The Degree and Generation of Localization in Marketing: An Empirical Study Based on Coca-Cola’s Localization Strategy
Bibliographic record
Abstract
In a world where development is no longer limited by geography, globalization has become the slogan and goal of many companies. However, not many have noticed that companies that have achieved international sales, such as Coca-Cola, have adopted a localization strategy. This study aspires to examine the existing cases of localization in the market, how localization is implemented, and the impact of implementing localization, from both macro and micro perspectives. This paper identifies the marketing strategies used by the multinational beverage company Coca-Cola through an analysis of its advertising in China in the process of achieving global sales. Based on the marketing mix, this paper analyzes and compares countries with vastly different degrees of localization to determine the decisive factors for using localization strategies in the four areas of product, price, promotion, and place. Current marketing research on the success of Coca-Cola is still narrow, with studies limited to how marketing success is accomplished in a specific location but does not identify the underlying strategy that had led to such success, localization. Meanwhile, the market lacks practical guidance on localization strategies due to inadequate research and this study fills these currently identified limitations.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".