Customer Analysis of Coffee Shops in San Francisco
GEOG 352 Assignment 2: Study Areas, Geocoding, Customers, and Trade Areas
Written by Weston Tobias
Introduction
Two friends, Steiner and Bosworth, own coffee and doughnut shops in San Francisco. They have collected the addresses of their customers and have asked me to analyze the data.
Steiner and Bosworth hope to:
- Maximize the trade areas of their respective businesses
- Prevent competition between their businesses
This report will give an overview of where Steiner and Bosworth's customers are located, display the locations of their competitors, and analyze customer data within their respective trade areas (including walking distance).
After analyzing customer data, conclusions and recommendations will be made in order to maximize Steiner and Bosworth's respective trade areas.
Customer Locations
I mapped Steiner and Bosworth separately using the address list they provided. Bosworth has 425 customers and resides in the south side of San Francisco. Steiner has 352 customers and resides in the north side of San Francisco. The mean center for each of their customer groups are right on the location of each of their respective coffee shops. Based off of the proximity of the mean centers to the coffee shops, it seems as if each business is not taking away customers from areas that are too far off from what is reasonable.
Competitors
Many competitor coffee shops are located on the northeast part of San Francisco. Steiner on the North end has more competition than Bosworth on the South end, which helps explain why Bosworth has more customers than Steiner.
Customer Derived Trade Areas
Thirteen of Bosworth customers are in Steiner's trade area in the North. Seven of Steiner customers are in Bosworth's trade area in the South. However, each business' trade areas for 80% of their customers do not overlap. At this moment, competition does not seem to be significant. Should each business owner attempt to maximize their trade area, they may also cause their trade areas to change and overlap.
Each trade area happens to be made of different key demographics defined by the Esri Tapestry data.
Drive/Walk Times
