Labubu Phenomenon: How Celebrity Endorsements and Viral Marketing Drive Sales Growth for Pop Mart
Bibliographic record
Abstract
In the digital-native era, collectible toys such as Pop Mart’s Labubu have gained tremendous popularity. However, limited studies explore how celebrity influence and short-form videos create explosive market phenomena, especially in Chinese brands exporting culturally resonant IPs. This study investigates the Labubu phenomenon, focusing on its role in Pop Mart’s growth through celebrity endorsements, online virality, and hype-driven sales. Using a mixed-methods approach, combining qualitative case study analysis of viral content and endorsements with quantitative data review of sales figures and social media metrics from 2024-2025, the study draws on industry reports and academic literature. Findings show that Labubu generated $670 million in H1 2025, accounting for 34.7% of Pop Mart's revenue, with profits increasing by 396.5%. This growth is attributed to scarcity tactics and social proof. The study highlights the risks of overconsumption and suggests that brands balance hype with sustainability to ensure long-term viability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".