---
title: "Shoplist Data Methodology"
description: "What Shoplist tracks, how data is refreshed, and where coverage has limits."
canonical_url: "https://shoplist.co/data-methodology"
markdown_url: "https://shoplist.co/data-methodology.md"
last_updated: "2026-06-10"
---

# 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.

## Principles
- 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. Shoplist does 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.

## What Shoplist tracks, organized in layers
- Apps & developers: app-store listings, categories, reviews and review movement, pricing and packaging signals, launch and listing changes, and developer and agency context.
- Merchants & adoption evidence: Shopify storefront signals where available, detected app-stack evidence, and install, uninstall, reinstall, and category movement where reliable.
- Ecosystem relationships: agencies, experts, adjacent vendors, public relationship context, and contacts only where appropriate for the surface and plan.
- Change monitoring: app launches, pricing changes, review movement, listing edits, category movement, and portfolio or thesis monitoring.

## How evidence is labeled
- Observed: captured directly from public pages or source systems.
- Detected: confirmed from public-surface evidence — near-perfectly accurate when present.
- Inferred: derived from multiple source signals and subject to error.
- Estimated: modeled or approximated against a named denominator.
- Unavailable: not visible from current Shoplist coverage.

## How a signal becomes a labeled field
- Capture from public Shopify App Store pages, storefronts, and productized ecosystem signals on staggered crawl cadences.
- Cross-check and resolve each signal against other sources; ambiguous, conflicting, or stale signals are flagged, not guessed.
- Label the evidence so its confidence level is clear before anyone acts on it.
- Publish what holds up to the surfaces where it belongs, keeping private and unstable fields out.

## Freshness, coverage & scope
- Freshness: different datasets refresh on different cadences; not every field is real time, and app-store metadata, pricing, reviews, listing changes, and detected usage can change between crawls.
- Accuracy and coverage: detected app installs are near-perfectly accurate — when Shoplist shows an app on a store, it is there. Coverage is comprehensive and expands every day as more stores and apps are tracked.
- 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.
- Shoplist provides market intelligence and research data. It does not provide investment advice, securities recommendations, price targets, or buy/sell guidance.

## Related pages
- [Shoplist overview](https://shoplist.co/index.md)
- [Investors](https://shoplist.co/investors.md)
- [Shopify App Category Brief](https://shoplist.co/reports/shopify-app-market-share.md)
- [Pricing and access limits](https://shoplist.co/pricing.md)
- [Shopify apps directory](https://shoplist.co/shopify-apps)
- [Contact Shoplist](https://shoplist.co/contact)