Assessing the Consequences of a Channel Switch
Bibliographic record
Abstract
Switching marketing channels is an expensive and sticky decision. While a number of theories suggest efficiency and strategic differences between channels, there is virtually no work on combining these ideas into an empirically workable methodology to assess the impact of a channel switch. In this study, we undertake to close this gap with an empirical study of the sports drink market, featuring competing producers and heterogeneous channels. We estimate demand and cost parameters for a number of alternative models of competitive interaction and use these estimates to study the switching of Gatorade from its extant (independent wholesaler) channel to the direct store delivery (DSD) channel belonging to Pepsi. Our initial results indicate the following: Pepsi should switch Gatorade to the DSD channel only if (i) the switch decreases Gatorade's manufacturing cost by at least 14%, or (ii) the switch increases the share of profit it can obtain by at least 13%, or (iii) the switch enhances demand by the equivalent of a price cut of 4.96¢ for a 32-ounces package. Absent these increases, Pepsi should not switch. Our methodology and results speak to both managers contemplating a channel switch and antitrust authorities faced with the task of evaluating the consequences of a change in vertical structure.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".