Use Case

July 8, 2024

How Costco Leverages Geospatial Analysis to Enhance Sales and Logistics Operations

Wanna know how a leading company like Costco makes use of Geospatial Analysis to boost Sales and Logistics Operations ? Read below :)


Costco Wholesale, headquartered in Issaquah, Washington, is a multi-billion dollar global retailer and a recognized leader in the warehouse club industry. Operating 876 locations across countries including the United States, Canada, Japan, and Spain, Costco serves over 132 million members worldwide. Known for its dedication to quality and outstanding business ethics, Costco offers approximately 4,000 SKUs per warehouse, significantly fewer than the 30,000 found in most supermarkets, allowing for efficient and cost-effective operations. 

The company reported impressive financial figures in 2023, with a revenue of $242.3 billion, an operating income of $8.114 billion, and a net income of $6.292 billion. With a strong emphasis on employee welfare, Costco has been recognized as one of the top companies to work for in Washington State. The company also boasts a diverse range of services, including merchandise, gas stations, and its popular private label, Kirkland Signature. Under the leadership of Chairman Hamilton E. James and President and CEO Ron Vachris, Costco continues to thrive and expand globally.Now, let's dive into some insights (just for learning purposes!) on how Costco might analyze their data to better understand their geographical performance.

About Dataset

Let’s Inspect our Superstore dataset. This dataset got all the deeds on sales – order IDs, order and shipping dates, shipping mode, customer names and where they're at, plus product info like category and name. And of course, we've got the numbers – sales amount, quantity, discounts, and profit. 

This data enables analysis of sales performance, profitability, and customer behavior to improve supply chain management decisions. Here are some sample Rows & Columns of the Superstore Dataset.

Order Details
Transaction Information
Row ID Order ID Order Date Ship Date Ship Mode Customer ID Customer Name Segment Country/Region City State/Province
1 US-2019-103800 1/3/2019 1/7/2019 Standard Class DP-13000 Darren Powers Consumer United States Houston Texas
2 US-2019-112326 1/4/2019 1/8/2019 Standard Class PO-19195 Phillina Ober Home Office United States Naperville Illinois
3 US-2019-112326 1/4/2019 1/8/2019 Standard Class PO-19195 Phillina Ober Home Office United States Naperville Illinois
4 US-2019-112326 1/4/2019 1/8/2019 Standard Class PO-19195 Phillina Ober Home Office United States Naperville Illinois
5 US-2019-141817 1/5/2019 1/12/2019 Standard Class MB-18085 Mick Brown Consumer United States Philadelphia Pennsylvania
Product Details
Transaction Information
Postal Code Region Product ID Category Sub-Category Product Name
77095 Central OFF-PA-10000174 Office Supplies Paper Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book
60540 Central OFF-BI-10004094 Office Supplies Binders GBC Standard Plastic Binding Systems Combs
60540 Central OFF-LA-10003223 Office Supplies Labels Avery 508
60540 Central OFF-ST-10002743 Office Supplies Storage SAFCO Boltless Steel Shelving
19143 East OFF-AR-10000348 Office Supplies Art Avery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack
Sales Data
Transaction Information
Customer ID Customer Name Product ID Product Name Sales Quantity Discount Profit
DP-13000 Darren Powers OFF-PA-10000174 Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book 16.448 2 0.2 5.5512
PO-19195 Phillina Ober OFF-BI-10004094 GBC Standard Plastic Binding Systems Combs 3.54 2 0.8 -5.487
PO-19195 Phillina Ober OFF-LA-10003223 Avery 508 11.784 3 0.2 4.2717
PO-19195 Phillina Ober OFF-ST-10002743 SAFCO Boltless Steel Shelving 272.736 3 0.2 -64.7748
MB-18085 Mick Brown OFF-AR-10003478 Avery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack 19.536 3 0.2 4.884

Each row in the dataset corresponds to an order made by a customer. We have the following features:

Order Shipment
Transaction Information
Field Type Description
Order ID String Unique identifier for each order.
Order Date Date Date when the order was placed.
Ship Date Date Date when the order was shipped.
Ship Mode String Mode of shipment chosen for the order.
Segment String Market segment the customer belongs to.
Region String Region where the order was placed.
City String City where the order was placed.
State/Province String State or province where the order was placed.
Postal Code Integer Postal code of the location where the order was placed.
Transaction Information
Field Type Description
Product ID String Unique identifier for each product.
Category String Category of the product.
Sub-Category String Subcategory of the product.
Product Name String Name of the product.
Sales Integer Total sales amount for the order.
Quantity Integer Quantity of the product ordered.
Discount Integer Discount applied to the order.
Profit Integer Profit generated from the order.
Other Feature
Transaction Information
Field Type Description
Row ID String Identifier for each row in the dataset.
Customer ID String Unique identifier for each customer.
Customer Name String Name of the customer placing the order.

The SuperStore Dataset contains over 10194 orders and 13 of our Columns have String Data type, 4 of our Columns have Integers Data Type and 2 of the columns have Date Data Type.

How Costco Uses Geospatial Analytics to Improve Sales & Logistics Performance geographically?

To start with the Geospatial analysis ,it is necessary to follow the 4 factors of Data Analysis and that are : 

1. Identify the users or stakeholders for the dashboard.

2. Design Empathy Map to define Users' Goals and Challenges or pain points.

3. Identify Metrics or KPIs Matter the Most.

4. Understand the Objectives and Goals.

5. Ask Business Questions

I know it looks a bit overwhelming, that's why in this article, we'll lay the foundation for a top-notch geospatial analysis‍.

Step 1: Identify the users or stakeholders for the analysis

Define the User or stakeholder  who will use the geospatial analysis data story. In our case, We’ll use a  Geospatial Analyst. Recognizing their diverse needs, challenges, and priorities becomes the cornerstone for tailoring an effective data story. 

User Persona: Geospatial Analyst

  1. Analyzing geospatial data to discern spatial patterns, trends, and opportunities for optimization within various geographic contexts.
  2. Utilizing Geographic Information System (GIS) tools to map and visualize complex spatial relationships within the supply chain network, including distribution centers, transportation routes, and retail locations.
  3. Evaluating geographical variables affecting supply chain efficiency, such as travel times, route optimization, and site suitability analysis for new facilities, using advanced spatial analysis techniques.
  4. Collaborating with stakeholders to integrate geospatial insights into strategic planning and operational enhancements for the supply chain.
  1. Access to comprehensive, high-resolution geospatial datasets covering the entire supply chain network to ensure accuracy in analysis and mapping.
  2. Proficiency with advanced GIS software and spatial analysis tools to model diverse supply chain scenarios, optimize routes, and conduct location intelligence studies effectively.
  3. Seamless integration capabilities with other systems within the supply chain management framework to maintain a cohesive view of operations, inventory, and logistics.
  1. Ensuring the precision and currency of geospatial data to reflect real-time conditions and changes within the supply chain network accurately.
  2. Addressing the complexities inherent in modeling and analyzing supply chain operations across diverse geographical regions with varying influencing factors.
  3. Meeting the demand for scalability in geospatial analyses to accommodate the expansion or alteration of supply chain networks while preserving the accuracy and timeliness of insights.

Since, we have defined the User Persona & have mapped the needs, challenges and responsibilities. Our Next step will be to design an Empathy Map which will map the pain points of the user.

Step 2: Design Empathy Map

To truly connect with the experiences and expectations of a Geospatial analyst, the creation of an empathy map is invaluable. This visual tool allows for a deeper understanding of the emotions, aspirations, and pain points of users. 

By empathizing with their perspectives, we can design a data story that not only meets functional requirements but also resonates with the human elements of their roles.

Step 3: Identify the Key Performance Indicators (KPI’s)

The heartbeat of any analysis lies in KPI’s and their Metrics. It's crucial to identify the KPIs that matter most to achieving the defined objectives and by focusing on the most relevant metrics, organizations can gain actionable insights into their geographical demographics. 

Transaction Information
KPI Name Formula Description
Total Sales Sum of all Sales Measures the overall revenue generated from all sales. This KPI is essential for evaluating the financial health and growth trajectory of the business across all regions and categories.
Sales by Region Sum of Sales per Region Analyzes sales distribution across different geographical regions, helping identify which regions contribute most to revenue and where to focus growth strategies.
Top Performing States and Cities Max(Sales) grouped by State and City Identifies states and cities with the highest sales, indicating areas of strong market presence and potential regions for targeted marketing or expansion.
Top Performing Categories by Region Max(Sales) grouped by Category and Region Reveals the product categories with the highest sales in each region, offering insights into regional consumer preferences and market demand.
Top Product by Sales and Profit Max(Sales) and Max(Profit) per Product Determines the products with the highest sales and profit, highlighting successful items that significantly contribute to the company's financial performance.

Step 4: Understand the Goals & Objectives of User

Now, on the basis of Empathy Map and KPI’s, we need to define our goals and objectives of the Users. So, that it will align with the data story functionalities to ensure decision making. Here are the Key Objectives & Goals :

The objective of the geospatial analyst within a superstore context is to leverage geospatial analysis techniques and tools to optimize operational efficiency, strategic decision-making, and customer satisfaction. By harnessing spatial data, the analyst aims to enhance the store's performance across various aspects of its operations, from supply chain management to marketing strategies

The primary goal is spatial optimization, achieved by strategically locating stores and distribution centers to minimize transportation costs and improve logistical efficiency. Additionally, the analyst aims to segment customers based on geographic location and purchasing behavior, enabling targeted marketing strategies and personalized promotions.

Step 5 : Ask Business Questions

Beyond KPI’s, organizations must engage in business-driven inquiry. This involves asking strategic questions that directly align with overarching business objectives. 

1. What is the overall sales and profit performance and  trend over time?

  • Metric: Sales and profit
  • Question: 
    • What are my sales and profit?
  • Observation: The total sales is 2.3 M and total profit is 292.3k.

2. How is my performance across regions?

  • Metric: Sales and profit
  • Question: 
    • Sales by region?
    • [Add More] Profit by region?
  • Observation: West has highest sales and profit with central having worst performance in profit and south being low sales and high profit producer .

3. What were the sales by cities?

  • Metric: Sales
  • Question: 
    • Sales by state city?
  • Observation: In terms of sales by city, we observed that New York, Los Angeles, Seattle, San Francisco and Philadelphia are the top 5 cities with over 100k of sales.

4.What are the top/bottom performing states across different regions?

  • Metric: Sales, Profit
  • Question: 
    • Sales and profit by city region?
  • Observation: We can categorize the cities across regions in four quadrants in terms of sales and profit with most of the cities lying in lower performing quadrants. Only the West has cities distributed across all quadrants.


Regionally, the West outperforms other areas, with California leading in profits. The Consumer segment, especially in Technology, shows the highest sales and profit, while certain categories and regions, like Fasteners in Office Supplies and the Central region, underperform.


The West region and the Consumer segment are key drivers of profitability. To sustain growth, focus should remain on high-performing areas like California and Technology products, while strategies need to be developed to improve performance in weaker regions and categories.

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