Managing returns by selling and pricing strategies with online product reviews
Bibliographic record
Abstract
A full-refund return policy is intended to enhance customer satisfaction but can lead to financial losses when products fail to match customer preferences. Online reviews can help mitigate these mismatches by providing critical product information and enabling more informed purchase decisions. This paper develops a game-theoretic pricing model for an online retailer managing returns in the presence of reviews, integrating pre-purchase risk (review informativeness) with post-purchase risk (return hassle cost). Our analysis yields several key findings. First, the seller can enhance profitability and reduce unnecessary returns by strategically setting selling strategies and prices when the hassle costs of returns are moderate. Second, online reviews play a significant role in shaping these strategies by creating distinct market segments based on product fit signals. The seller lowers the price to attract all customer segments but raises it for those who perceive the product as a good fit. Third, a seller with high product-market fit can maximize profit by managing both pre- and post-purchase risks, whereas a seller with low product-market fit can attract a broader customer base by simplifying returns and leveraging informational ambiguity. A “beneficial zone” for the seller emerges when reviews are highly informative, return hassle costs are moderate, and product-market fit is strong. Finally, the study shows that online reviews generally enhance consumer surplus and, under certain conditions, improve social welfare, particularly when mismatched products have low social value and return hassle costs are moderate. These insights help guide the seller in balancing profitability, customer satisfaction, and consumer surplus.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".