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Record W7131999844

Tyrex: Improve Customer Acquisition with Data Insight

2020· other· W7131999844 on OpenAlexaff
Chen Lin, Chi Zhang

Bibliographic record

VenueCEIBS Institutional Repository · 2020
Typeother
Language
Field
Topic
Canadian institutionsCentre Casa
Fundersnot available
KeywordsData acquisitionData collectionKnowledge acquisitionQuality (philosophy)Key (lock)Customer intelligence
DOInot available

Abstract

fetched live from OpenAlex

专注于户外专家和都市精英群体的高端户外运动品牌始越公司自进入中国市场以来虽然稳步发展,但仍然比较“小众”。考虑到直营电商增速远超大盘,始越公司计划发力直营电商使业绩有所突破。2018年,始越公司电商销售目标为5,500万,增长比例高达49%;而营销费用仍维持为337万,费比7%。而且,始越公司一直执行高客单、全价销售策略,实现这个目标显然充满挑战。 到了2018年9月,作为电商渠道负责人的周旭开始有些焦虑:前三季度,始越公司电商业绩的整体达成率仅为75%。周旭明白唯有提效,尤其是提高电商获客的效率才是唯一可行的道路。如何利用大数据技术和数字化手段持续改进电商投放策略呢?

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)0.012
metaresearch head score (Gemma)0.036
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 score0.042
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0130.013
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0420.011

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 head0.022
GPT teacher head0.246
Teacher spread0.225 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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