Bibliographic record
Abstract
Inflationary trends in economy have led to increased media costs, forcing many companies to increased expenditure on sales promotion activities. It has been recognized that well-planned sales promotion activities have a strategic role to play in brand building and enhancing customer loyalty. This study examines the nature of schemes offered in the FMCG(fast moving consumer goods) category, to find out ratio of incentive and outlay (which the consumer is expected to make to avail sales promotion offers), explore the relationships, find out the rationale behind these offers, and provide guidelines to managers designing sales promotion activities. Eight different product categories were selected for the study. Information on actual offers made in these categories in a quarter was compiled and tabulated through content analysis in terms of brand, MRP(maximum retail price), offer(size of the incentive offered), nature of the scheme, pack being promoted, and outlay. Variations in I/O(incentive-outlay) ratios across product categories revealed that the non-food category exhibited more variations than the food category.The level of incentive in the nonfood category was higher than that of the food category , 0.33(33percent) was the most frequently offered level of incentive, Bonus pack followed by free gift and price offs were the popular tools used across product categories , Except for toilet soaps, in other categories medium to large pack was promoted more often. The findings suggest that managers need to be creative to create an impact , otherwise consumers would tend to be less loyal to any brand in a category and drift from one promoted brand to another. Several propositions generated in this research need to be addressed in future research. Factors to be considered and managerial issues concerning the design are also discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".