Digital Marketing of E-sports Industry in China: Case Study of King Pro League
Bibliographic record
Abstract
The purpose of this article is to introduce King Pro League (KPL) in the Chinese E-sports industry and discuss its digital marketing strategies through case analysis. KPL was born in 2016, based on China’s most popular cell phone MOBA game, Glory of King, and also became the largest E-sports league in China. KPL reaches 73 billion views in 2020, while the Glory of King reaches 0.1 billion daily active players. The article reviews the E-sports background and growth in China in recent years, as well as the development and operation of KPL that makes it successful in the industry. This paper also reviewed KPL’s current digital marketing strategies in enhancing customer experiences through manipulation of match mechanisms, commercialization and collaboration with both short-term and long-term business partners, and acquiring customers based on the motivation of E-sports spectators. The author also concludes the original game's strengths and limitations, and reviews King Pro League's further potential.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".