Korean Celebrity Brand Ambassador as a Strategy to Increase Sales of PT. Shopee Indonesia (Study: “Gfriend” In Shopee 11.11 Big Sale)
Bibliographic record
Abstract
This article examines PT. Shopee Indonesia's strategy in increasing sales with Korean celebrity brand ambassadors. Theory used is the integrated marketing communication theory by Kotler & Armstrong (2008) and brand ambassador concept owned by Rossita & Percy (2005). Data analysis is with observation, documentation and interviews of 4 informants by applying case study methods. The results showed choosing GFRIEND as the brand ambassador of Shopee 11.11 Big Sale campaign resulted in the sale of 70 million items sold on November 11, 2019, and sales tripled in the first hour compared to 2018. By running four of eight integrated marketing communication models by Kotler & Armstrong (2008) such as sales promotion, advertising, interactive marketing, and also events and experiences conducted through both mass media, online media, and social media resulted in a third quarter achievement of 2019 PT. Shopee Indonesia increased by 261,1% or equivalent to 257,2 million dollars, as well as Gross Merchandising Value (GMV) of 69,9% or equivalent to 4,6 billion dollars compared to 2018. This means that making GFRIEND as a brand ambassador Shopee 11.11 Big Sales is the right strategy and successfully brings PT. Shopee Indonesia to a significant increase in sales.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".