Selling through Priceline? On the impact of name-your-own-price in competitive market
Bibliographic record
Abstract
Priceline.com patented the innovative pricing strategy, Name-Your-Own-Price (NYOP), that sells opaque products through customer-driven pricing. In this article, we study how competitive sellers with substitutable, non-replenishable goods may sell their products (i) as regular goods, through a direct channel at posted prices, and possibly at the same time (ii) as opaque goods, through a third-party channel that engages in NYOP. We establish a stylized model framework that incorporates three sets of stakeholders: two competing sellers, an intermediary NYOP firm, and a sequence of customers. We first characterize customers’ optimal purchasing/bidding decisions under various channel structures and then analyze corresponding sellers’ dynamic pricing equilibrium. We conduct extensive numerical studies to illustrate the impact of inventory and time on equilibrium prices, expected profit, and channel strategies. We find that the implications are highly dependent on channel structure (dual versus single). In particular, more inventory may reduce one’s expected profit under the dual structure, whereas this never happens when a seller only uses the direct channel. Interestingly, although competing sellers seldom benefit from the existence of NYOP channels, it is possible that one or both of the sellers adopt it in equilibrium. We identify timing, inventory levels, and channel opaqueness as key drivers for NYOP adoption and characterize equilibrium areas for each type of channel structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".