Bibliographic record
Abstract
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".