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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.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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