Bibliographic record
Abstract
Vertiv是全球领先的数字基础设施技术提供商，中国分公司维谛技术主要为数据中心等客户提供不间断电源产品和工业空调等产品和服务。维谛的营销部门在2020年开展了数字营销探索，力求在品牌建设和销售方面做出成绩。在通过数字营销收集到线索后，早期维谛是将线索输入到公司的客户关系管理系统（VSCP），由销售团队来决定线索是否值得跟进。结果发现，数字营销带来的线索通常金额较小，销售团队没有动力跟进，导致线索大量流失。后期，营销团队发现公司销售渠道里的流量型分销商更加重视这种线索，因为他们承担了压货责任，对能带来销售的线索非常感兴趣。营销部门通过线索的正确分配有效提升了数字营销的转化率。2022年，公司为更好地提升数字营销转化率，自建了智享空间用于服务流量型渠道客户。经过一段时间的运行，智享空间的线索转化率超过12%，远远超过公司在百度上的转化率（1.5%）。从维谛整体的销售来看，公司仍以大客户销售为主（占公司55%），流量型业务仅占5%。数字营销在销售上的效果主要体现在流量型业务。 维谛认为数字营销在品牌建设和销售两方面都发挥作用，但是仅从销售来看，数字营销带来的销售额在公司占比仍然较小。在有限的营销预算下，维谛营销总监田军也在思考未来数字营销应该如何投放？目前的做法还有哪些环节需要进一步改善？
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.079 |
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; both teacher heads agree on what is shown here.
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".