Strategy for the marketing of the premium service of the Playa Pesquero resort hotel
Bibliographic record
Abstract
Marketing luxury hotels is relevant to meet the growing demands of customers, in this, the Premium services are essential to maintain the planned revenue. The research defined as scientific problem the following: The insufficient commercialization from the strategic of the Premium service, limits the income of this service in the hotel Playa Pesquero. The general objective was to: Develop a strategy for the marketing of the Premium service, which contributes to increasing the income of this service in the Playa Pesquero hotel. Several theoretical methods were used, such as analysis and synthesis, inductivedeductive, structural systemic, and among the empirical ones: scientific observation, interview, review of documents and the criterion of specialists. The strategy for marketing the Premium service was structured in four essential components: objectives, phases, tasks, and techniques to be employed. The partial application of the strategy has achieved among its results the following: design of the profile of target markets: Canada, United Kingdom, Germany and Italy; the updating of strategic objectives; the design of the product, price, distribution and promotion strategies; and an action plan based on the strategies outlined, all of which contributed to the increase of the total revenues of the Premium service.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".