Methodology
The method behind every Shopify signal
Shoplist maps public and productized Shopify ecosystem signals so GTM and research teams can discover, qualify, monitor, and export account and app intelligence. Here is how that data is collected, labeled, and kept accurate.
Source-labeled
Every field is tagged observed, detected, inferred, estimated, or unavailable, so its confidence level travels with the data.
Public + productized
Built from public Shopify pages and productized ecosystem signals. We do not use private merchant or partner admin data, and business contact details appear only where the surface, plan, and privacy choices support them.
Traceable to the source
Every signal traces back to a public Shopify source, so what you act on is grounded in evidence, not guesswork.
What Shoplist tracks
Signals, organized in layers
Shoplist connects public and productized signals across the Shopify ecosystem. Each layer is collected from a different surface and carries its own coverage and freshness.
Apps & developers
App-store listings and the developers behind them, captured from public Shopify App Store pages.
- App-store listings
- Categories and positioning
- Reviews and review movement
- Pricing and packaging signals
- Launch and listing changes
- Developer and agency context
Merchants & adoption evidence
Shopify storefront signals and detected app-stack evidence, surfaced where coverage supports it.
- Shopify merchants and storefront signals where available
- Detected app-stack evidence
- Install, uninstall, reinstall, and category movement where reliable
- Merchant cohorts and examples
Ecosystem relationships
The agencies, experts, and public relationships that connect apps, developers, and merchants.
- Company and people context where available
- Agencies, experts, and adjacent vendors
- Public relationship context
- Contacts only where appropriate for the surface and plan
Change monitoring
Movement over time — what launched, what changed, and what moved category.
- App launches
- Pricing changes
- Review movement
- Listing edits
- Category movement
- Portfolio or thesis monitoring
How evidence is labeled
Every signal is graded by evidence
Before any signal reaches a page, export, or brief, it is graded with one of five evidence labels — so you always know how strong each data point is and can act with the right level of confidence.
Captured directly from public pages or source systems.
Confirmed from public-surface evidence — near-perfectly accurate when present.
Derived from multiple source signals and subject to error.
Modeled or approximated against a named denominator.
Not visible from current Shoplist coverage.
How to read a Shoplist signal
The same fact lands at a different confidence depending on how it was sourced. The label makes that explicit before the data reaches a memo, IC discussion, or first call.
App-store listing
ObservedCategory, pricing, reviews, launch timing, and developer footprint
Merchant storefront evidence
DetectedVisible adoption examples and app-stack evidence where available
Review and launch movement
InferredDirectional category momentum and competitive movement
Observed install sample share
EstimatedDirectional share inside a named Shoplist-indexed sample
Private financials & admin data
UnavailableARR, revenue, retention, private inboxes, and non-public merchant or partner admin data are not visible from public signals
| Evidence | Label | Use with caveat |
|---|---|---|
| App-store listing | Observed | Category, pricing, reviews, launch timing, and developer footprint |
| Merchant storefront evidence | Detected | Visible adoption examples and app-stack evidence where available |
| Review and launch movement | Inferred | Directional category momentum and competitive movement |
| Observed install sample share | Estimated | Directional share inside a named Shoplist-indexed sample |
| Private financials & admin data | Unavailable | ARR, revenue, retention, private inboxes, and non-public merchant or partner admin data are not visible from public signals |
The pipeline
How a signal becomes a labeled field
Every signal moves through the same four steps before it is published, so confidence is established at the source — not bolted on afterward.
- 1
Capture from public sources
Pull from public Shopify App Store pages, storefronts, and productized ecosystem signals on staggered crawl cadences.
- 2
Cross-check and resolve
Reconcile each signal against other sources. Ambiguous, conflicting, or stale signals are flagged — not guessed.
- 3
Label the evidence
Tag every field observed, detected, inferred, estimated, or unavailable so its confidence level is clear before anyone acts on it.
- 4
Publish what holds up
Promote signals that pass the cross-check to the surfaces where they belong, and keep private or unstable fields out.
Good to know
Freshness, coverage & scope
A few notes on how current the data is and what each surface includes.
Freshness
Different datasets refresh on different cadences; not every field is real time. App-store metadata, pricing, reviews, listing changes, and detected usage can change between crawls.
Accuracy & coverage
When Shoplist detects an app on a store, that detection is near-perfectly accurate — a confirmed install, not a guess. Coverage is comprehensive and expands every day as we track more stores and apps.
Intentional exclusions
Private merchant or partner admin data, internal scores, crawl provenance, and unstable operational fields are deliberately excluded from public Markdown profiles. Contact fields only appear where the customer surface and plan support them.
Naming the denominator
Market-share figures always name what they are a share of — for example, share of observed installs inside a Shoplist-indexed Shopify store sample — so every percentage is anchored to a clear denominator.
Keep reading
See the methodology applied
The same source-labeled signals power the public app directory, investor briefs, and the agent-readable Markdown surfaces.