Is Personalized Pricing Profitable When Firms Can Differentiate?
Bibliographic record
Abstract
We consider the role of personalized pricing (PP) on product differentiation when PP is costly to implement. Using a stylized yet commonly used formulation, we find that when firms decide on positioning before deciding on PP implementation, PP implementation cost affects not only the amount of differentiation firms choose in their positioning, firm profits, consumer surplus, and social welfare, but also whether firms implement PP. When PP implementation cost is low, firms cannot help but to implement PP and engage in direct price competition. Moreover, firms implementing PP reduce their differentiation, further intensifying price competition, and are worse off. When PP implementation cost is moderate, firms position to reduce their differentiation to commit to not implementing PP, again aggravating price competition. In contrast, when PP implementation cost is higher, firms increase their differentiation due to the threat of PP but do not implement PP. As a result, the availability of PP improves firm profits, even though firms do not implement PP. However, if differentiation is restricted, then PP availability cannot improve firm profits. If an information seller sets the PP implementation cost, then it sets the cost low. Consequently, firms implement PP and are worse off. We also find that when firms decide whether to implement PP before deciding on positioning, they never implement PP. This is the case when PP implementation is complex, and differentiation can be affected by short-run advertising and promotion. Finally, we show that banning PP can benefit consumers when accounting for changes in firm positioning. This paper was accepted by D. J. Wu, information systems. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada and the Hong Kong Research Grants Council [Grant 21500920]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.02740 .
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".