Pengaruh Strategi Digital Marketing Terhadap Minat Beli Konsumen Di Era Pandemi Covid-19
Bibliographic record
Abstract
Shopee is a marketplace from Singapore, and has started to expand the Southeast Asian market since 2015 including Indonesia. Lifestyle changes amid the pandemic have increased the use of digital media to support online shopping activities. The number of E-commerce usage has increased by 38.3% during the Covid-19 pandemic which started from January to July 2020. Shopee, which is under the auspices of the SEA Group company, is able to get the attention of consumers in Indonesia. It is known that in the first quarter of 2020 Shopee received 71.5 million visits and in the second quarter of 2020 there were 93.4 million visits with the number of orders entering the number of 260 million orders or an increase of 130% from the previous. The purpose of this study is to find out what digital marketing strategy is being carried out by Shopee, and how it affects consumer buying interest, especially in West Java Province during the Covid-19 pandemic, as well as what efforts can be made by Shopee to improve digital marketing strategies. they. Based on the results of the research, it is known that there are 3 digital marketing strategies carried out by Shopee, namely marketing techniques that are in accordance with the trend, maximizing digital media as a place of promotion, and implementing the 4C marketing mix which has influenced consumer buying interest by 51.50% and the remaining 48. ,50% is influenced by other factors not examined in this study such as needs, quality products, and so on.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".