Research on Marketing Strategy of New energy Vehicles in China
Bibliographic record
Abstract
As the background of the exacerbation of climate change and various pollution, the development of new energy vehicles is gradually on the right track.The study used Secondary market and case study two methods to collect and analyze the data.The study cited BYD as an example and first discussed the Chinese NEV Car Market and showed the market trend.At the same time, the great intention of high-end NEV cars about the consumer and supply sides of the market was shown.As BYD's advantage of charging stations and its five performance are also facing challenges from Tesla and other enterprises.Based on these, this study proposed some solutions.The first is that BYD's strategy mainly focuses on the middle and high-end customer groups, while the current middle and low-end customers are inconsistent with its strategic goals.According to the 4C theory, BYD must understand and design customer products at different demand levels.Secondly, from customer cost management, BYD needs to provide excellent after-sales service to eliminate customers' concerns and provide free maintenance and vehicle repair in the first few years after purchase.BYD will carefully consider issues related to cost, as its pricing focuses on low -and mid-range customers.Although price sensitiveness appear in all customer groups, lower-end customers are much more price-sensitive than high-end customers.Also, BYD needs to properly train its staff to answer any questions from customers and show them expertise and service.At the same time, BYD can also open stores in large shopping malls to attract more people and link brands that can identify regions to a complete marketing strategy.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".