At a glance
Order count by state / region (billing_state for US, province for CA, region for UK / AU) over the trailing 30 days. The sub-country geographic decomposition that complements BC AOV by Country and BC Orders by Channel. Useful for state-tax routing decisions, regional shipping zone optimisation, and identifying geographic concentration risk (one state contributes 40%+ of revenue = exposure to that state’s consumer trends).
| What it counts | COUNT(orders) GROUP BY billing_country_code, billing_state over the trailing 30 days. The country-state combination is the unique key, “California” appears under US; “Bavaria” under DE. Excludes Cancelled and Incomplete. |
| API endpoint | GET /v2/orders exposes billing_address.state and billing_country_iso2. The OpenSearch index materialises per-state-per-day order counts. |
| VAT / tax treatment | n/a, count metric. |
| Shipping | n/a, but states with high shipping cost (HI, AK in US) tend to have outsized AOV due to shipping inflation. |
| Discounts | n/a. |
| Refunds | Refunded orders still count by their original state. |
| Cancelled orders | Excluded. |
Incomplete orders | Excluded. |
| Currency | n/a. |
| State coverage | All ISO 3166-2 codes BC indexes. US states: 50 + DC + territories (PR, GU, etc.). Canadian provinces: 13. UK regions: typically not populated by BC’s address fields (UK doesn’t widely use county / region in addresses); UK reads as country-level only. AU states: 8 (incl. NT, ACT). EU regions: variable; some countries populate, some don’t. |
| Sub-country granularity gotchas | Country reads correctly; state varies by country. US, CA, AU well-populated; UK, DE, FR poorly populated. The card auto-degrades to country-level for poorly-populated countries; configure under Settings → Geography. |
| B2B Edition behaviour | B2B orders use the wholesale account’s billing state, which is the customer’s HQ rather than the delivery state. For ship-to attribution, configure Settings → Geography → Use shipping state. |
| Multi-storefront | Per-storefront filter available; useful for “is my UK storefront actually attracting English vs Scottish customers?”. |
| Time window | 30D rolling. |
| Alert trigger | None on this card; pairs with BC Alert Channel Revenue Drop for state-level anomaly detection (configurable per-state). |
| Roles | owner, marketing |
Calculation
Calculated automatically from your BigCommerce data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A US homewares brand on BigCommerce Pro. Snapshot for 1 Apr to 30 Apr 26, US-only view (top 10 states by order count).| State | Orders | % | AOV (context) | Notes |
|---|---|---|---|---|
| CA | 1,180 | 24.5% | $124 | Largest state, high-AOV |
| NY | 720 | 14.9% | $138 | High-AOV, premium urban |
| TX | 540 | 11.2% | $112 | Volume-driven, broad demand |
| FL | 420 | 8.7% | $115 | Volume-driven |
| IL | 310 | 6.4% | $128 | Premium urban |
| WA | 240 | 5.0% | $135 | Premium urban |
| MA | 220 | 4.6% | $142 | Highest AOV in top-10 |
| GA | 180 | 3.7% | $108 | Volume-driven |
| NJ | 160 | 3.3% | $130 | Premium urban |
| PA | 145 | 3.0% | $118 | Mid-AOV |
| Other 40 states | 705 | 14.6% | various | Long tail |
| Total US | 4,820 | $120 |
- CA + NY together = 39% of order count. This is healthy concentration for a US homewares brand; the two largest population states should be ~40%. Concentration above 50% would be a risk (single-state-trend exposure); concentration below 25% would suggest under-coverage in core states.
- The state-AOV pattern reveals demographics. Higher-AOV states (NY, MA, WA) trend urban / premium; volume states (TX, FL, GA) trend value-conscious. The merchant could test premium-positioning ads in high-AOV states and value-positioning in volume states, the same product, two different funnels.
- MA at 4.6% but $142 AOV is the standout. The Boston / Cambridge demographic loves the brand; AOV is 18% above headline. Worth investigating, is there an academic / professional cluster the brand resonates with? Doubling MA spend would likely produce above-average ROI.
- Long tail at 14.6% across 40 states is moderate. Healthy stores see 15-25% in the long tail (national reach with concentration); below 10% means under-covered (geographic gap to fill); above 30% means under-concentrated (no flagship markets).
- Sales-tax routing implications: states with >5% revenue share need tax-nexus consideration. CA, NY, TX, FL, IL all qualify here. Cross-reference with BC Settings → Tax configurations to ensure proper sales-tax collection in these states.
- Identify top-3 states for marketing concentration; ensure your ads spend matches your customer base.
- Identify high-AOV niche states (MA in this example) for double-down testing.
- Audit long-tail coverage, are there mid-population states (NC, VA, MI, AZ, CO) absent from the top 10 that should be present?
- Cross-reference with BC AOV by Country for the country-level comparison.
- For sales-tax purposes, ensure states above 5% revenue have tax-nexus configured in BC.
Sibling cards merchants should reference together
| Card | Why pair it with Orders by State/Region |
|---|---|
| BC AOV by Country | Country-level rollup; this card is sub-country decomposition. |
| BC Channel Currency Mix | Currency context for international stores. |
| BC Orders by Channel | Channel × geography cross-cut. |
| BC Channel Conversion Rate | Geography may correlate with conversion (urban vs rural broadband). |
| Total Revenue | Revenue context for prioritisation. |
| BC Top Customers | High-LTV customers tend to cluster geographically. |
| BC Channel Refund Rate | Some states have higher refund rates (return-friendly culture, or shipping-damage risk). |
| BC Alert Channel Revenue Drop | State-level anomaly detection. |
Reconciling against the vendor’s own dashboard
Where to look in BigCommerce Control Panel: Analytics → Customers (Plus / Pro / Enterprise) shows per-country customer counts but not natively per-state. Reports → Custom lets you build a state-level report. For tax-nexus configuration: Settings → Tax → Tax Zones shows configured tax states. For shipping-zone analysis: Settings → Shipping → Shipping Zones shows configured zones; cross-reference against this card’s state distribution. Why our state breakdown may differ from BC reports:| Reason | Direction |
|---|---|
| Billing vs shipping state. Default we use billing; BC’s reports vary, gift orders cause divergence. | Either direction |
| State name normalisation. BC sometimes stores “California” instead of “CA”; we normalise to ISO 3166-2 codes. | Different rows for same state |
| Tax-state vs billing-state. Some BC tax features attribute to tax-state (which can differ from billing-state for nexus purposes); we use billing. | Different rows |
| Multi-storefront aggregation. We aggregate across storefronts; BC’s reports are per-storefront. | Different denominators |
| B2B billing-state. B2B uses HQ billing rather than delivery state; the gap can be material for distributed wholesale customers. | Different rows |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
shippo.shippo_shipments_by_state | Shippo’s shipping-state breakdown should match within 10% | Shippo uses shipping address; we use billing. Gift orders diverge. |
avalara.avalara_tax_state_revenue | Avalara’s per-state tax revenue correlates with this card | Avalara aggregates by tax-jurisdiction; some states use county-level tax. |
taxjar.tj_state_orders | TaxJar’s per-state order count should match within 5% | TaxJar uses ship-to for tax; we use billing. |
billing_address.province_code) and Adobe Commerce (per region_code); merchant-facing semantics are equivalent.