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Sequential Pricing of Multiple Products: Leveraging Revealed Preferences of Retail Customers Online and with Auto-ID Technologies

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  • John Aloysius

    (Information Systems, University of Arkansas, Fayetteville, Arkansas 72701)

  • Cary Deck

    (Sam M. Walton College of Business, University of Arkansas, Fayetteville, Arkansas 72701)

  • Amy Farmer

    (Sam M. Walton College of Business, University of Arkansas, Fayetteville, Arkansas 72701)

Abstract

Technological advances enable sellers to price discriminate based on a customer's revealed purchasing intentions. E-tailers can track items in online shopping carts and radio frequency identification tags enable retailers to do the same in brick-and-mortar stores. To leverage this information, it is important to understand how this new visibility impacts pricing and market outcomes. We propose a model in which a seller sets prices for goods A and B , allowing for the possibility of sequentially revising the price for good B if the buyer reveals a preference for good A by making an initial purchase decision. We derive comparative statics results for the prices of products that have superadditive or subadditive values, and also for the associated profits. We also run simulations for a range of distributions of buyer values, to compare sequential pricing with mixed bundling. The results indicate that information technology-enabled sequential pricing can increase profits relative to mixed bundling or pure components pricing for substitute goods due to a reduction of intraseller competition. We also consider the case of goods with positively or negatively correlated values and find that when sellers can condition the second good's price on the buyer's decision to purchase the first good, sequential pricing increases profits when customer's values for the goods are highly positively correlated.

Suggested Citation

  • John Aloysius & Cary Deck & Amy Farmer, 2013. "Sequential Pricing of Multiple Products: Leveraging Revealed Preferences of Retail Customers Online and with Auto-ID Technologies," Information Systems Research, INFORMS, vol. 24(2), pages 372-393, June.
    • Handle: RePEc:inm:orisre:v:24:y:2013:i:2:p:372-393
    DOI: 10.1287/isre.1120.0440
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    4. Adhikari, Arnab & Sharma, Megha & Basu, Sumanta & Jha, Ashish Kumar, 2022. "Uniform or spatially differentiated? Pricing Strategies for Information Goods under simultaneous and sequential decision-making in multi-market context," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).

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