beer_markets <- readr::read_csv(
"https://bcdanl.github.io/data/beer_markets_all_cleaned.csv"
)Beer Market
Marketing
Consumer analytics
Analyze household purchases, brands, pricing, promotions, markets, and consumer characteristics.
The beer_markets dataset records household beer purchases across brands and U.S. markets. It connects purchase behavior with product attributes, promotion status, geography, and household characteristics.
Explore the data on a map
Explore the project data by location. Select a location for details.
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Data
On-page tables preview a sample of rows. Download the files to analyze the complete datasets.
The latest available cleaned teaching version was revised on March 6, 2025 and checked on October 6, 2026. It contains 73,115 purchases and 26 variables, with standardized category labels. A newer public release of this matching household sample was not found. The export has no purchase-date field, so its calendar coverage cannot be established and it should support cross-sectional comparisons.
This one CSV already includes every purchase in the earlier teaching version. The cleaning revision changes category labels, while retaining the original numeric observations. Stacking the versions would count those purchases twice.
Variables to know
| Variable | Meaning |
|---|---|
household |
Household identifier |
quantity |
Number of items purchased |
brand |
Beer brand, including Bud Light, Busch Light, Coors Light, Miller Lite, and Natural Light |
dollar_spent |
Total dollar value of the purchase |
beer_floz |
Total volume purchased in fluid ounces |
price_floz |
Price per fluid ounce |
container |
Package or container type |
promo |
Whether a coupon or other promotion applied |
region, state, market |
Geographic market fields |
The dataset also includes household characteristics such as income, education, age, household size, ethnicity, employment class, and the presence of children. Category values may contain underscores, such as BUD_LIGHT.
Possible story directions
- How do price and promotion patterns differ across brands?
- Which markets or household segments purchase the greatest volume?
- Do container choices vary by geography or household characteristics?
- When promotions occur, are they associated with larger quantities or greater spending?
A useful first pass
Start by counting purchases by brand, comparing the distribution of price_floz, and checking how much missing data appears in the demographic fields. Select only the variables that help your group tell one coherent story.