Online-Exclusive or Hybrid? Channel Merchandising Strategies for Ship-to-Store Implementation
Bibliographic record
Abstract
We study how merchandising products as online-exclusive (i.e., products available only online) versus hybrid (i.e., products available both online and offline) can improve the performance of ship-to-store (STS) services, an omnichannel retail fulfillment initiative that allows customers to pick up their online orders in-store. First, using a stylized model, we theoretically demonstrate that although STS is likely to increase sales, it may also entail the risk of losing some customers by exposing them to alternative products at nearby competitors during in-store pickup visits. Online-exclusive products and hybrid products are subject to this tradeoff at different degrees. To minimize the risk of STS, we theoretically propose a channel merchandising strategy for the STS implementation. Next, we empirically test our theoretical predictions using data from an omnichannel retailer that launched the STS functionality. We also conduct an empirical counterfactual analysis to quantify the benefits of our proposed channel merchandising strategy. Overall, our theoretical model coupled with the empirical analysis suggests that to improve the performance of STS implementation, an omnichannel retailer should offer (i) products that are somewhat generic, low-priced, and with high in-store availability as online-exclusive and (ii) products that are somewhat unique, high-priced, and with low in-store availability as hybrid. The counterfactual analysis reveals that the proposed channel merchandising strategy can improve STS performance by increasing overall retail sales by another 2.7% for the focal retailer. This paper was accepted by Vishal Gaur, operations management.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".