Study Areas, Geocoding, Customers, and Trade Areas

Two coffee shops in San Francisco, California wanted analysis performed on their customer bases to better understand their markets and their customers. 

Store 1's customers are primarily located around the Western Addition of San Francisco, with major populations also coming from Chinatown, the Marina District, and the Richmond District (Figure 1). Store 2's customers are primarily located between the Outer Mission and the Mission District, with a smaller westward concentration in between Monterey Boulevard and Portola Drive (Figure 1). The mean center for Store 1’s customers is located ¼ mile southwest of the store, suggesting that the population is concentrated fairly evenly around the store, with slightly more customers southwest of the store’s location (Figure 1). The Mean Center for Store 2’s customers is ½ mile north-by-northeast of the store’s location. This suggests that there are more distribution of customers for Store 2 is more widespread, and the map supports this by showing a large number of outlying customers as far as Chinatown and the Marina District (Figure 1).

Figure 1: Store Locations & Mean Centers

Their competition is concentrated in the Financial District between Chinatown and I-80, with some other locations throughout the Mission District (Figure 2). The stores in the financial district prevent either store from receiving barely any customers from that district, though the impact is more pronounced on Store 1 (Figure 1). The competing stores in the Mission District limit the concentration of customers in that area, though Store 2 is still able to maintain a fairly strong customer base regardless of the competition (Figures 1,2).

Figure 2: Competitor Locations

Both stores exist in completely different markets. Figure 3 shows Customer Derived Trade Areas with 40% of the customers within the yellow ring, 60% of the customers within the red ring, and 80% of the customers within the blue rings. The stores are both in very different neighborhoods with very different client compositions.  

Figure 3: Customer Derived Trade Areas

Analysis of Store 1’s trade areas indicated that the customers within the 60% ring spend the most on food away from home, with a spending index of 148 (100 is the national average). The 40 and 80% rings had spending indices of 129 and 136, respectively. Among the trade areas for Store 1, the 60% ring has the highest median household income, and the lowest racial/ethnic diversity. The household sizes in Store 1’s trade areas are all between 1.81 – 1.78, suggesting that there aren’t many families in the area.

Figure 4: Store 1's trade area information.

Customers within all of Store 2’s trade areas have higher average spending indices than Store 1’s 60% ring, with the 40, 60, and 80% rings having 151, 159, and 161 respectively. Store 2’s 80% ring has the highest household income, lowest white population percentage, and the highest Asian population percentage among all of the trade areas. Store 2’s trade areas have much lower black populations, much higher Hispanic populations, and substantially more of the population identifying as “Some other race alone”, than Store 1’s trade areas.

Figure 5: Store 2's trade area information

Almost all of Store 1’s customers live within 1.5 miles of Store 1. Store 1 is in a part of the city with a much more traditional street grid which makes it easy for customers to walk to the store directly. Store 2 is in a part of San Francisco with irregularly organized streets, which cause the drive time area rings to be slightly shorter, in Euclidean distance (Figure 6).

Figure 6: Drive Time Areas for both stores.

According to my analysis, Store 2 is in a demographically preferable position. The population’s higher average household size allows for higher disposable income, which allows for more spending on coffee.