The Influencing Factors Model and Scale Designing of RMB Financing Products' Marketing Segmentation from the Perspective of Niche
Bibliographic record
Abstract
First, there is an analysis on the present marketing segmentation of RMB financing products. Then, this paper explores whether the theory of niche has any application on the marketing segmentation of RMB financing products from the perspective of niche. Based on this, the influencing factors model of RMB financing products’ niche is given. After that, the niche selection criteria of RMB financing products’ marketing segmentation is given according to three aspects - needs of customers and behavior characteristics, innovation ability of RMB financing products and competitiveness of banks. At last, the scale of niche selection criteria of RMB financing products’ marketing segmentation is designed which is helpful to the special target market selection of RMB financing products and also provides reference for the exact orientation and dislocation competition of financing enterprises. Key words: Niche; RMB financing products; Market segmentation; Scale designing
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".