OPTIMIZATION OF NEW MEDIA INTEGRATED MARKETING PATH FOR FAST-MOVING CONSUMER GOODS: DECONSTRUCTION BASED ON COCA-COLA'S SUMMER CAMPAIGN
Bibliographic record
Abstract
In the new media environment, the fast-moving consumer goods industry is confronted with challenges such as fragmented user attention and diversified consumption scenarios. This article takes Coca-Cola's Summer Campaign as the research object and deconstructs the core logic of its integrated marketing path from four dimensions: brand positioning reconstruction, content matrix innovation, user interaction deepening, and data-driven optimization. Research shows that Coca-Cola has achieved a dual boost in brand awareness and sales conversion through strategies such as emotional value implantation, subculture penetration, and technological tool empowerment. The research conclusion provides a replicable new media marketing methodology for the fast-moving consumer goods industry, emphasizing the need to build a dynamic marketing system centered on users.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".