To Each Their Own: Personalized Product Offerings in Competition
Bibliographic record
Abstract
We study competition between two firms that personalize product offerings to consumers. Firms have private, imperfect signals of each consumer’s ideal location and offer each consumer a different product without observing the competitor’s product offering. We characterize the equilibrium personalization strategy and examine how the accuracies of firms’ signals affect equilibrium strategy, profits, and consumer welfare. A firm generally charges a higher price for a more niche product and profits more from niche consumers unless its prediction accuracy is sufficiently lower than its competitor’s. When both firms have the same industry-level prediction accuracy, an increase in accuracy initially relaxes but later intensifies price competition for niche consumers, having the opposite effect on mainstream consumers. Interestingly, equilibrium profits also have an inverse-U shape in the prediction accuracy. A higher accuracy can also decrease welfare for mainstream consumers. When firms can endogenously invest in prediction accuracy, firms have incentives to overinvest in equilibrium, resulting in a prisoner’s dilemma. Privacy regulations that reduce predictive accuracy, including industry self-regulation, could improve profits and hurt consumer welfare by relaxing price competition. Our results remain robust under consumer search. The paper also discusses what happens if firms charge uniform pricing, if consumers’ ideal locations are distributed on the Salop circle, or if firms receive common signals, highlighting price discrimination between mainstream and niche consumers as the key driver of results. This paper was accepted by Dmitri Kuksov, marketing. Funding: J. Du is grateful for financial support from the Research Grants Council of Hong Kong [Grant GRF/17501823]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.02540 .
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".