At a glance
Top cities ranked by order count, derived from Order.shippingAddress.city. The geographic affinity card; tells you where your customers physically live.
| What it counts | COUNT(orders) GROUP BY shippingAddress.city ORDER BY count DESC LIMIT 25 (default 25 cities). City strings are normalised case-insensitively. |
| VAT / tax treatment | Not applicable, count metric. |
| Shipping | Not applicable as a £ figure; city is the shipping destination. |
| Discounts | Not applicable. |
| Refunds | Not deducted; original order’s city is what counts. |
| Cancelled / voided orders | Included if Shopify indexed them. |
| Currency | Multi-currency safe (count metric). |
| Channels / sources | All channels. POS orders typically tag the store-location city, not the customer’s home; this can over-index your retail city. |
| Time window | 30D (default 30D rolling) |
| Alert trigger | None; descriptive distribution. |
| Roles | owner, marketing |
Calculation
Worked example
A UK lifestyle brand on Shopify, ships UK + EU. 30D window 12 Apr 26 to 12 May 26.| Rank | City | Orders | Share | Note |
|---|---|---|---|---|
| 1 | London | 1,820 | 22.8% | Greater London (multiple postcodes lump as “London”) |
| 2 | Manchester | 380 | 4.8% | Northern hub |
| 3 | Birmingham | 290 | 3.6% | West Midlands |
| 4 | Edinburgh | 215 | 2.7% | Scotland |
| 5 | Bristol | 198 | 2.5% | South West |
| 6 | Leeds | 175 | 2.2% | Yorkshire |
| 7 | Glasgow | 158 | 2.0% | Scotland |
| 8 | Liverpool | 142 | 1.8% | North West |
| 9 | Cardiff | 128 | 1.6% | Wales |
| 10 | Brighton | 115 | 1.4% | Coastal south |
| … | … | … | … | |
| Top-25 total | 5,210 | 65.1% | ||
| Long tail | 2,790 | 34.9% | Smaller towns / rural |
- London is structurally dominant. 22.8% share is normal for UK DTC; Greater London is 13% of UK population but ~25% of online retail spend due to age demographics and disposable income.
- Top-25 cities cover 65% of orders. Common shape; the 65/35 split between cities and long tail is consistent across UK DTC. Use for ad-targeting decisions: 65% of the brand’s customers are addressable in 25 city radii.
- Manchester / Birmingham / Edinburgh form the secondary tier. 3-5% share each. These deserve targeted geo-campaigns (out-of-home, local PR, pop-up shops).
- Coastal cities (Brighton, Bournemouth) over-index for lifestyle brands. Aspirational lifestyle alignment with seaside.
- Long-tail dominates rural orders. 35% of orders ship to towns / rural areas not in top-25. These customers are valuable but harder to target through OOH or local press.
- POS bias. If brand has Manchester pop-up, in-store sales tag “Manchester” inflating the rank. The DTC-only Manchester share would be ~3-3.5%; the POS-included share is 4.8%. Filter by sales channel for the e-commerce-only view.
Sibling cards merchants should reference together
Top Cities is the city-level geo card. Companions:| Card | Why pair it with Top Cities |
|---|---|
| Customer Countries | Country-level cousin; cities sum to country totals. |
| AOV by Country | £-weighted geographic split; some cities are higher-AOV than others. |
| Total Orders | Aggregate context. |
| Shipping Methods | Methods cluster geographically; remote cities skew toward express/longer-distance methods. |
| Customer Segments | Geographic segments often correlate with spend tier. |
| Repeat Customer Rate | City-level repeat differs; urban customers often repeat more than rural. |
Reconciling against the vendor’s own dashboard
Where to look in Shopify Admin:Shopify Admin → Analytics → Reports → “Sales by destination city” or “Sales by billing city”The most direct equivalent. Pick the same window. City rankings should match this card to within sync-lag tolerance. Other Shopify Admin views:
- Customers → Filter by city: customer-list filtered to one city.
- Markets → International settings (Shopify Plus): country-aggregate views.
| Reason | Direction | Why |
|---|---|---|
| City normalisation | Either | We normalise case-insensitively but don’t merge alternative spellings (“London” vs “Greater London”). Shopify reports may. |
| Billing vs ship-to | Either | We use ship-to. Shopify exposes both. |
| Time zone | Boundary | UTC vs store time zone. |
| Channel filter | Either | Reports filtered to “Online Store” only differ from blended. |
| Sync lag | Ours lower for “today” | Most-recent 5-15 min may not be in. |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
google_analytics.ga_users_by_city | Should track shape | GA4 measures sessions by IP geolocation, not orders by ship-to city. Shapes correlate but values differ. |
google_ads.gads_revenue_by_geo | Indirect | Ads geo-attribution differs from order ship-to geo. |
Known limitations / merchant FAQs
Why is my “London” share 25%? That seems too high. It probably is right. Greater London is the largest single retail catchment in the UK. The figure conflates inner London, outer boroughs, and the Greater London commute belt. Healthy DTC brands typically see 18-30% London share. My city normalisation looks messy. “London” and “Greater London” both appear. Yes. Customers enter shipping city manually; “London”, “London (England)”, “London Borough”, “Greater London”, and various postcode-suburbs all appear. Normalise externally if you need clean view. Vortex IQ doesn’t auto-merge synonyms; the data is verbatim fromshippingAddress.city.
Should I open a pop-up in cities 4-10?
Maybe. Order-count alone doesn’t justify pop-up economics. Pair with:
- AOV by city (per-city basket size).
- Repeat-rate by city (loyalty depth).
- Pop-up cost (rent, fit-out, staffing).
- Brand awareness (OOH and PR effectiveness).
- Ad clicks don’t equal purchases: traffic-generating cities may not convert.
- City typo / misspelling at checkout: customers enter their city differently than your ad targeting.
- Postcode-area ambiguity: customers in suburbs may type “London” rather than the suburb name.
- Geo ad targeting: top-10 cities deserve paid-media weight.
- OOH placement: top-3 cities are candidates for billboard / transit advertising.
- PR / press placement: time launches with local press in top-5 cities.
- Pop-up location selection: top-3 cities + AOV-overlay + footfall-data.
- Customer-experience research: top customers in the top cities are the ideal qualitative-research panel; geographically clustered for in-person interviews.
- Shipping-cost optimisation: top cities allow consolidated shipping (multi-parcel deals with carriers); long-tail rural orders are more expensive.