Online Marketing Strategies Adopted by Retailers in Organised Retail store During Covid-19 Period: A Study in Balasore City, Odisha, India
Bibliographic record
Abstract
In India, retail industry attained enormous growth in terms of available opportunities and their potential for development of multiple segments and sectors. Presently, the economy of India in retail is considered as a flourishing sector. This sector has been considered as one of the rapid development sectors. The selling of products and services by companies to final consumers is known as retail. The process by which merchants raise interest in and knowledge of their products and services in an attempt to increase sales from customers is known as retail marketing. Retailers may sell their products and services using a wide range of techniques and tactics. The goal of online retail marketing is to draw consumers to various shop formats and online transactions. The best kind of marketing for the store should be used to draw in as many consumers as possible and increase sales for the company. This is the aim of all firms. Traditional retailers are switching from conventional marketing to online marketing. The impact of covid-19 is making a challenge for all retail industry to switch to online mode of retailing. There is a high customer demand for delivery all retail things on a single click on internet. People’s adoption to online retailing has a positive response. The key factors like ease of payment, delivery time and quality of product making the most important element before formulating a new marketing strategy. In India, retail industry attained enormous growth in terms of available opportunities and their potential for development of multiple segments and sectors. This sector has been considered as one of the rapid development sectors. The Indian consumer market is predicted to raise its revenue by 13 per cent per annum.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".