Segmentation and drivers of wine liking and consumption in US wine consumers
Bibliographic record
Abstract
Abstract: This study examined the influence of selected experiential (wine expertise), psychological (alcoholic beverage adventurousness), and biological (age, sex, 6- n -propylthiouracil [PROP] responsiveness) factors on self-reported liking and consumption of 14 wine styles in a sample of 1,010 US wine consumers. Cluster analysis of wine liking scores revealed three distinct groups, representing plausible market segments, namely red wine lovers, dry table wine likers and sweet dislikers, and sweet wine likers. These clusters differ in key demographic measures, including sex, age, household income, and education, as well as wine expertise and PROP responsiveness. Wines were collapsed into five categories (dry table, sparkling, fortified, sweet, and wine-based beverages) to examine more closely the factors affecting wine liking, total annual intake, and consumption frequency (analysis of variance [ANOVA] followed by Tukey's honest significant difference [HSD] 0.05). Wine expertise was most strongly associated with liking and consumption measures, while PROP responsiveness and alcoholic beverage adventurousness were also important contributors. Neither age nor sex had any large and consistent effects on liking or consumption, although the sex × expertise interaction was significant for some styles. These data provide an example of multifactorial segmentation of a wine market using Northeastern United States as an example, and indicate opportunities for targeted alignment of marketing to cohorts identified here. Keywords: market segmentation, taste genetics, PROP, wine expertise, wine liking, adventurousness
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".