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Case Study - Chicken Kitchen Expansion - Communicating Data Science Results

Case Study: Chicken Kitchen Expansion

The case study centered around a data-driven decision-making case study for Chicken Kitchen, a premium fast-casual restaurant chain. As a data scientist, I was tasked with analyzing potential expansion locations for the franchise. The focus was on developing effective communication skills for data scientists, emphasizing the importance of tailoring content for different audiences and utilizing various communication formats.

Final presentations are available here:

Chicken Kitchen Profile

Chicken Kitchen positioned itself as "premium fast casual with great service." It appealed to consumers willing to pay a dollar or two more than budget fast food prices for food with "clean" ingredients (e.g., no preservatives), friendly service, and delicious taste. Successful locations tended to be in communities that were more conservative, higher income, younger, and in areas that were more recently built up. Chicken Kitchen locations near other upscale fast food restaurants like Chipotle, CoreLife, and Qdoba were more profitable than those near McDonald's or Taco Bell.

Ideal Location Characteristics

Chicken Kitchen performed well in locations that were:

  • More newly developed
  • Had highway proximity
  • Had household income above the national median
  • Had a car culture (as opposed to reliance on public transportation)
  • Had room for continued population growth and development
  • Had a dining-out culture
  • Skewed more conservative

Case Study Requirements

The franchise department of Chicken Kitchen requested assistance in identifying a new location for expansion based on applications from two counties. The case study involved:

  1. Researching and comparing two counties: one from Michigan and one from another state
  2. Determining which county was more advantageous for expansion
  3. Identifying specific action steps for the organization within the preferred county
  4. Explaining why the other county was rejected

The analysis did not require finding a clear "winner" and "loser." The goal was to effectively communicate the decision-making process to the executive team, considering both immediate expansion potential and long-term growth opportunities.

Data Analysis Requirements

The case study involved using government and proprietary consumer data to analyze the demographics and dynamics within each county. Key considerations included:

  • Alignment of county demographics with Chicken Kitchen's target market
  • Potential for at least two locations within the chosen county
  • Evaluation of newly developed areas with highway proximity
  • Assessment of household income relative to the national median
  • Analysis of transportation habits (preference for car culture)
  • Examination of population growth trends and development potential
  • Evaluation of local dining-out culture
  • Consideration of political and cultural leanings

Chicken Kitchen Branding

The case study also considered Chicken Kitchen's branding, notably their pink logo. The specific colors used were:

Logo

CK Logo

Darker Pink of Logo:

  • Hexcode: #AA3660
  • RGB: (170, 54, 96)
  • CMYK: (14, 27, 14, 89)

Lighter Pink of Logo:

  • Hexcode: #DE5498
  • RGB: (222, 84, 152)
  • CMYK: (8, 75, 4, 0)

Methodology

Data Sources

The analysis incorporated data from various sources, including:

  1. American Community Survey (ACS) data via CensusReporter.org
  2. United States Census data via Gale Business DemographicsNow
  3. Consumer behavior and profile data from Gale Business DemographicsNow
  4. Web data from Google Maps, Wikipedia, and Yelp

Key Success Criteria

The analysis focused on the following key success criteria:

  1. Total Population
  2. Stable or increasing population of young people
  3. Income Growth
  4. Infrastructure
  5. Peer Competitors

Analysis and Results

The project compared two counties: Oakland County, MI and Bergen County, NJ.

Key Findings

  1. Total Population (2020)

    • Oakland County, MI: 1,257,982
    • Bergen County, NJ: 941,439
  2. Young Population (age 20 to 44)

    • Oakland County maintained a stable young population at approximately 31.5%
  3. Income Growth

    • Oakland County showed an estimated 8% growth in the 100k+ income group from 2020 to 2025
  4. Infrastructure

    • Oakland County has good infrastructure with 3 interstate highways (I-96, I-275, I-75)
  5. Peer Competitors

    • The presence of peer competitors in Oakland County was noted, indicating a strong market for premium fast-casual dining

Recommendation

Based on the analysis, Oakland County, MI was recommended for Chicken Kitchen's expansion. Reasons for not choosing Bergen County, NJ included:

  • Smaller total population
  • Slightly lower percentage of young population
  • Lower projected income growth in the 100k+ bracket

Recommended Next Steps

  1. Select Oakland County for expansion and continue monitoring Bergen County
  2. Focus on Novi City within Oakland County
  3. Initiate real-estate research in the target area
  4. Adjust marketing campaigns to align with the local demographic profile

Deliverables

The project resulted in three main deliverables:

  1. Executive Summary: A concise overview of findings and recommendations for the CEO and executive team.
  2. Technical Report: A detailed analysis of the data, methodologies used, and justification for the recommendations.
  3. Oral Presentation: A five-minute recorded presentation summarizing the key points of the analysis and recommendations.

These deliverables were tailored for an audience of the company CEO and their executive team, who, while data-oriented, did not have a background in data science or highly-technical analysis.

Final presentations are available here:

Tools and Technologies Used

  • Data Analysis:
    • American Community Survey (ACS) via CensusReporter.org
    • United States Census data via Gale Business DemographicsNow
    • Consumer behavior and profile data from Gale Business DemographicsNow
  • Visualization:
    • Charts created using data from Gale Business DemographicsNow
    • Maps sourced from Google Maps and Wikipedia
  • Additional Research:
    • Web data from Google Maps, Wikipedia, and Yelp
  • Presentation: PowerPoint or similar software (based on the PDF format of the presentation)

Acknowledgements

Special thanks to the UMSI faculty and librarian Shevon Desai for their guidance and resources throughout this project.