Research on the International Marketing Strategy of Tik Tok -- Based on the Analysis of 4P Theory
Bibliographic record
Abstract
In the context of globalization, more and more industries have gone abroad and opened overseas markets. And the internet companies are no exception. This article takes the overseas marketing strategy of the app “Tik Tok” owned by ByteDance Company as the research object. Three issues were discussed in detail: First, the marketing environment of Tik Tok in overseas markets was analyzed, and the opportunities and challenges it faced were pointed out. Secondly, the 4P theory of the product, price, channel and promotion strategies of Tik Tok was used to focus on the analysis. Finally, based on the analysis results, this research puts forward measures and suggestions that “Tik Tok” can be improved in international marketing. Since the launch of the overseas version of Tik Tok, Tik Tok has accumulated more than 500 million active users, surpassing the total population of the United States, Mexico and Canada, covering 150 countries and regions, 75 languages, becoming one of the world's most downloaded applications. The huge success of Tik Tok in the international market is inseparable from the product itself and advantages and the company's grasp of the market, but in the process of rapid growth, it has also experienced many challenges and doubts. It was banned in the Indonesian and Indian markets, and then encountered regulatory pressure from the United States. Behind the seemingly smooth sailing, it is actually undercurrent. This article will analyze Tik Tok's marketing strategy and challenges from the perspective of international marketing to provide ideas for future development of domestic Internet companies overseas.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".