Strategic Turnaround and Market Resurgence: A Case Study on Luckin Coffee’s Operational Optimization and Brand Revitalization
Bibliographic record
Abstract
China’s coffee market is experiencing rapid growth, but many emerging domestic brands face the risk of over-expansion and weak governance. Luckin Coffee, once derailed by its 2020 financial fraud scandal, provides a representative case of how a company can recover from a crisis in such a competitive environment. This study examines Luckin’s strategic turnaround in China from 2020 to 2025. Using a case study method, it draws on prior academic studies and Luckin’s annual reports, with financial data contrast as the main analytical tool. Findings show that Luckin’s recovery was powered by both operational optimization and brand revitalization. On the operational side, the company reduced costs through supply chain upgrades, tech-based operations, and store format innovation. These rose its net revenue from RMB 4.0 billion in 2020 to RMB 34.5 billion in 2024. On the branding side, Luckin rebuilt trust through governance reforms and cultural marketing, introduced products like the Raw Coconut Latte, and leveraged digital campaigns and co-branding (e.g., Moutai Latte) to expand consumer loyalty and brand equity. The study concludes that Luckin’s revival depended on the synergy of operational efficiency and brand renewal. Theoretically, it extends Aaker’s and Keller’s brand equity frameworks into post-crisis settings. Practically, it provides insights for new consumer brands in China on sustainable growth and crisis management.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".