Our mission is clear:

Strategically position your venture for success by understanding the nuances of the American coffee landscape. By delving into the preferences, behaviors, and expectations of coffee enthusiasts across the nation, we aim to tailor our offerings to resonate deeply with our target audience.

Approach:

The provided data originated from James Hoffmann’s Great American Coffee Taste Test and was accompanied by a sheet of metadata including 55 questions, their types, and corresponding answer possibilities. The survey data utilized for this project was comprised of 111 columns, which included the breakdowns for any multiple-select question into Boolean columns with True/False values for each option. The journey through the data began in the query editor of Power BI.

Considerations:

It is vital to recognize potential volunteer bias within the data. While recommendations will be based solely on the available data, it is crucial to recognize that individuals who volunteered for this study may exhibit a higher level of dedication to coffee compared to the average American. This could introduce a bias that may influence the findings derived from this dataset.

Several questions were multiple-select, meaning the data is not mutually exclusive. For example, an individual who drinks at home could also drink at a café. This is the case for the following pages and charts:

• What is it Worth? (Pricing Strategy)
  • Location of Drinking
  • Location of buying on the Go
• Caffeine Connoisseurs (Consumer insights)
  • How to brew coffee at home

Cleaning:

Delving into the dataset involved becoming familiar with its structure, data types, and values while undertaking various data-cleaning procedures. The initial observation revealed a significant prevalence of missing values. Subsequently, the column labeled 'What kind of flavorings do you add?' along with its associated empty columns, containing no data, were identified and eliminated.

Additionally, columns related to multiple-select questions were eliminated, and Boolean columns derived from these questions were transformed into whole numbers for ease of future aggregation. Text-based fields lacking constraints, such as 'Notes,' were excluded due to their extensive variability.

Supplementary columns were introduced to extract and convert the upper or lower bounds of value ranges, initially presented as text. This conversion facilitated their utilization as constant or continuous axes, simplifying aggregation and integration into histograms. To mitigate the creation of outliers, text such as “100>” was replaced with incrementally reasonable values for lower or upper bounds. For example, the monthly budget was segmented into $20 increments for each range.

To focus the analysis, the dataset was filtered to include individuals who either liked the taste of coffee or left the question blank. This approach was based on the observation that individuals who did not enjoy the taste of coffee tended to consume less and spend less across all metrics compared to those who did.

Lastly, explicit measures were developed to calculate percentages, providing a clear framework for evaluating and interpreting the data.

Planning:

Upon reviewing the data, individual pages dedicated to each specific area of interest were created. Additionally, an extra page was allocated to delve deeper into the interplay between various tester characteristics. Meaningful insights were systematically integrated into the dashboard as the analysis progressed. This is an explanatory dashboard, so filtering has been disabled in most cases.

The primary focus of the initial page centered on examining the ratings associated with test coffees A, B, C, and D. Through this examination, common attributes among the favored coffees were identified. Subsequently, the focus shifted to understanding the spending behaviors of the testers to ascertain their willingness to invest in the products under consideration. However, limitations were faced in gauging information regarding coffee beans due to the spending data primarily relating to purchasing beverages, which would have been valuable considering most testers prepare coffee at home.

Furthermore, the preferences of the target consumers regarding various coffee attributes were examined to pinpoint products with the highest potential for sales within the target market.

To enhance the interpretability of the dashboard pages, color coding was implemented throughout, aligning coffee A with references to Light roast, coffee B with Medium roast, and coffee C with Dark roast as appropriate. Additionally, the chosen color scheme underwent testing for accessibility using a colorblind simulator.

Survey Sleuthing:

The survey covered four different kinds of coffee:

Light Roast -> A

Medium Roast -> B

Dark Roast -> C

Fermented -> D

James Hoffmann characterized coffee D as natural, funky, fruity, and distinctive—an observation not captured in the dataset, yet one that provokes thoughtful consideration. While some tasters may have been intrigued by the "funky" and "distinct" flavors, others may have found them unappealing. Although similar insights are lacking for the other coffees tested, this description could specifically help explain reactions to coffee D.

Coffee A and D exhibited a higher preference among individuals who rated their expertise higher, while B and C displayed the opposite trend.

The survey examined preferences in three subsets: The most familiar roasts for participants (A, B, C), Overall (A, B, C, D), and the Favorites from the other two groups (A, D).

A | B | C --> A emerged as the clear favorite among the three, garnering over 600 votes, surpassing C.

A | B | C | D --> D emerged as the overall favorite with 500+ votes, surpassing A

A | D --> D led by 277 votes but A was closer this time.

D increased by 563 votes while A increased by 846, indicating that individuals who preferred B or C coffee would be more likely to choose a light roast over a fermented coffee if medium and dark roasts were unavailable.

Given that A was among the top two choices, an investigation was conducted to determine if they shared common traits. Utilizing the rating on Preference, Bitterness, and Acidity, a decomposition tree was employed, revealing that those who favored coffees A and D and marked them as 5 for personal preference primarily rated the coffees as 1 or 2 for bitterness and primarily 4 for acidity. Coffee A was considered less acidic than coffee D, considering more participants rated it 3 than 5, while coffee D had the opposite result, with 5 soundly beating the 3 ratings.

It's noteworthy that B and C exhibited very similar ratings to each other, with medium bitterness and acidity, primarily rated with 2s or 3s. Additionally, D, despite having the highest likes, also had the highest 1 ratings, indicating polarization among participants—either they really liked it or they really did not, with fewer individuals falling in the middle compared to the other three coffees.

What is it worth?

In examining consumer spending habits, it is evident that a higher proportion of individuals deem it worthwhile to invest in their coffee brewing equipment compared to purchasing beverages from stores. A significant 95% find equipment investment worthwhile, contrasting with 59% for coffee beverages.

The majority of participants have allocated over $1000 towards coffee brewing equipment, and a notable 93% prefer brewing coffee at home, surpassing other consumption locations like cafes, offices, or on-the-go options combined. While many individuals enjoy coffee both at home and in other locations, only 7% of respondents abstain from home coffee consumption.

An essential component for brewing coffee at home is, undoubtedly, the quality of coffee beans. Further exploration of bean price points and market dynamics would shed light on strategic avenues for supplying beans to other companies or establishing proprietary stores.

In terms of retail strategies, two approaches emerge: the lower-risk route involves partnerships with specialty shops, local cafes, and national chains, leveraging established customer bases to build brand recognition before venturing into proprietary stores. Conversely, the higher-risk, higher-profit option entails establishing standalone specialty shops or cafes, affording complete control over branding, pricing, and direct customer interactions, albeit with greater initial investment and risk.

Regarding coffee purchasing patterns, specialty shops, local cafes, and national chains emerge as the preferred destinations, while drive-thru or market options are less favored. The highest price range for a cup of coffee falls between $6-$8 and $8-$10, with most customers willing to pay within the $8-$10 range. Notably, preferences vary among individuals who consider cafe coffee worth the expense, exhibiting different consumption patterns and equipment investment levels compared to those who do not share the same sentiment.

The highest amount paid for a cup of coffee is close between $6-$8 and $8-$10. When looking at how much customers would be willing to pay, most said $8-10. When looking specifically at customers' thoughts on the value of café coffee, there were differences in where they drink their coffee. Those who think it is worth it drink at Cafes and on the go at an increased rate; they also spend less on coffee equipment, the majority of them spending between $100 and $300, followed closely by $1,000>. Those who believe it is not worth it, drink at home more and less at cafés or on the go, they also have more individuals investing more in their at-home coffee setup.

Maintaining a competitive price point is crucial to retaining customers who value cafe coffee. While many affirm willingness to pay $8-$10, consideration must be given to pricing thresholds, as the percentage of customers willing to pay decreases notably beyond $10. Understanding local market pricing dynamics is imperative to make informed pricing decisions.

Caffeine Connoisseurs:

Among participants who brew coffee at home, Pour Over emerges as the most favored method, best suited for medium to medium-fine grinds. Espresso follows, requiring finely ground beans, with French Press requiring a coarse grind to prevent slippage through the mesh filter.

Consumer demand leans heavily towards caffeinated options, with most individuals consuming two cups daily. Monthly spending on coffee ranges from $48 to $60 across age groups, notably peaking among 25 and 45-year-olds.

The analysis of expertise levels reveals that the majority of participants rated themselves between 5-8 in terms of coffee knowledge. Individuals with these levels of expertise averaged higher preference ratings for A and D. Given the prevalence of 5-8 level expertise individuals, it is understandable how A and D were the most favored. Additionally, the data indicates that individuals in the middle age group exhibit the highest monthly spending on coffee-related purchases, which correlates with individuals in that expertise bracket. This observation underscores the alignment between consumer behavior, product preference, and age demographics, reinforcing insights obtained from earlier analyses.

While Light roast remains popular, Medium roast gains traction alongside it. Pour Over emerges as the top choice among favorite drinks, significantly ahead of alternatives. Most consumers prefer somewhat strong or medium-strength coffee, with fruity undertones ranking highest. Light coffee drinkers lean towards light flavors, while medium coffee drinkers favor chocolatey notes. Interestingly, the overall favorite, Coffee D was reported by James Hoffmann to have a fruity flavor.

Stated preferences versus observed tastes reveal nuances, particularly concerning medium and dark roasts. While Light roast is the observed preference, preferences for Medium and Dark roasts show a reversal, indicating the line between medium and dark may be blurry.

KEY TAKEAWAYS:

• Winning Attributes: Investors should prioritize coffee varieties characterized by mild bitterness and robust acidity. By focusing on these attributes, investors can align product offerings with consumer preferences, enhancing market competitiveness and consumer satisfaction.
• Price Point for Beverages: Optimal beverage prices should not exceed $10, with the majority falling within the $6-$8 range. Maintaining competitive pricing within this range ensures attractiveness to consumers while maximizing profitability.
• Coffee Beans as Market Entry Strategy: Leveraging coffee beans presents a promising avenue for market entry due to its versatility and potential for differentiation. Exploring diverse distribution channels and branding strategies can capitalize on the growing demand for quality coffee beans.
• Most Market Appeal: Offer somewhat strong fruity light roast or chocolatey medium roast pour-over coffee. These profiles resonate well with a broad audience and align with prevailing consumer preferences, potentially enhancing product adoption and market penetration.