Comparing Marketing Strategies for Cosmetics Between China and The U.S.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study presents a comparative study of promotional strategies in the cosmetic industry within two major global markets: The United States and China. They were chosen for this study because they are the two greatest e-commerce beauty markets in the world. The focus is on how digital transformation has reshaped these strategies, with a case study of L’Oréal providing practical insights. This topic is chosen for study due to the transformation of traditional marketing to digital marketing, especially with the emergence of social media marketing. The analysis reveals that while both markets have embraced digital marketing, there are significant differences in their approaches. In China, a more integrated strategy has emerged, leveraging E-commerce platforms to reach a broader consumer base and stimulate consumption. In contrast, the U.S. cosmetic market relies more on traditional promotional channels and offers fewer E-commerce discounts, with a slower adoption of live-streaming culture. However, U.S. firms continue to utilize digital channels to foster brand awareness, as demonstrated by L’Oréal's significant advertising expenditure and influencer collaborations. The study uses the literature review method with over three hundred of sources to synthesize and compare evidence in the U.S. market could potentially benefit from integrating more elements of the Chinese digital marketing strategy, particularly with respect to discounts and live-streaming. This study sheds light on the potential for adapting successful promotional strategies across diverse markets while acknowledging cultural and regional specificities. Despite its focus on top brands, future research could explore strategies employed by smaller firms and extend the comparison to other sectors.
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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 | 0.002 | 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it