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Record W7160364018

Botanee: Leveraging Multi-touchpoint Marketing to Build a Strong Chinese Brand in the Digital Age

2023· other· W7160364018 on OpenAlexaff
Yajin 王雅瑾, 曹之静

Bibliographic record

VenueCEIBS Institutional Repository · 2023
Typeother
Language
Field
Topic
Canadian institutionsCentre Casa
Fundersnot available
KeywordsDigital marketingThe InternetQuality (philosophy)Online advertisingContext (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

近年来,随着消费者的需求越来越个性化、多样化,越来越多的新消费品牌开始涌现在市场上。但近几年的行业表现证明,只是通过资本加持、外包生产、过度营销等手段,品牌很难获得长期的可持续发展。本案例选择专注敏感肌肤的功效性护肤品牌薇诺娜及其公司贝泰妮作为对象,描述了薇诺娜从创立以来是如何通过产品创新,渠道布局和数字时代多触点的管理来切入敏感性肌肤这个细分市场的,又是如何一步步拓展市场并成为国内的护肤品领军品牌的。 从2018年开始,薇诺娜连续多年入围天猫美容护肤类目榜单前十,也是唯一连续上榜的中国品牌。 展望未来,贝泰妮还面临着很多问题。薇诺娜目前占据公司营收的99%以上,如何才能做好品牌拓展、覆盖更多的客群?薇诺娜旗下的明星单品特护霜一直是品牌收入的重要贡献来源,未来公司如何打造第二款“大单品”? 随着公司规模扩大、触点不断增多,人员结构越来越复杂,如何做好内部的品牌管理?

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.005
Scholarly communication0.0070.010
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.002

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 head0.020
GPT teacher head0.269
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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