing: A review from systems perspective ⋮ CRM in social media: predicting increases in Facebook usage frequency ⋮ A novel method to investigate the effect of social network ``Hook images on purchasing prospects in e-commerce ⋮ E-fulfillment and multi-channel distribution - A review ⋮ IMPROVING PURCHASING BEHAVIOR PREDICTIONS BY DATA AUGMENTATION WITH SITUATIONAL VARIABLES

Uses Software

• SAS

Cites Work

• Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting
• Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers.
• Counting your customers: Compounding customer's in-store decisions, interpurchase time and repurchasing behavior
• Bayesian neural network learning for repeat purchase modelling in direct marketing
• Customer attrition analysis for financial services using proportional hazard models
• Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models
• Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
• Regressions by Leaps and Bounds
• Consistent Sum-Constrained Models

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