Growth drivers, characteristics, preference and challenges faced by Fast Moving Consumer Goods - A study with reference to Bengaluru
Bibliographic record
Abstract
Fast Moving Consumer Goods (FMCG) is the 4th largest sector in India and provides employment to around 3 million people (ASSOCHAM, 2020). FMCG industry in India is growing at 9.4% in due quarter ending March, 2021 after a growth at 7.3% in the previous year. India’s robust economic growth and household incomes are expected to increase consumer spending to US$ 3.6 trillion by 2020. The retail market in India is expected to reach USD 1.1 trillion by 2020 from USD 840 billion in 2017 with a modern trade expected to grow at 20.25% per annum which is likely to boost revenue of FMCG (ibef.org.2018). The demand for packaged goods segment of FMCG grow by 7.8% in March quarter of 2020, compared to non-food categories which grew only 1.8% in value. This trend indicates people preferred panic buying and stockpiling of food items. Covid-19 impacted very much on FMCG sector and a change is observed not only in the consumer behaviour but also made the companies to reconsider strategies towards consumers acquisition, retention and value proposition (Rajeshwari, 2021). Money would not flow to consumers and thus consumers resort to conservative buying (Gaurav Shetty et al., 2020). The need at present arises more than previous about identifying changing consumer buying behaviour. The paper analyses demographic profile of respondents and its impact on FMCG buying, factors driving the growth of FMCG sector, characteristics, respondents preference of health and skincare brands, and challenges faced by FMCG industry. The data for this research work has been collected through questionnaire and findings have been theoretically presented. The survey reveals that respondents are aware of growth drivers of FMCG, characteristics, preferences and challenges faced by the industry.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".