Tools used in this project
Maven Coffee Challenge

Power BI Dashboard

About this project


The Maven Coffee Challenge involves creating a data-driven strategy for opening a coffee shop based on insights from "The Great American Coffee Taste Test" survey by James Hoffmann and Cometeer. The strategy should cover audience targeting, product selection, and pricing aligned with customer value perception.

About The Data Set

The data contains survey responses from ~4,000 Americans after a blind coffee taste test conducted by YouTube coffee expert James Hoffmann and Cometeer. This first-of-its-kind experiment was designed to provide a largely identical tasting experience for people across the country. After the tasting, and once the surveys were submitted, details about each of the 4 coffees they tasted were revealed (more info in the full dataset details).


Target Audience: Identifying the type of customer to target and understand their preferences based on the survey data.

Product Offering: Determining the types of coffee beans and drinks to offer in the coffee shop, informed by the taste test data.

Pricing Strategy: Devising a pricing strategy that aligns with customer value perception, ensuring competitive pricing while maintaining profitability.

Data Cleaning and Transformation Summary

The original data encompassed a wide range of variables, from demographic information to detailed consumption patterns. It has information related to coffee consumption habits, including age groups, the number of cups of coffee consumed per day, locations where coffee is typically drunk (e.g., at home, at the office, on the go, at a cafe), and opinions on the value for money of coffee, equipment etc. This table serves as the comprehensive baseline from which all transformations and cleaning were initiated.

undefinedTo optimize the data model, wide table is converted into multiple tables clustering based on data similarity ( eg: a table with different methods of coffee brewing) undefined To improve performance, simplify the data schema, and enhance the ability to create complex visualizations and analyses, few tables are unpivoted

undefinedTools Used: Excel (Power Query), Power BI, complemented by design work in Figma.

One of my key principles in dashboard design is to keep it single-page, ensuring that users can quickly grasp insights without navigating through multiple tabs or pages.

In this dashboard, I've utilized what-if parameters and DAX to create a dynamic and interactive experience. Users can adjust parameters to see how changes in variables affect outcomes, providing a deeper understanding of the data and enabling better decision-making.

Few snapshots of the DAX: undefinedundefinedundefined

The Insights of this project are:

Demographics and Consumption:

  • The largest coffee-consuming age group is 25-34 years old.
  • Males drink more coffee than females and other gender identities.
  • The majority of coffee drinkers are white/Caucasian and employed full-time.
  • People with a bachelor's degree represent a significant segment of coffee consumers.
  • Those who work from home tend to consume a lot of coffee, and individuals without children are the biggest coffee drinkers.
  • Political affiliation shows variance in coffee consumption, with Democrats drinking the most.

Product Offerings:

  • Pour-over is the preferred brewing technique, followed by espresso and French press.
  • Most consumers (90%) drink their coffee at home, and a significant proportion (65%) prefer their coffee black.
  • There is a large market for adding milk, dairy, or creamer to coffee, as well as sugar or sweetener.
  • Taste and caffeine are the top reasons for consuming coffee, indicating that flavor and energy boost are key motivators for customers.
  • Whole milk and oat milk are the most preferred types of milk added to coffee, which suggests consumer preference for creaminess and plant-based options.
  • Granulated sugar is the most common sweetener added to coffee, with raw sugar and artificial sweeteners following.
  • Consumers primarily purchase their coffee from specialty coffee shops, local cafes, and national chains, with specialty shops being slightly more preferred than others.

Pricing Strategy:

  • Consumers spend an average of $46 per month on coffee, but the average spend per cup is around $9.
  • There’s a notable difference between the average price consumers are paying and what they’re willing to pay.
  • Only about half of the consumers feel they are getting good value for their money.


  • Ensure your coffee shop is a welcoming space for remote workers, potentially by providing dedicated areas for work.
  • Align your product development with consumer preferences for certain types of coffee and brew methods.
  • Develop targeted promotions and value deals for the 25-44 age group to align better with their current spending and enhance their perception of value.
  • Review and potentially adjust pricing to better match the price customers are willing to pay, especially in the lower brackets (<$6), to attract more price-sensitive customers.
  • Enhance communication around the quality of the beans and the coffee-making process to justify the premium pricing to those who might be willing to pay more for perceived higher quality.
  • Consider introducing smaller sizes or value products that meet the lower price points preferred by some customers.

By presenting these insights through visually appealing and easy-to-understand dashboards, I aim to provide the investors with a comprehensive overview of the coffee market landscape. The visualizations showcase key findings, trends, and recommendations, enabling them to make informed decisions for their market entry strategy.

Overall, this project demonstrates the power of data-driven analysis in guiding business decisions, particularly in a highly competitive industry like the coffee market. Leveraging the wealth of information available in the "The Great American Coffee Taste Test" dataset, I deliver a well-rounded strategy that addresses the investors' key areas of interest while also uncovering additional insights that could contribute to their success.

Figma Template: undefined Thank you, Maven, for this opportunity to broaden my knowledge and contribute meaningfully to the industry.

Additional project images

Discussion and feedback(5 comments)
Duttuluri Hithesh Kumar
Duttuluri Hithesh Kumar
about 1 month ago
Fantastic work on the PowerBI dashboard! I'm impressed by how intuitive and self-explanatory it is, making it easy to navigate and understand the data at a glance.

Alexandru Dobre
about 1 month ago
Cool dashboard but those percentages don't seem to make much sense.

Tuan Anh Tata
Tuan Anh Tata
22 days ago
May I know how you calculate "Consumers spend an average of $46 per month" and "the average spend per cup is around $9". Thank you

18 days ago
The dashboard is cool
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