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Metrics type: Key Metrics • Category: Fulfilment & Logistics

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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.

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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.

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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. 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.

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Sibling cards merchants should reference together

This card is the demand-geography view; pair with operational cards for action:

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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: Cross-connector reconciliation:

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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 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 (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 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.

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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 or book a demo to see this metric running on your own data.