At a glance
Geographic distribution of customer ship-to addresses, rendered as a choropleth map. Where customers actually live; the input that should drive DC-allocation decisions and inventory placement.
| What it counts | COUNT(orders) GROUP BY ship_to_country, ship_to_state over the period. Each shipped order counts 1 against the customer’s ship-to region. Held / cancelled orders excluded. |
| API endpoint | GET /order (Orders API). Reads recipient.address.country_code and recipient.address.state per order. The card aggregates and renders. |
| DC scope | Not DC-scoped, this card is customer-geography, not DC-geography. Use Warehouse Proximity for the cross-tab of customer-region against ship-from-DC. |
| Shipping-method scope | All methods pooled. Method mix varies by region (West Coast more Overnight to coastal-West-Coast customers); not split here. |
| Inventory-split semantics | Order-level. A multi-DC split order counts 1 against the customer’s region. |
| Perfect-order definition | Not applicable, this is a volume distribution metric. |
| SLA definition | Not directly applicable, but the card pairs with SLA cards: regions far from any ShipBob DC will run lower SLA compliance. Customer-geography drives DC allocation strategy. |
| Peak-period seasonality | Q4 typically shifts customer geography toward gift-recipient addresses, not buyer addresses. A West-Coast brand sending Christmas gifts may see East-Coast and Midwest order volume rise 30 to 60% versus baseline; the buyer-vs-recipient distinction matters for DC-allocation. |
| API rate limits | 200 requests / minute / token; standard Orders API; no extra cost over rate cards. |
| Time window | 30D (rolling 30 days) |
| Alert trigger | -, no alert. The card is informational; combine with SLA-by-region or carrier-perf-by-region for actionable monitoring. |
| Roles | owner, operations, marketing |
Calculation
Calculated automatically from your ShipBob 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 DTC home-goods brand using ShipBob 3PL with 3 DCs (Chicago, Moreno Valley, Cincinnati). Reading taken on 12 Mar 26 for the trailing 30 days.| Region | Orders | % of total | DC currently shipping (closest to customer) |
|---|---|---|---|
| California | 4,820 | 19.2% | Moreno Valley (CA) |
| Texas | 3,140 | 12.5% | Cincinnati (OH), suboptimal |
| New York | 2,610 | 10.4% | Chicago (IL), acceptable |
| Florida | 2,180 | 8.7% | Cincinnati (OH), acceptable |
| Illinois | 1,820 | 7.3% | Chicago (IL) |
| Pennsylvania | 1,420 | 5.7% | Chicago (IL) |
| Ohio | 1,210 | 4.8% | Chicago (IL) |
| Other US states | 5,870 | 23.4% | mix |
| International (CA, UK, AU) | 1,990 | 7.9% | Bristol UK / Toronto / Melbourne |
| Total (this card) | 25,060 | 100% |
- California dominates and is well-served by Moreno Valley. 19.2% of orders ship 200 to 800 miles, hitting the 1-day-to-2-day SLA. This is the geography-DC fit working as intended.
- Texas is 12.5% of orders shipping from Cincinnati, a 1,000-mile zone-7 problem. Cincinnati ships these orders because Moreno Valley lacks stock for the Texas-popular SKUs and Chicago is further. Adding Texas-popular SKUs to Moreno Valley would convert these to zone-3 shipments, faster and cheaper. This is the highest-leverage inventory-split decision visible in the card.
- The Other US States bucket at 23.4% deserves drilldown. Geography is fragmented; some “other” states are zone-7 from every existing DC. Pivot to per-state view to find the worst-served regions and decide whether a fourth DC (Atlanta, Dallas) would pay for itself.
- International at 7.9% with three DCs (UK, CA, AU) suggests UK is the biggest opportunity. UK volume from Bristol UK DC ships local Royal Mail / DPD; Canada via Toronto; AU via Melbourne. Each international DC has its own carrier mix and SLA profile; benchmark each separately.
- Q4 typically shifts this distribution toward gift-shipping geography. This brand’s December reading pulled East-Coast volume up by 8 percentage points (gift recipients in NY, NJ, MA, PA) at the expense of California (-3 points). The 6-to-8-week pre-position window before BFCM should account for this shift.
Sibling cards merchants should reference together
This card is the demand-geography view; pair with operational cards for action:| Card | Why pair it with Orders by Region | What the combination tells you |
|---|---|---|
| Warehouse Proximity | Cross-tab of customer region against ship-from-DC. | Identifies regions where customers are too far from current DCs; highest-leverage rebalance opportunities. |
| Geo Delivery Time | Per-region delivery time. | Slow regions are usually distant-DC problems; this card and proximity together identify the cause. |
| Geo Cost | Per-region cost. | High-cost regions are distant-zone shipments; same root cause as slow regions. |
| SLA Compliance by Warehouse | DC-side performance vs region demand. | A DC handling far-zone overflow will sag SLA; the cause is geographic mismatch. |
| Orders by Warehouse | DC throughput share. | Compare to orders-by-region to find geography-DC mismatches. |
Cross-connector: shopify.aov by region (if available) | Per-region revenue. | High-volume + low-AOV regions may not justify a dedicated DC; volume alone is misleading. |
Cross-connector: google_analytics.ga_sessions by region | Traffic-to-conversion ratio per region. | Some regions over-index in traffic but under-convert; could indicate shipping-cost barriers. |
| Cross-connector: customer NPS by region | Downstream sentiment per region. | Regions with worst SLA / longest delivery typically have worst NPS. |
Reconciling against the vendor’s own dashboard
Where to look in ShipBob Merchant Portal: ShipBob Merchant Portal → Analytics → Orders → Geographic Distribution. The portal renders the same map with a downloadable CSV. Use All DCs, All Methods, Last 30 Days for like-for-like. Why our number may legitimately differ from ShipBob’s portal:| Reason | Direction | Why |
|---|---|---|
| Timezone (UTC default) | Boundary days off | Card uses UTC; portal uses UTC by default. |
| DC-level vs aggregated reporting | Aggregated by definition | This card is customer-geography; portal can filter by ship-from-DC for a different (cross-tabbed) view. Different cuts. |
| SLA definition variance | Not applicable | Volume metric, not SLA-tied. |
| Peak-period batch-processing delays | Ours lower for “today” | Q4 webhook lag of 4 to 12 hours; T-2 fully reconciles. |
| Address-validation failures | Either | Orders with unvalidated addresses are excluded from the card (no region attribution); portal may include them in an “Unknown” bucket. |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
shopify.unfulfilled_orders | Upstream order source. | Shopify order count by region should equal ShipBob order count by region within sync-lag tolerance; persistent gap > 24 hours signals connection issue. |
| Amazon FBA orders by region | Peer 3PL with its own customer geography. | Different orders entirely. |
google_analytics.ga_sessions by region | Traffic source-to-buyer geography. | GA traffic geography is by IP geolocation; ShipBob orders are by ship-to address. Differences reveal “ordered from one place, shipped to another” gift behaviour. |
| Customer NPS by region | Downstream sentiment per region. | Sample size limits; NPS only useful for top regions with sufficient survey responses. |