Intensity of Sales Turnover and Promotional Expenditure and the Likelihood of New Product Success: Configurational Matrix of Fast Moving Consumer Goods (FMCG)
Bibliographic record
Abstract
The FMCG marketing phenomenon plays all elements of the marketing mix (4P's), so that each element is thought to have a high degree of complexity. The first objective of this research was to uncover the responses of buyers in the business market to the offering of a new product related to the marketing environment situation - P: promotion and other environments; and next, detecting product performance and its parameters as an evaluation tool for the success of new products. Qualitative research methodology was applied, and was designed using a grounded theory strategy and an interpretative approach, constructivism and pragmatism. Data was collected from the phenomenon of FMCG competition in traditional markets in Indonesia. The results of the research identified buyers' responses reveal that the marketing - promotion environment was directly taken into consideration by purchasing decisions; marketing environment - promotion is valued by the buyer in relation to the marketing environment - price, and the expected impact on the competitive environment - activity and results of promotion. The concepts from the results of this study have implications for the practice of corporate strategies regarding new product launches, marketing and financial budgeting, brand strategy, and marketing market performance. All these matters show that the depth of the purchasing behavior perspective, as well as the performance and success of a product based on promotional strategies is the originality of this paper.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".