Événements et spectacles sportifs : état des lieux des stratégies de segmentation
Bibliographic record
Abstract
La segmentation représente un aspect important des stratégies de marketing mises en place par la plupart des organisations. Le marché du sport professionnel et les nombreuses franchises en activité ne font pas exception. Les marques qui composent ce marché singulier à fort contenu émotionnel, où gravitent des consommateurs aux caractéristiques particulières, doivent apporter une attention spéciale à la segmentation si elles veulent connaître du succès, et ce, malgré la relative absence d’une tradition stratégique en ce sens. Cet article vise un double objectif : rappeler l’importance de la segmentation dans le contexte du marketing sportif, en illustrant différentes pratiques, et présenter des approches de segmentation différentes, en mettant l’accent sur une utilisation accrue des motivations des spectateurs et sur leur perception de la personnalité des marques sportives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".