Product Returns, Customer Segmentation, and Dynamic Pricing in the Online Retail Market
Bibliographic record
Abstract
ABSTRACT Online retailers can adopt generous return policies to entice customers to buy and try new products. In this article, we focus on the learning aspect of product returns and show how an online retailer can utilize returns to segment customers based on their individual valuations of a product. We derive the optimal dynamic pricing strategy, including a potential fee for product returns (restocking fee), which balances the benefits from effective customer segmentation and the costs associated with product returns. Strategic customers, who understand that their return decisions affect future prices, may choose to return the product even when their valuations exceed the initial price. To curb strategic returns, which compromise the effective segmentation of customers, it is optimal for the retailer to reduce the initial price of the product and charge a higher fee for returns. We also identify conditions so that it is optimal for a retailer to overcharge customers for product returns (i.e., the return fee exceeds the actual cost of a return). This allows the retailer to extract surplus from customers who have low product valuations and return the product, and is therefore a form of price discrimination.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".