Bibliographic record
Abstract
近几年，新消费品牌浪潮降温，聚光灯再次回到“老品牌”身上。越来越多人意识到：与从0到1相比，如何从1到100并保持基业长青，才是一个更值得研究和学习的课题。 本案例描述了欧莱雅集团（简称“欧莱雅”）在中国市场上的品牌战略演进过程。欧莱雅成立于1909年，以一款染发剂为起点，经过一系列地外延收购，逐步扩大其品牌阵容，成为全球最大的化妆品集团。该集团目前旗下拥有500多个化妆品品牌，产品线覆盖染发、护肤、彩妆、香水等多个领域。自1996年起，欧莱雅开始将“兰蔻”“卡尼尔”等数十个定位各异的品牌引入中国市场。在中国的高端化妆品市场中，欧莱雅取得了显著成绩。然而，在更具市场份额的大众护肤市场上，欧莱雅引进的品牌却始终未能占据主导地位。为进一步拓展其在中国大众护肤市场的份额，2004年欧莱雅收购了“小护士”“羽西”等中国著名的本土品牌。遗憾的是，这些品牌在被收购后并未实现预期的成功，反而逐渐淡出了公众的视野。到了2022年，欧莱雅在中国成立了一家投资公司，采取股权投资的方式，与本土品牌展开深度合作。 通过欧莱雅集团在中国市场从引入旗下品牌，到收购本土品牌，再到与本土品牌进行股权投资合作的演变过程，我们可以清晰地看到其品牌战略的逐步演进。每一步战略调整的背后，都是对市场需求的深入洞察和对前一战略实践的反思与总结，以及在面临挑战时不断寻求创新的努力。在本案例中，我们将深入探讨欧莱雅在实施其品牌战略过程中遇到了哪些挑战，又是何种原因推动了每一次战略调整。
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".