Optimal non-exclusive trade-in rebates design for a profit-maximizing company
Bibliographic record
Abstract
The trade-in program is a common marketing strategy adopted by companies to encourage customers to return their used products in exchange for a discount or rebate on the purchase of new items. A trade-in program can significantly boost sales by lowering customers’ purchasing costs while enabling the collection of returned products for recycling or remanufacturing. However, it also introduces additional operational and financial costs. Therefore, the company must carefully balance profit gains with associated costs to ensure that the trade-in program remains economically viable. This paper explores the company’s optimal non-exclusive trade-in program, under which both existing customers and competitors’ customers can trade their on-hand products for new ones. The innovation level of new generation products, the previous pricing strategy of the company and its competitor, the durability of the products, and the relative perceived value of the old generation products by customers are considered in the paper. The results show that the optimal trade-in strategy design depends on the previous pricing strategy, and setting high trade-in rebates is not always optimal to maximize profit. Moreover, whether the company should provide a higher trade-in rebate to its existing customers depends on the customers’ perceived value of the company’s past products compared to competitors’ offerings, along with both parties’ past pricing strategies, and the trade-in program does not always benefit the environment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".