RESEARCH ON MARKETING COMMUNICATIONS IN THE SPHERE OF RETAIL TRADE DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
The approaches to the interpretation of the concept of «marketing communication» in modern business conditions are singled out. The authors propose to consider this concept as an enterprise activity related to advertising, sales promotion, event sponsorship, public relations, direct, interactive, referral marketing, omnichannel interaction with consumers, digital transformation of the enterprise and fintech ecosystems. The development trends of the essential commodities market and some aspects of goods promotion in retail networks in the conditions of Covid-19 are analyzed. The turnover of retail trade in Ukraine and Lviv region in January-September 2021, monthly expenses for home food in Ukraine in the first quarter of 2021 are studied. It is established that for the 1st quarter of 2021 the equivalent total soaring per person averaged UAH 91.55. for one day. The dynamics of prices for basic necessities in Ukraine in 2021 is presented (in% from December 2020 to September 2021). Analyzing the prices of essential goods from December 2020, it was proved that egg prices fell sharply to -20% from May to August, and prices for sunflower oil rose sharply from March to September 2021. Online and offline tools for promoting retail networks in Ukraine are systematized. It is important to use and develop the system of online tools for promoting local product networks in the conditions of Covid-19, in particular «blogging», prize drawing and promotional offers in social networks, mail advertising related to loyalty programs, posting projects on crowdfunding platforms (indirect advertising) with the possibility to attract investments, sms-advertising or viber-advertising through the CRM-system of the enterprise, Internet advertising. The peculiarities of the functioning of the marketing communications system in the sphere of retail trade in the context of digital transformation and the CRM-systems using of enterprises are investigated. The authors propose to understand the development process of marketing communication system in the conditions of Covid-19 pandemic through the prism of the triad «health-security-mutual aid».
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".