Data Analysis on Starbucks Location

Posted on Aug 23, 2020
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Data Analysis on Starbucks Location

Introduction

Since its inception in 1971 at Seattle’s Pike Place Market, data shows Starbucks has grown into the world’s largest coffeehouse chain.  The company currently operates over 30,000 locations in more than 70 countries.  In major metropolitan areas, Starbucks locations are seemingly everywhere - on some major thoroughfares in cities such as New York and Chicago there can be up to four locations in a two block radius.  Given this abundance, a a natural question arises: How does Starbucks go about its location selection?

Through analyzing store data and Census demographics, we can gain insights into how Starbucks identifies a target market and expands within these markets.

Data

The map below shows all store locations in the U.S. (each dot shows one store)  Clearly, Starbucks locations are skewed towards coastal areas and major metropolitan markets, which makes sense given the “luxury” of a cup of coffee that can retail for $4 or more.  

Data Analysis on Starbucks Location

The intuition from above is backed up by census data from the American Community Survey accessed through the census.gov API.  Below, we compare demographic data for counties with a Starbucks location and those without.

Data Analysis on Starbucks Location

The data shows that Starbucks targets locations with higher household income (median of ~ $53,000 vs ~$43,000) , larger populations ( median of ~90,000 vs ~18,000) and more individuals in the work force.

Starbucks Locations

Going back to our maps, we can see that Starbucks not only has more locations in areas with larger populations but also over indexes in states with major metropolitan areas.  Washington is clearly an outlier in this respect but given the brand’s roots it make sense that there would be a higher number of stores per capita.

Data Analysis on Starbucks Location
Data Analysis on Starbucks Location

Given this information, the question that follows is, what counties that currently do not have a Starbucks should be considered for future expansion.  From the correlation matrix below, we can see that both population and employment are strong factors into what make an “ideal” Starbucks location.

Factors in an "Ideal" Location

Using both population and employment we train a logistic regression model to classify potential locations.

 

Given that this dataset is a few years old at this point we are able look at the locations that gave the highest probability of having a Starbucks and see if our views are aligned with the company’s actual expansion.  After looking though Starbucks current locations all but one of our highest potential counties now has at least one Starbucks location within the county.  Only Wilson, Texas (outside of Lubbock, Texas) still remains without a Starbucks.

Conclusion

Eventually, Starbucks will increase its footprint into the areas we have identified and expansion within counties / zip codes will require further analysis.  As an example, in New York City, Starbucks operates over 225 stores.  Expansion within the New York City market will require deeper insights into competition, traffic patterns, and landmarks, which county level data can not provide.

However, we have provided a high level filtering strategy for Starbucks initial expansion strategy and this analysis will continue to prove useful in managing county wide demographic changes and the opportunities that these changes may present.

About Author

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI