Et si le marketing devenait moins segmenté et plus inclusif ?
Bibliographic record
Abstract
La segmentation permet de regrouper les consommateurs selon des critères précis afin de mieux les cibler. Cette réflexion montre comment deux exemples de segmentation, concernant les individus âgés de plus de 50 ans et les femmes ayant un surplus de poids, peuvent conduire des entreprises à négliger ou à ostraciser des marchés rentables de consommateurs. Afin d’éviter ce genre de situation, les entreprises doivent retourner aux sources du marketing en se posant la question : « À quels besoins du consommateur notre campagne de marketing répond-elle ? » Cela mènera à des campagnes plus inclusives qui tenteront de satisfaire les besoins fondamentaux de toutes les personnes, à savoir les besoins d’identité, de relation, d’utilité, d’adaptation et d’énergie.
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 | 0.001 |
| 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 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".