Assessing brand equity in the luxury wine market by exploiting tastemaker scores
Bibliographic record
Abstract
Purpose Calculating brand equity, the price differential that a branded product is able to charge compared to an unbranded equivalent, often suffers from a lack of a means to truly determine equivalence. Luxury wines have the benefit of an established measure of equivalency – the Parker score. Robert Parker’s influence as a tastemaker provides a point of comparison across brands. This study looks at brand equity of Bordeaux classified growth wines considering château brands, growths and vintages to illustrate the intangible value for the consumer. Design/methodology/approach Using price and wine-specific data from Wine-Searcher.com, an online database and search engine, an initial sample of 393 wines with Parker scores ranging from 72 to 100 is presented. A subset of perfect wines, with 100-point Parker scores, is also reviewed focusing on the great vintage of 2009. Findings The results indicate that brand equity in the luxury wine market exists. Not only is this true for the brand of a specific château, but there is also equity associated with the vintage and the growth. Practical implications This offers practical implications for brand managers in positioning their wines. Originality/value An analysis of luxury wines supports the financial perspective on brand equity, especially when there is a viable means of determining equivalence, such as the Parker score.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".