Supporting New Product or Service Introductions: Location, Marketing, and Word of Mouth
Bibliographic record
Abstract
Introduction of a new, innovative product or service is a fundamental problem that managers face regularly. The temporal sales pattern of such a product is often dynamically influenced by word of mouth as well as by marketing and distribution support. Appropriate marketing support strategies must be specified to induce the best sales pattern; however, the success of these strategies is heavily tied to the accessibility of the retail facilities, whether physical stores or virtual ones such as the Internet or phone. Managing the relation between accessibility and marketing support becomes more challenging when the firm faces a limited time, often due to short product life cycle. In this work, we present a general model for the joint design of the network of retail facilities and marketing strategies in the presence of word-of-mouth effects and limited time horizon. We develop exact and heuristic solution methods and provide insights on the structure of the optimal solution. Our solution methods identify the number and location of retail facilities to carry the product, as well as the proper mix of marketing channels and expenditures in them over time. Our results demonstrate that significant profit improvement can be achievable by jointly optimizing the design of the network of retail facilities with the choice of marketing strategies. Results of numerical experiments and an illustrative case study on opening Nespresso boutiques are also reported.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".