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Record W4211138116 · doi:10.54691/bcpbm.v16i.272

The Analysis of Louis Vuitton’s Marketing Strategy in China Based on the 4P Model and Brand Marketing

2021· article· en· W4211138116 on OpenAlex
Jingyi Wang, Lushan Yu

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBCP Business & Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMarketingChinaAdvertisingBusinessMarketing mixMarketing strategyStrengths and weaknessesMarketing researchBrand strategyMarketing managementPolitical sciencePsychology

Abstract

fetched live from OpenAlex

China, as an emerging market in the global luxury industry, has drawn a lot of attention from luxury brands. However, many traditional luxury brands have not achieved the desired benefits in this broad market of China. This study adopts a qualitative research approach of case study, analyzes the strengths and weaknesses of Louis Vuitton's marketing mix strategy and brand marketing strategy in recent years. The study shows that Louis Vuitton's marketing efforts in China have been effective in recent years. However, its marketing strategies could be improved in terms of choosing celebrity endorsements, expanding marketing channels, increasing exposure, and balancing tradition with innovation. This article hopes to provide some marketing ideas for luxury brands that want to develop better in the Chinese market.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.238
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it