Using Data to Analyze McDonald's Expansion Strategy in NYC

Posted on Oct 26, 2020

Link to GitHub Repository

Introduction

McDonald’s is the backbone of America’s fast-food industry, generating about $7.84bn across more than 13,800 restaurants in the United States. It is no mistake that their expansion strategy has benefitted them greatly. In high density urban areas such as New York City, it is especially imperative that their strategy is adaptive to the dynamic environment. In New York City, McDonald’s restaurants are as common as pigeons; setting the fast-food giant the focus of this exploratory data analysis project. In this text will we use data to analyze McDonald’s Expansion Strategy.


Focus Question

How does McDonald’s choose their restaurant location? What are the factors that play into it?

Initial Analysis

To get an understanding of McDonald's location strategy, first it helps to know where their chains are actually located. Utilizing the longitude, latitude, city, and state variables from a Kaggle dataset for McDonald’s locations in the United States, here is the result.

Screenshot of McDonald's Manhattan locations from R Shiny App

The first noticeable detail is how close each restaurant is to one another, a trend that occurs in every borough. But taking a closer look reveals more interesting insights. Restaurants are commonly located near transit centers such as bus stops and subway stations.

Brooklyn
Bronx

But wait, there's more!

- Ronald M. Popeil

After some more inspection, every McDonald’s is also suspiciously near a park, playground or school. In the following set of maps, blue circle markers represent schools and green circles indicate a park or playground.

Manhattan
Queens

This makes sense when you look at the demographics they serve. McDonald's is a family and budget friendly restaurant chain mainly targeting families and lower income individuals, which includes students. However, not every cluster of schools or parks has a McDonald’s located in it. So, what are the prerequisites?


New Angle

What makes one set of park, playground, or school locations more attractive than others?

  1. population density?
  2. economic status of the surrounding area?

The economic status associated with the area each restaurant is found in sounds more interesting. Utilizing zip codes and income information extracted from a Kaggle dataset on the US household income statistics by geographic location, this new angle can be tackled. Since New York City has 176 unique zip codes designated for major neighborhoods in the city this works.

New Driving Question

Does the average household income level of a zip code determine the amount of McDonald’s within it?


Results

Zip codes that contain X amount of McDonald’s restaurant(s) have an average household income of:

  • 1 restaurant: $76,323
  • 2 restaurants: $67,307
  • 3 restaurants: $72,680
  • 4 restaurants: $69,450
  • 5 restaurants: $53,434

What does this show?

McDonald’s targets neighborhoods where the household income level is between $50,000 and $77,000. McDonald's are more frequent in areas that are closer to the median household income in New York City, for instance the fourth bullet point above.

This website offers deeper insight into McDonald's consumers.

This data makes sense when thinking about who McDonald's targets, take for example some of their menu items:

  • The $1 $2 $3 Dollar Menu
  • Happy Meals for kids

Next Steps

This is just the tip of the iceberg for McDonald's location strategy in New York City. Moving forward I want to further explore sub filters McDonald's potentially uses for settling within their desired neighborhood. Specifically, I want to look at high traffic areas with the help of a heat map, and measure the average influx/outflux of people in them. This might offer some explanation as to why the 10010 zip code in Manhattan has five McDonald's restaurants within it.

Thank you for taking the time to read this blog post! Any feedback and/or praise is welcome! 

About Author

Steven Lantigua

Steven Lantigua is a Data Science Fellow at NYC Data Science Academy and a recent graduate from the University of Connecticut. He hopes to leverage his background in research & advisory, where he spent the last year of...
View all posts by Steven Lantigua >

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