Analysis overview and configuration
| Parameter | Value | _row |
|---|---|---|
| top_n_countries | 10 | top_n_countries |
| top_n_cities | 10 | top_n_cities |
| top_n_provinces | 10 | top_n_provinces |
| date_granularity | monthly | date_granularity |
This analysis examines the geographic distribution of Shopify orders across the United States to identify revenue concentration, regional performance patterns, and city-level demand. Understanding where orders originate helps optimize fulfillment, marketing spend, and inventory allocation by high-value regions.
The order base is heavily concentrated in major metropolitan areas, with New York commanding the highest average order value ($377.60). Texas shows the highest order volume despite lower per-order values, suggesting different customer segments. The significant data loss during processing indicates incomplete geographic tagging in the source system, which may underrepresent smaller markets or rural orders.
Analysis relies on billing address attribution and assumes first line-item revenue represents
Data preprocessing and column mapping
| Metric | Value |
|---|---|
| Initial Rows | 80 |
| Final Rows | 41 |
| Rows Removed | 39 |
| Retention Rate | 51.2% |
This section documents the data cleaning process applied to raw Shopify order exports before geographic analysis. The 51.2% retention rate reflects intentional deduplication and geographic filtering necessary to transform line-item-level transaction data into order-level geographic insights, which directly supports the objective of identifying top-performing regions and revenue patterns.
The 51.2% retention rate is appropriate for this analysis type and does not indicate data quality problems. The documented removal reasons—line-item deduplication and geographic filtering—are standard preprocessing steps for order-level geographic analysis. This ensures each order is counted once with accurate billing location attribution, which is essential for reliable city, province, and country-level revenue calculations presented in downstream datasets.
The analysis assumes billing address accuracy and uses the first line-item row's total for order revenue. No train/
| Finding | Value |
|---|---|
| Total Orders | 41 |
| Total Revenue | $12,512 |
| Average Order Value | $305.16 |
| Countries Active | 1 |
| Cities Active | 12 |
| Top Country | United States |
| Top City | New York, NY |
| Top State | NY |
| Top 3 City Revenue Share | 47.5% |
This executive summary assesses whether the geographic order analysis achieved its objective of identifying top-performing regions and location-based revenue patterns. The analysis examines 41 Shopify orders across 12 U.S. cities to understand market concentration, shipping patterns, and regional performance drivers that inform resource allocation decisions.
The analysis successfully identified geographic performance tiers and regional revenue distribution. The 47.5% concentration in top 3 cities indicates healthy diversification—not over-reliant on a single market—while revealing clear tier-1 performers (NY, CA,
Revenue and order volume breakdown by Billing Country
This section identifies which countries generate revenue for the Shopify store and quantifies their contribution. Understanding geographic revenue distribution at the country level establishes the foundation for deeper regional analysis and helps determine market concentration and international expansion opportunities.
The analysis reveals a fully domestic revenue profile with zero international orders. This 100% concentration in the United States indicates the store currently operates as a single-market business. The $305 average order value and 41-order volume establish a baseline for evaluating performance within US regions, which the city and province-level analyses explore in greater detail.
This country-level view assumes billing address geography attribution. The single-country result limits comparative international insights but enables focused analysis of domestic regional performance across the 12 cities and 7 states represented in the dataset.
Top cities ranked by total revenue and order volume
This section identifies which cities generate the most revenue for the Shopify store, helping to understand geographic concentration of sales. By ranking cities by total revenue and order volume, it reveals whether the business has diversified demand across multiple markets or relies heavily on a few key locations—critical for inventory, marketing, and fulfillment strategy decisions.
The data reveals a moderately concentrated customer base with New York as the clear leader. While the top 3 cities drive nearly half of revenue, the remaining 9 cities collectively contribute 52.5%, suggesting meaningful geographic diversification. The variation in average order values across cities (ranging from $225–$442) indicates potential differences
Monthly revenue trends by top states/provinces
This section tracks how revenue is distributed across the top five states over a six-month period (May–November 2024). Understanding temporal geographic patterns reveals which regions are driving consistent demand versus seasonal or sporadic activity, helping identify stable market segments and emerging opportunities.
The data reveals that geographic revenue is not evenly distributed across time. Rather than steady monthly performance, the top five states exhibit episodic purchasing patterns, with October emerging as a peak month. This suggests either seasonal demand, campaign-driven activity, or data collection timing effects. The median monthly revenue of $0 across all state-month combinations underscores that most geographic markets are inactive in most months, concentrating activity in specific periods.
This analysis assumes billing address accuracy and covers only the five highest-performing states
State/Billing Province performance comparison by revenue and average order value
This section identifies which states drive the highest revenue and average order value, revealing geographic performance disparities across the customer base. Understanding state-level performance helps pinpoint high-value markets and assess whether revenue concentration or order value patterns vary by region.
The data reveals a revenue concentration pattern where NY dominates absolute revenue while TX leads in order frequency. This suggests two distinct customer behaviors: NY customers place fewer but higher-value orders ($377.60 AOV), while TX customers demonstrate higher purchase frequency but lower transaction values. The 64% revenue share concentrated in the top three states (NY, CA, TX) indicates geographic clustering of demand, which is typical for e-commerce but warrants monitoring for market diversification risk.