Mini Case Study 1: Understanding Consumer Behavior in U.S. Coffee Market (Part 1)
Leveraging data from the ‘Great American Coffee Taste Test’ survey (courtesy of Maven Analytics), this case study provides the insights for potential investors interested in launching a coffee business within the United States.
What is a Mini Case Study?
It is essentially a part of my data storytelling content that dives into a step-by-step guide for data analyst projects, like the ones I’ve been working on. It essentially breaks down my report, explaining the thought process behind each visual element.
The Big Idea Worksheet
To introduce this case study, we’ll leverage the Big Idea Worksheet, a tool from Cole Nussbaumer Knaflic’s book ‘Storytelling with Data’. This will help us briefly explain the project’s goals.
Objective 1: Understanding Consumer Price Sensitivity
To understand our target market’s price sensitivity, we’ll analyze two key variables: buying power (highest past purchase) and willingness to pay (maximum they’d consider spending). This will help us determine a suitable price range for our coffee.
To understand how buying power relates to willingness to pay for coffee, we’ll create a price sensitivity matrix. We’ll exclude any blank responses, leaving us with a usable dataset of 3,503 respondents out of the initial 4,042.
For data visualization, a matrix is an excellent choice because it allows us to effectively represent relationships between multiple variables. In this case, we’ll use it to visualize the connection between buying power and willingness to pay for coffee.
By analyzing their past spending habits (highest price paid) in comparison to their willingness to pay, we can segment the respondents into 3 consumer groups for coffee purchases.
A surprising trend emerges when we consider the market attractiveness of each segment. The majority of respondents (42.9%) fall into the price-insensitive category, followed by price-neutral (34.9%). This suggests significant potential for the premium coffee market in U.S.
Assuming these survey respondents are representative of the entire U.S. population allows us to draw general conclusions about price sensitivity in the coffee market. However, it’s important to acknowledge any potential limitations of the survey that could affect the generalizability of these findings.
Our understanding of market attractiveness suggests targeting customers who are less sensitive to price. While the survey indicates a high willingness to pay, it’s important to consider affordability. We should focus on segments that perceive the value to justify the cost.
To determine the ideal price range for a cup of coffee, we should analyze the size of each segment within the buying power and willingness to pay groups. This will help us identify the sweet spot where value perception aligns with affordability. For better visualization, let’s convert our matrix into a heatmap. This will use color intensity to represent values, with brighter colors indicating higher values and dimmer colors for lower values.
Let’s leverage data storytelling to highlight the price-insensitive and price-neutral segments — our most attractive target market due to their willingness to pay a premium for coffee. This allows us to de-emphasize the price-sensitive segment in the heatmap presentation.
The heatmap reveals that most respondents have spent between $6 and $10 on a cup of coffee in the past. They also indicate a willingness to pay between $6 and $15 for a future purchase. It’s important to consider that the ideal price may vary depending on the specific drink being offered.
Conclusion
In conclusion, our analysis suggests a potential starting price range of $6-$15 per drink, based on customer buying power and willingness to pay. However, as a new entrant in the U.S. coffee industry, we need to consider competitor pricing to determine the most strategic price point. We’ll delve into competitor analysis in the next part of this case study.
Thank you for reading!
I hope this case study provided valuable insights. If you have any questions, feel free to reach out.
For those interested in exploring more data storytelling and data visualization content, I consistently create such content on my Patreon page.
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