Coffee Consumer Preferences & Spending Behavior Analysis

By abhishek.verma75000 · June 16, 2026

This report analyzes consumer coffee preferences, spending habits, brewing behaviors, and taste profiles based on 3,770 survey responses. The study…

Coffee D is the clear overall winner, receiving 1,385 votes — that's 36.7% of all 3,770 valid responses. Coffee A came in second with 818 votes (21.7%), followed closely by Coffee C and Coffee B, which were nearly tied at 784 and 783 votes respectively (both around 20.8%).

Total spend was analyzed across three segments — age, employment status, and number of children — with three bar charts and supporting data tables generated for each. The 35-44 age group leads in spending (~$49 avg), full-time employed customers spend the most by employment status (~$47 avg), and households with more than 3 children show the highest spend (~$50 avg).

Across all favorite coffee types and caffeine choices, 'It tastes good' is overwhelmingly the dominant reason people drink coffee. Two bar charts were generated showing the breakdown of why-drink reasons across the top 6 favorite coffee types and caffeine preferences. No matter the coffee type — Pourover (964), Latte (549), Regular drip (362), Cappuccino (292), or Cortado (270) — taste is the 1 driver. The same holds true for caffeine choices: Full caffeine drinkers (3,083), Half caff (162), and Decaf (100) all cite taste as their primary motivation.

Two grouped bar charts were generated comparing what respondents have actually paid (most paid) versus what they are willing to pay (most willing), broken down by roast level and expertise. The charts reveal the gap between actual spending and price willingness across different coffee drinker profiles.

The analysis reveals that low bitterness combined with medium-to-high acidity tends to produce the highest personal preference scores. The top-rated combination is bitterness=1 with acidity=4, achieving a mean preference score of 3.72 across 920 responses. A heatmap and bar chart have been generated to visualize these patterns clearly.

Personal preference ratings vary notably across expertise levels, and the pattern differs by coffee. A line chart and heatmap were generated to visualize these trends across all 10 expertise levels for Coffees A, B, C, and D.

A data table was generated comparing personal preference ratings across Coffee A, B, C, and D. The analysis attempted to calculate mean, median, standard deviation, and rating ranges for each coffee, then rank them from most to least preferred.

The stacked bar chart and data tables reveal how coffee addition preferences vary across age groups. Across all age groups, 'No - just black' is the most popular choice, but the rates differ significantly. The 25-34 age group leads with 57.7% drinking black coffee, followed by 35-44 (54.4%) and 18-24 (51.1%). Older and younger drinkers are less likely to go black — the under-18 group has the lowest rate at just 26.3%, and the 55-64 group sits at 40.0%. The 65+ group comes in at 48.4%.

The analysis identified the top 5 brew methods used by respondents and cross-referenced each with their most popular favorite coffee style. Two visualizations were generated: a grouped bar chart showing favorite coffee styles across the top 5 brew methods, and a frequency/percentage bar chart for the top 5 brew methods. Pour over is the most popular brew method with 575 respondents (15.72%), followed by the combination of Pour over & Espresso (358, 9.79%), Espresso alone (293, 8.01%), Other (206, 5.63%), and Coffee brewing machine like Mr. Coffee (204, 5.58%).

The analysis successfully examined self-reported coffee expertise levels across education groups. Overall, expertise level 7 is the most popular, chosen by 818 respondents (23.8%), followed by level 6 (21.9%) and level 5 (14.7%). The distribution skews toward mid-to-high expertise (levels 5–8 account for over 73% of responses), with very few people rating themselves at the extremes (levels 1, 9, or 10). A heatmap was generated showing how expertise levels are distributed as percentages within each education group, allowing you to visually compare whether higher education levels correspond to higher self-reported expertise concentrations.