Selling Your Product Through Competitors’ Outlets: Channel Strategy When Consumers Comparison Shop
Bibliographic record
Abstract
This paper develops a new rationale for decentralization in distribution channels: providing a one-stop comparison shopping experience for consumers. In our duopoly model, when consumers are knowledgeable about their brand preferences, each manufacturer would distribute through its own vertically integrated retail outlets only. When some consumers are unsure about their brand preferences, however, it may be optimal for one of the manufacturers to also distribute through its competitor’s outlets. The resulting equilibrium has several interesting properties. First, only one of the manufacturers chooses to add competitor-outlet distribution, not both—even when the manufacturers are symmetric. Second, the manufacturer distributing through its competitor’s outlets also distributes through its own outlets, i.e., its distribution strategy is a hybrid strategy, combining vertical integration and decentralization. Third, when the manufacturers’ brands are asymmetric, it is the weaker brand that has a stronger incentive to pursue hybrid distribution. Fourth, the competitor’s outlets in question welcome the new brand, even when no consumer would actually buy the new brand—a case of pure showrooming. These results highlight the linkages between distribution strategy, shopping efficiency, and retail formats. Shopping costs and consumers’ uncertainty about their own brand preferences create a demand for multibrand retailing, and in pursuing this demand, manufacturers may eschew the efficiency advantages of vertical integration in favor of hybrid distribution. However, the fact that only one of the manufacturers chooses to do so suggests that this strategy also has weaknesses, which we discuss in the paper.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".