> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Orders by Region, ShipBob > Orders by Region for ShipBob stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it. **Metrics type:** [Key Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Fulfilment & Logistics](/nerve-centre/connectors#connectors-by-type) ## 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](/nerve-centre/kpi-cards/shipbob/warehouse-proximity-performance) 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%** | | The card renders as a choropleth with California darkest. Five things to notice: 1. **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. 2. **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. 3. **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. 4. **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. 5. **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](/nerve-centre/kpi-cards/shipbob/warehouse-proximity-performance) | 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](/nerve-centre/kpi-cards/shipbob/avg-delivery-time-by-region) | Per-region delivery time. | Slow regions are usually distant-DC problems; this card and proximity together identify the cause. | | [Geo Cost](/nerve-centre/kpi-cards/shipbob/shipping-cost-by-region) | Per-region cost. | High-cost regions are distant-zone shipments; same root cause as slow regions. | | [SLA Compliance by Warehouse](/nerve-centre/kpi-cards/shipbob/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](/nerve-centre/kpi-cards/shipbob/orders-by-warehouse) | DC throughput share. | Compare to orders-by-region to find geography-DC mismatches. | | Cross-connector: [`shopify.aov`](/nerve-centre/kpi-cards/shopify/average-order-value) 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`](/nerve-centre/kpi-cards/google-analytics/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](https://merchant.shipbob.com/) → **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. | **Cross-connector reconciliation:** | Card | Expected relationship | What causes legitimate divergence | | --------------------------------------------------------------------------------------------- | ----------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [`shopify.unfulfilled_orders`](/nerve-centre/kpi-cards/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`](/nerve-centre/kpi-cards/google-analytics/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. | ***
Documentation cross-reference (for agencies running multiple 3PLs) Customer-geography distribution exists on every commerce platform; the shape is platform-independent because customers ship to the same addresses regardless of which 3PL fulfils. Cross-link exists for documentation navigation; not a reconciliation. * Amazon FBA customer geography (Amazon-channel-only) * Shopify Fulfillment Network historical region report (legacy)
## Known limitations / merchant FAQs **ShipBob vs FBA, can I overlay both?** Not in one card today. ShipBob's view is DTC orders; FBA's is Amazon-marketplace orders. The combined customer geography lives in Shopify (which sees both) or in your data warehouse. ShipBob alone shows the DTC slice. **How does ShipBob choose which DC to ship from for a given region?** Closest-DC-with-stock; multi-DC splits when no single DC has full stock. The card shows where customers are; pivot to [Warehouse Proximity](/nerve-centre/kpi-cards/shipbob/warehouse-proximity-performance) to see which DC actually shipped each region. **SLA-vs-carrier-tracking discrepancy, why are some regions slower than expected?** Three reasons. (1) The closest DC lacks stock for popular SKUs, forcing far-DC ships. (2) Carrier transit-time tables vary by region (rural vs urban); a 1,000-mile zone-7 ship takes 5+ business days even when the DC ships same-day. (3) International regions (UK, Canada, AU) ship from local DCs but local carriers have their own SLA shapes. **Perfect-order rate vs region, what is the relationship?** Some regions have systematically lower perfect-order rates due to distance (longer transit = more damage opportunity), local carrier quality, address-validation issues. Not directly visible in this card; cross-tab with regional SLA / damage cards. **How do I plan for Q4 / BFCM peak using customer geography?** Two actions. (1) Review last year's December map to anticipate gift-shipping shifts (typical East-Coast bump). (2) Pre-position SKUs at the DC closest to the projected gift-recipient population, not the buyer population. The card supports both decisions when paired with year-over-year comparison. **Multi-DC inventory split optimisation, how does this card help?** Highest-leverage. Combine this card (where customers are) with [Inventory by Warehouse](/nerve-centre/kpi-cards/shipbob/inventory-by-warehouse) (where stock is) to identify the largest-volume regions whose inventory is in the wrong DC. The Texas-from-Cincinnati example in the worked example is the canonical case. **Returns flow, do they appear in this card?** No. Card is shipped-orders, not returns. [Returns by Region](/nerve-centre/kpi-cards/shipbob/returns-by-region) is the returns-side equivalent. **Why does ShipBob show order sent to one region but Shopify shows a different ship-to?** Sync of address-edits. If a customer edits their ship-to address after the order is placed but before ShipBob ships, Shopify sometimes captures the new address while ShipBob's snapshot has the old one. The card uses ShipBob's recorded ship-to (truth at label-print). **Why is the international section so small?** Most ShipBob brands prioritise US distribution and treat international as a secondary segment. The 7.9% in the worked example is typical for a US-first DTC brand. International expansion typically requires a dedicated international DC (Bristol UK, Toronto, Melbourne) to compete on local SLA and cost; without it, international ships from the US at high cost and slow speed. *** ### Tracked live in Vortex IQ Nerve Centre *Orders by Region* is one of hundreds of KPI pulses Vortex IQ tracks across ShipBob and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. [Start for free](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.