Price Discrimination and Inventory Allocation in Bertrand Competition
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Bibliographic record
Abstract
Problem definition: It is common practice for firms to deploy strategies based on customer segmentation (by clustering customers into different segments) and price discrimination (by offering different prices to different customer segments). Price discrimination, although seemingly beneficial, can hurt firms in competitive environments. Academic/practical relevance: It is thus critical for firms to understand when to engage in price discrimination and how to support discriminatory pricing practices with appropriate inventory management strategies. This paper tackles this overarching question through operational lenses by studying the joint impact of price discrimination and the allocation of limited inventory across customer segments. Methodology: We develop a Bertrand competition game featuring capacity restrictions, quality differentiation, and customer heterogeneity. Results: We characterize (pure- or mixed-strategy) Nash equilibria for a single-stage game reflecting uniform pricing and for a two-stage inventory-price game reflecting discriminatory pricing along with endogenous inventory allocation. Managerial implications: We identify three sources of market friction in price competition enabling firms to earn higher profits: capacity limitations, quality differentiation, and customer heterogeneity. Price discrimination eliminates the market frictions from customer heterogeneity, but strategic inventory allocation restores (or strengthens) the market frictions from capacity limitations. As such, price discrimination is only beneficial when combined with optimal inventory allocation across segments. We discuss relevant real-world examples featuring regional price discrimination along with strategic inventory allocation, including fast fashion and vaccines. Otherwise, uniform pricing may outperform discriminatory pricing. Our results thus underscore the critical role of inventory allocation in the design of competitive pricing strategies. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1146 .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it