Category–level drivers of the market share–rank power law relationship
Bibliographic record
Abstract
Purpose This paper aims to analyze variations in the parameters of the market share–rank power law across consumer packaged goods (CPG) categories. Design/methodology/approach The authors use a two-level hierarchical linear model to examine the relationships between category-level variables and the parameters of the market share–rank power law in 790 CPG categories. Findings The slope of the market share–rank power law is shallower – indicating more equal market shares – in categories of high importance to retailers and those with high levels of promotional activity or high-volume purchases. Higher levels of market share inequality are associated with categories with high overall prices. Research limitations/implications To the best of the authors’ knowledge, this is the first research to show the systematic influence of category characteristics on the relationship between brands’ market shares and their ranks, thus, identifying a key moderator for this important empirical generalization in marketing. Practical implications While market leadership may be a desirable goal for many brands, the corresponding market share at the top brand does vary. Moreover, the share premium for being number one in the category (gap between the top and other highly ranked brands) can be greatly affected by retailers’ strategies. In addition, the slope of the power law has desirable qualities as a measure of market concentration. However, the empirical study shows that category characteristics must be considered when analyzing differences in concentration across categories or time. Originality/value While other studies document variations in the market share–rank power law relationship, to the best of the authors’ knowledge, this is the first that models these variations as a function of observable category characteristics. The comprehensive nature of the data demonstrates the universality of the market share–rank power law relationship across CPG categories in the USA.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".