About Dataset

The dataset provided for the Maven Coffee Challenge offers insights gleaned from a comprehensive survey conducted after a blind coffee taste test involving approximately 4,000 participants across the United States. It serves as a valuable resource to understand consumer preferences in the American coffee market.

The dataset encompasses four distinct variations of coffee, each offering unique flavor profiles and characteristics.

• (Coffee A) Light Roast
• (Coffee B) Medium Roast
• (Coffee C) Dark Roast
• (Coffee D) Fermented Coffee

Business Needs

• Understanding the Target Audience: Investors need to identify their ideal customer - age, income, location - and their coffee preferences (light roasts, flavored options, variety) to tailor their offerings.
• Crafting a Winning Product Mix: Based on customer preferences, the business needs to decide what types of coffee beans and brewing options to offer.
• Developing a Competitive Pricing Strategy: Analyzing customer preferences and historical spending on coffee equipment to establish pricing strategies that align with their expectations and maximize value perception.

Approach

In handling the dataset, I utilized Power BI and Power Query for thorough data cleanup and analysis. Using these tools, I created new measures and visuals to present insights effectively. To streamline the process and make it easy to understand, I divided the analysis into four sections within Power BI. Each section focuses on different aspects of the data, allowing us to highlight important trends and findings. This method helped organize the information in a clear and business-friendly manner, making it easier to grasp the insights from the dataset.

Key Insights:

Target Audience Identification:

• Primary Demographic: Ages 25-34, White Caucasian, Democrat.
• Preference: Home coffee consumption.
• Family Status: Predominantly without children.
• Key Consumer Base: Individuals working from home.
• Education Level: Many have bachelor's degrees, indicating professional flexibility.

Winning Product Mix:

• Cater to diverse preferences with brewing methods like pour-over and espresso.
• Provide somewhat strong coffee to meet the preference for robust flavors.
• Offer a variety of roast levels, including light to medium options.
• Ensure a selection of coffee with full caffeine content, favored by the majority.
• Highlight Coffee D as the overall preferred choice, especially for its high acidity ratings.
• Include bright, fruity blends and robust, full-bodied coffees to appeal to various tastes.
• Offer sugar and flavored syrups as additional, but not primary customization options for healthier or low-sugar choices.
• Provide a range of dairy options (Half and Half, oats, almonds and soy) while emphasizing the availability of black coffee.
• Consider offering Coffee B to cater to the preferences of older individuals, as it is favored by this demographic.

Competitive Pricing Strategy:

• Standard pricing: Offer quality coffee priced between $6 and $8 as a standard option.
• Premium options: Provide potential premium offerings ranging from $8 to $15, with specialty blends exceeding $20.
• Consumer spending: Individuals aged 25-34 have exceeded $1000 in coffee-related expenses over five years, indicating their potential value as customers in the coffee industry.

Recommendations:

• Offer subscription services where customers can receive freshly roasted coffee beans or pre-ground coffee delivered directly to their homes on a regular basis.
• Improve cafe offerings based on customer feedback to enhance overall satisfaction.
• Invest in high-quality coffee equipment to enhance the value proposition for customers.
• Host virtual coffee tastings and educational sessions where customers can learn about different coffee origins, brewing techniques, and flavor profiles from the comfort of their homes.
• Utilize social media and collaborations with influencers to engage the target audience effectively.
• Offer loyalty programs and discounts to incentivize repeat business.

Bias Awareness: The dataset may exhibit gender bias as it contains a higher number of male respondents compared to females. Consequently, the findings may not fully represent the entire population's preferences and behaviors.

Closing Thoughts:

As I wrap up this project, I want to extend my heartfelt appreciation to everyone who has followed along on this journey. Over the past 20 days, I've poured my energy and dedication into crafting insightful analysis and actionable recommendations based on "The Great American Coffee Taste Test" dataset and I hope that the findings shared here have been both informative and inspiring.

I'm open to remote work opportunities. Feel free to like or share any suggestions for improvement. Thank you! https://www.linkedin.com/in/aroosakkhalid/