Notice bibliographique
Résumé
A local perspective iIt was September 29, 2016 and Marta Quelhas, head of the Personal Care Marketing Team of Unilever Portugal and Gonçalo Bernardes, Marketing Director, were preparing for a conference call with the Global Marketing Team.The conversation was going to be about Dove Real Beauty Beats, a campaign that Unilever Portugal had launched 1 week earlier.Based on local research the Portuguese team had come to the conclusion that there were particular consumer insights that the Dove global campaigns were not addressing.Dove Beauty Beats video was designed as part of an Integrated Marketing Campaign to specifically target Portuguese consumers.First the video was launched using paid media, but soon it was clear that people were sharing and commenting and its spread did not need investment backing it up: it was reaching the entire planet organically.This conversation was expected to be challenging as traditionally the Global Office was responsible for developing master campaigns with no particular category or product and Local teams would then adapt it to local markets.Only one week had passed after the launch of the campaign and it was clear that results exceeded the expected outcome as the video had gone viral, crossed borders and it had gained a life on its own.Several countries, such as Canada, had contacted the Global Office asking to use Dove Beauty Beats so Marta and Gonçalo needed to decide on the next steps.This conference call was supposed to clear the doubts around the process, discuss results and make the following decision: should the Portuguese local office continue with such isolated initiatives or should they never repeat this again? BackgroundUnilever, the company that owned Dove, a beauty care brand, is an Anglo-Dutch multinational consumer goods company founded in the 1880's.It is the third largest FMCG company in the world and one of the oldest.It is present in around 190 countries and its products are present in 7 out of 10 households globally.2 billion consumers used Unilever's products every day.Its revenues were over 50 billion euros in 2016.Unilever owns around 400 brands, 13 of them with revenues of over 1 billion euros a year: Knorr, Skip, Dove, Lipton and Becel, to name a few (for the complete list see Exhibit 1).The mission of Unilever was "accelerating growth in the business, while reducing environmental footprint and increasing the positive impact". 1 In Portugal, the first brands of Unilever entered the national market around 1926, with Jerónimo Martins Distribution.ii 2 In 1949, both companies formed a joint venture, with Jerónimo Martins i Ana Amaral, Part-Time Lisbon MBA graduate, prepared this case under the supervision of Jorge Velosa, Marketing Professor at the Lisbon MBA, solely as the basis for class discussion.This case is not intended to serve as endorsement, source of primary data or illustrations of effective or ineffective management.We thank Unilever for their cooperation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,012 | 0,005 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».