> ## 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. # Top Cities by Orders, BigCommerce > Top Cities by Orders for BigCommerce stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it. **Metrics type:** [Supporting Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Ecommerce Platform](/nerve-centre/connectors#connectors-by-type) ## At a glance > The top-N cities by order count and revenue over the period. The card that turns "we ship to all 50 states" into "62% of revenue comes from 5 metropolitan areas". Geographic concentration drives shipping-rate optimisation, paid-acquisition geo-targeting, and physical-warehouse decisions. **City rankings shift week-to-week**, especially for stores below \~1,000 orders/month where individual buying patterns dominate. Read this card in 30D+ windows; weekly views are too noisy for most stores. | | | | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | `COUNT(orders) GROUP BY billing_address.city` plus `SUM(total_inc_tax)` per city. Top 25 cities by default; toggle to top 50 or top 100 for fuller view. | | **VAT / tax treatment** | Tax-inclusive revenue per the [BC Total Revenue](/nerve-centre/kpi-cards/bigcommerce/total-revenue) definition. | | **Shipping** | Included in revenue; per-city average shipping cost is a useful secondary view. | | **Discounts** | Deducted. | | **Refunds** | Not deducted (gross). | | **Cancelled orders** | Excluded. | | **`Incomplete` orders** | Excluded. | | **Currency** | Multi-currency without FX; cities aggregate across currency at the order count level, but revenue figures are per-currency. | | **Channels / sources** | All channels aggregate. POS orders use the store address (not customer billing) so POS is concentrated to the store's city; web orders show actual customer geography. | | **City-name normalisation caveat** | Customer-entered city names are messy: "New York", "NY", "New York City", "Manhattan" all refer to the same metro but appear as separate rows. The card applies basic normalisation (case-folding, trimming) but does not resolve aliases. **For high-accuracy geo-analysis, use the postcode-level breakdown via Ask Viq** rather than this city-level view. | | **Sample size warning** | Below 100 orders in the period, top-cities rankings are dominated by random variation; one B2B customer in Cleveland can put Cleveland in the top 3. **Don't draw strategic conclusions from the top-cities of a low-volume window**; aggregate to 90D or 180D for stability. | | **B2B Edition behaviour** | B2B orders use `billing_address.city` of the company's primary billing address. A B2B customer placing 50 orders/year shows their company city as a top city even though the company's customers may be distributed widely. Filter to DTC for end-customer geography. | | **Time window** | `30D` (rolling 30 days; settings allow 7D, 30D, 90D, 180D). | | **Alert trigger** | None; this is a discovery card. | | **Roles** | owner, marketing | ## Calculation ``` GROUP BY billingAddress.city.keyword WHERE date BETWEEN [period_start, period_end] ``` ## Worked example A US apparel brand on BigCommerce, 30-day window 14 Apr 26 to 13 May 26. Total of 5,860 orders, \$575,000 revenue. | Rank | City | Orders | Revenue (\$) | Avg shipping (\$) | % of total revenue | | -------------------------- | ----------------- | --------- | ------------- | ----------------- | ------------------ | | 1 | New York, NY | 482 | \$54,300 | \$11.20 | 9.4% | | 2 | Los Angeles, CA | 384 | \$44,100 | \$14.80 | 7.7% | | 3 | Chicago, IL | 218 | \$24,800 | \$9.40 | 4.3% | | 4 | Houston, TX | 196 | \$22,100 | \$11.60 | 3.8% | | 5 | Brooklyn, NY | 168 | \$19,200 | \$11.20 | 3.3% | | 6 | Miami, FL | 142 | \$16,400 | \$13.20 | 2.9% | | 7 | Philadelphia, PA | 138 | \$15,800 | \$9.80 | 2.7% | | 8 | San Diego, CA | 124 | \$14,200 | \$14.40 | 2.5% | | 9 | Atlanta, GA | 118 | \$13,500 | \$11.20 | 2.3% | | 10 | San Francisco, CA | 112 | \$12,800 | \$15.20 | 2.2% | | **Top 10 total** | | **2,082** | **\$237,200** | **avg \$12.24** | **41.3%** | | Long tail (\~2,800 cities) | | 3,778 | \$337,800 | avg \$9.80 | 58.7% | What's interesting: 1. **Top 10 cities = 41% of revenue.** This is moderate concentration; pure DTC commerce typically sits at 35-50% in top 10. Below 30% means very wide distribution (high shipping costs, lots of long-haul); above 55% means heavy concentration (warehouse co-location decisions become valuable). 2. **New York + Brooklyn = \$73,500 (12.7%) of revenue.** **Brooklyn is split out from NYC** in customer entry (different cities to USPS); operationally these are the same metro. Combine them mentally; New York metro is a top-3 priority. 3. **California cities (LA + SD + SF) = $71,100 (12.4%).** West coast concentration; **shipping from an East Coast warehouse to California adds 3-5 days transit and $4-6 to shipping cost** versus a CA fulfilment partner. This is the classic "should I add a 3PL on the West Coast?" trigger. 4. **Average shipping cost varies $9.40 (Chicago) to $15.20 (San Francisco).** SF is twice as expensive to ship to versus Chicago at the same order value; this is geography in action. **For SF customers, consider a free-ship threshold lift** (raise from $50 to $75 just for CA addresses); customers don't notice but margin improves. 5. **Long tail (58.7% of revenue across \~2,800 cities)** is the unstructured majority. Each individual city is small but collectively they're the bulk. **Don't try to optimise the long tail city-by-city**; treat it as "general DTC" and focus the city-specific analysis on top 10-25. Action playbook this card surfaces: 1. **Combine metros mentally.** New York + Brooklyn + Queens + Staten Island = NYC metro; LA + Long Beach + Anaheim = LA metro. Don't optimise sub-cities separately. 2. **Audit shipping rates per top city.** A flat-rate shipping policy will overcharge cheap zones (Chicago) and undercharge expensive ones (SF). Switch to zone-based rates if not already. 3. **Geo-target paid acquisition** to top-10 cities at premium CPM tiers. Each top city is a known-converting geography; ad spend efficiency is highest there. 4. **Consider a West Coast 3PL** if California cumulative revenue exceeds 15-20% of total. Cuts transit time, reduces lost-package rates, lifts repeat purchase rate. 5. **Segment customer-base communications geographically.** New Yorkers respond differently to "free shipping" promos than Texans (NY tax-free status on apparel, TX 8.25%); messaging variants by region lift conversion. 6. **Watch for sudden city rises.** A new city jumping into the top 10 typically signals viral / influencer activity in that geography; investigate to amplify. ## Sibling cards merchants should reference together | Card | Why pair it with Top Cities | | ----------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | | [BC Customer Countries](/nerve-centre/kpi-cards/bigcommerce/orders-by-country) | Country-level rollup; pair to see country-distribution before drilling to cities. | | [BC Orders by State](/nerve-centre/kpi-cards/bigcommerce/orders-by-state-region) | State / region-level; intermediate granularity between countries and cities. | | [Total Revenue](/nerve-centre/kpi-cards/bigcommerce/total-revenue) | Denominator. | | [BC Top Customers](/nerve-centre/kpi-cards/bigcommerce/top-customers) | Customer-level; some top cities are just one or two big customers. | | [BC Shipping Methods](/nerve-centre/kpi-cards/bigcommerce/shipping-methods) | Geographic distribution drives method optimisation. | | [BC Shipping %](/nerve-centre/kpi-cards/bigcommerce/shipping-cost-as-of-revenue) | Wide distribution = higher shipping cost percentage. | | [BC Total Tax](/nerve-centre/kpi-cards/bigcommerce/total-tax-collected) | Tax rate varies by city / state / postcode. | | [Customer Segments](/nerve-centre/kpi-cards/bigcommerce/customer-spend-segments) | Pair to see which customer segments cluster in which cities. | | [Repeat Rate](/nerve-centre/kpi-cards/bigcommerce/repeat-customer-rate) | Per-city repeat rate identifies high-LTV markets. | | [BC Customer Trend](/nerve-centre/kpi-cards/bigcommerce/customer-acquisition-trend) | New customers in new cities is the geographic-expansion signal. | | [BC AOV by Country](/nerve-centre/kpi-cards/bigcommerce/aov-by-country) | AOV varies by city / country; affluent cities (SF, NYC) typically have 1.3-1.6x AOV. | ## Reconciling against the vendor's own dashboard **Where to look in BigCommerce's own dashboard:** BC Insights doesn't natively expose a top-cities report. The closest equivalent is the **Sales Reports → Sales by Customer** filtered to the period; the customer table includes city per customer. Pivot in spreadsheet for a city-level rollup. Plus and Enterprise tiers can build a custom Insights query for city-level revenue. For Standard tier, the Orders export with `billing_city` column is the working data. **Why our number may legitimately differ from the vendor's:** | Reason | Direction | Why | | ---------------------------- | --------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | | **City-name normalisation** | Either | "New York" vs "New York City" vs "NYC" all aggregate to one row in our view (case-folded, whitespace-trimmed); BC may show separately. | | **Billing vs shipping city** | Either | We use `billing_address.city`; BC reports may use `shipping_address.city`. For most orders these match; for gifts they differ. | | **POS orders** | Different attribution | POS orders use the *store* address, not the customer address; our default includes them, distorting the home-store city upward. | | **Time zone** | Trivial | UTC vs store time zone. | | **Sample size** | Trivial | Below 100 orders the rankings are unstable. | **Cross-connector reconciliation (when both connectors are connected for this merchant):** | Card | Expected relationship | Notes | | -------------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------- | | [`google_analytics.ga_top_cities`](/nerve-centre/google_analytics/ga_top_cities) | Top-N city list should overlap by \~70-80% | GA4 attributes by IP-derived city; BC by billing-address city. The two diverge for VPN users, mobile users on cellular roaming, and corporate IPs. | ***
Documentation cross-reference (for agencies running multiple platforms) Top-cities views exist on Shopify and Adobe Commerce with similar definitions. Different city-name normalisation conventions can produce different top-N lists. * [`shopify.top_cities`](/nerve-centre/kpi-cards/shopify/top-cities-by-orders) * [`adobe_commerce.top_cities`](/nerve-centre/adobe_commerce/top_cities)
## Known limitations / merchant FAQs **My top cities shifted dramatically week to week, why?** Sample-size noise. Below 1,000 orders/week, top-cities rankings move on individual large orders. Use 30D or 90D windows for stable rankings. The 7D view is for trend monitoring (is a new city emerging?), not for strategic decisions. **Why is my POS city always #1?** POS orders use the store's billing address, not the customer's. If 30% of your orders are POS at a store in Austin, Austin is your top city by definition. **Filter to web channel only** (channel\_id = 1) for true customer geography. **My city list shows duplicates: "New York" and "New York City" and "Manhattan", how do I fix?** The card normalises case and whitespace but doesn't resolve aliases. For aggregated metro views, use Ask Viq with metro grouping: "show top metros for last 30 days" combines NYC, Brooklyn, Queens, etc. into a single "New York Metro" row. **Should I open a warehouse in my top city?** Probably not based solely on city; based on regional concentration. If California cities cumulatively are >15% of revenue, a CA 3PL is worth costing out. A single city rarely justifies a warehouse unless it's >5% of total revenue. **Why is Boston not in my top 10 when I sell winter apparel?** Possible reasons: (1) seasonal effect (winter apparel peaks in late autumn / early winter; the May window misses it), (2) competitor dominance in Boston (a local rival outranks you organically), (3) shipping cost from your warehouse to MA is high (customers price-shop). Look at 90D windows or year-over-year for stable read. **Can I filter to specific countries?** Yes via Ask Viq: "top cities in the UK over last 30 days". Useful for international stores to see regional distribution within country. **My ranking shows a city I've never heard of in the top 25, what is it?** Possibly a fraud signature (test orders from unfamiliar cities at unusual hours), a single big customer, or a viral / influencer moment. Pull the underlying orders for that city; if they're 5+ orders from different customers, it's a real signal. **Why doesn't this match Google Analytics' city report?** Different attribution: GA uses IP-derived city; BC uses billing address. VPN, corporate IPs, and mobile users diverge. **Trust BC for shipping decisions**, GA for advertising decisions. **Does this include refunded orders?** Yes. Refunded orders count toward their billing city. For net city revenue (excluding refunds), use Ask Viq with refund-exclusion filter. **My top city has a high refund rate, what does that mean?** Geographic refund clustering can indicate: (1) carrier issues in that geography (UPS regional reliability problems), (2) demographic mismatch (the city's customer base doesn't fit the product), (3) fraud cluster (organised return fraud often clusters geographically). Cross-check [BC Top Refunded](/nerve-centre/kpi-cards/bigcommerce/top-refunding-customers) for the same city. **Can I see top cities by margin instead of revenue?** Yes via Ask Viq: "top cities by gross margin for last 30 days". Cities with high shipping costs (SF, Anchorage) often have lower margin than revenue rank suggests. **Why is the top city not the city with the most population?** Population doesn't directly drive ecommerce spend; income, demographics, and ad-targeting do. NY, LA, Chicago, Houston are typically top-4 by population *and* revenue, but mid-size affluent cities (Boston, Seattle, Austin) often punch above their population in DTC. **My B2B Edition portal makes Cleveland #1, but I'm a NYC retail brand. What's happening?** A B2B customer in Cleveland (large company HQ) places high-value orders that pull Cleveland up by revenue. Filter to DTC for end-customer geography. B2B and DTC have different geographies and shouldn't be aggregated. *** ### Tracked live in Vortex IQ Nerve Centre *Top Cities by Orders* is one of hundreds of KPI pulses Vortex IQ tracks across BigCommerce 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.