Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Value of Internet Commerce to the Customer
Management Science
Bundling and Competition on the Internet
Marketing Science
Consumer Addressability and Customized Pricing
Marketing Science
Augmenting Conjoint Analysis to Estimate Consumer Reservation Price
Management Science
Measuring Heterogeneous Reservation Prices for Product Bundles
Marketing Science
Market-based recommendation: Agents that compete for consumer attention
ACM Transactions on Internet Technology (TOIT)
Bundling Information Goods of Decreasing Value
Management Science
Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming (International Series of Numerical Mathematics)
Revenue Management Through Dynamic Cross Selling in E-Commerce Retailing
Operations Research
Marketing Science
Promotion Effect on Endogenous Consumption
Marketing Science
Dynamic Pricing on the Internet: Importance and Implications for Consumer Behavior
International Journal of Electronic Commerce
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
Shopbot 2.0: Integrating recommendations and promotions with comparison shopping
Decision Support Systems
Optimal Pricing of Digital Experience Goods Under Piracy
Journal of Management Information Systems
Marketing Science
Limited Memory, Categorization, and Competition
Marketing Science
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Online retailing provides an opportunity for new pricing options that are not feasible in traditional retail settings. This paper proposes an interactive, dynamic pricing strategy from the perspective of customized bundling to derive savings for customers while maximizing profits for electronic retailers (“e-tailers”). Given product costs, posted prices, shipping fees, and customers' reservation prices, we propose a nonlinear mixed-integer programming model to increase e-tailers' profits by sequentially pricing customized bundles. The model is flexible in terms of the number and variety of products customers may choose to incorporate during the various stages of their online shopping. Our computational study suggests that the proposed model not only attracts more customers to purchase the discounted bundle but also noticeably increases profits for e-tailers. This online dynamic bundle pricing model is robust under various bundle sizes and scenarios. It improves e-tailer profit and customer savings the most when facing divergent views about product values, lower budgets, and higher cost ratios.