Marketing Digital Strategy for Promoting Brand of Global Retailer Achieving Sustainability
Bibliographic record
Abstract
Development of the marketing strategy is a necessary process to promote the brand, create and implement the general company's program.And considering the speed of digital tools' development, the adaptation of the customer to certain approaches, there is a constant search for new effective strategies and methods of promotion.After all, those strategies and methods that were relevant 3-4 years ago (for example, mass-following, hashtags, etc.) are now not effective enough for global retailers especially.The purpose of this paper is to access the current state implementation of global retailer's digital marketing strategy for its brand promotion.So using modern marketing methods of analysis, a marketing management issue was identified, namely the development of a digital strategy of global retailer, which must be solved to improve the business activities of the company and to achieve sustainability.The marketing activity of global retailer in case for H&M Ukraine was analyzed and the current state of implementation the H&M Ukraine's digital marketing strategy was assessed.The main digital tools used by the company were described and their performance evaluation was provided.Based on the results of analyze data, the recommendations for improving the digital marketing strategy of the company were formed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".