Price Discrimination and Inventory Allocation in Bertrand Competition
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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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".