The Marketing Model of Chinese Warehouse Retailers under the New Retail Background
Bibliographic record
Abstract
New retailing refers to companies relying on the Internet to upgrade and transform the production, circulation, and sales processes of commodities through the use of advanced technology such as big data and artificial intelligence, thereby reshaping the business structure and ecosystem, and providing online services.It is a new retail model that integrates offline experience and modern logistics deeply.New media marketing is an increasingly vigorous marketing model, and more and more industries have begun to participate in it, including warehouse supermarkets.This article will start from this point and discuss how warehouse supermarkets can use new media to market under the background of new retail.This study found that warehouse supermarkets can increase their awareness and sales in the short term through influencers, but this approach does not provide the company with long-term sustainability.To avoid the possible negative effects of influencer marketing, the company inevitably needs to establish brand image and customer loyalty.Underlying the rapid development of China's e-commerce, it can be possible to conduct more research in the future on how to conduct new media marketing while also establishing a brand image faster and ensuring customer retention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".