How Point-of-Sale Marketing Mix Impacts National-Brand Purchase Shares
Bibliographic record
Abstract
Purchase shares of major national brands in consumer packaged-goods industries vary substantially across stores, both between geographic markets and across stores within markets. We measure the relationship between the variation in national-brand purchase shares and five store-specific marketing mix factors: prices, assortment shares, features, displays, and promotion intensity. We do this by first demonstrating the extent to which purchase shares of the top two national brands across six different categories vary across markets, accounts (defined as chain–market interactions) and stores: market-level variation accounts for approximately 30% of the weekly purchase share variation across stores, whereas account-level and store-level variation explain an additional 13% and 5% of the variation, respectively. We then measure the extent to which assortment, pricing, feature, display, and promotion activities affect the purchase shares of the top national brands. We find that price and assortment share are the two most important point-of-sale factors in determining a brand’s purchase share. We also examine how the proximity to a brand’s city of origin, the assortment share of a store’s private label, the extent of retail competition, and the demographics of the store’s neighborhood affect the purchase share’s sensitivity to the point-of-sale marketing mix, revealing several subtle effects. Finally, we measure the extent to which the variation in top national-brand purchase shares is explained by these five factors. We find that, on average, approximately 56% of the variation in national-brand purchase shares can be attributed to these five factors. These results demonstrate the potential importance of trade marketing on a brand’s purchase shares. This paper was accepted by J. Miguel Villas-Boas, marketing.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".