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Record W2605428892

Incentive Outlay Ratios in Fast Moving Consumer Goods Sector

2004· preprint· en· W2605428892 on OpenAlexaboutno aff
Vyas Preeta H

Bibliographic record

VenueRePEc: Research Papers in Economics · 2004
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveProduct (mathematics)Promotion (chess)BusinessMarketingFast-moving consumer goodsSales promotionQuarter (Canadian coin)LoyaltyEconomicsMicroeconomicsMathematics
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.307
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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