Shoplist data methodology
Read the signal. Make the right move.
Understand how Shoplist collects Shopify app, storefront and market intelligence—and how to read coverage, detection timestamps and change activity when choosing your next target accounts.
One change, four clocks
Merchant changes the storefront
The exact moment sits outside Shoplist’s record.
First detected
Shoplist observes the app on the public storefront.
Activity recorded
The observed addition is processed into activity.
Alert delivered
Your daily brief arrives on its schedule.
Tuesday dates the observation. Wednesday dates the delivery.
Connected Shopify intelligence, with the context to act.
Shoplist combines public Shopify storefront evidence with app listings, reviews, pricing, developers and ecosystem relationships. Use that connected view to find target brands, understand their app stacks and track relevant changes. Read each signal with its source, observation date and population in mind: those details tell you what the data establishes and how to use it.
- The source
- Public signals
Storefront evidence, app listings and connected ecosystem research.
- The clock
- Dated observations
Detection, processing and delivery describe different moments.
- The decision
- Account context
Connect app fit and recent activity to your next commercial move.
On this page
Sources
Where does Shoplist’s data come from?
Shoplist collects public Shopify App Store information, storefront signals and ecosystem context. App research covers listings, categories, developers, collected reviews, pricing and listing changes. Brand research connects merchant records with websites, detected app use and available company, people and relationship context.
These sources answer different questions. A listing tells you how an app is positioned. Storefront evidence connects an app to a website. Reviews reveal experiences and recurring themes. Together, they give an app team a sharper picture of its market and next target accounts. Shoplist’s storefront intelligence does not require access to a merchant’s private Shopify admin.
- Apps and developers: understand the product, publisher, positioning and changes.
- Brands and websites: research merchant context and observed app stacks.
- Reviews and relationships: investigate category needs and ecosystem connections.
Evidence
Separate what is observed from what is derived.
A captured price, a detected storefront app and an estimated share describe different kinds of evidence. Use the field’s wording and context to understand the distinction. A detected app is evidence of app use on the observed storefront; an estimate or inferred relationship adds interpretation to the underlying signals.
| Evidence | Meaning | Use it to |
|---|---|---|
| Captured from a public page or source. | Research a listing, price or collected review. | |
| Identified from public storefront evidence. | Find brands with relevant app-stack signals. | |
| Derived from source signals or relationships. | Develop a hypothesis to investigate. | |
| Calculated or modeled using a defined basis. | Compare a metric within its stated population. | |
| No usable value is supplied for that field. | Keep the question open rather than treating it as zero. |
Freshness
What do the timestamps actually mean?
App listings, reviews, storefront observations and aggregate datasets refresh through different collection and processing paths. Check the timestamp attached to the data you are using. A page refresh records when that view was refreshed; a source observation records when the underlying evidence was collected.
| Clock | What it describes | How to read it |
|---|---|---|
| The first detection recorded for the relevant app relationship or period. | The start of Shoplist’s recorded evidence, rather than an exact merchant-side installation time. | |
| The most recent detection recorded for that relationship. | Read alongside the relationship’s current state and the freshness of the view. | |
| When an observed change enters the activity system. | A detected addition, removal or return has been processed. | |
| When a view or maintained dataset was updated. | Its update clock can differ from the underlying observation clock. | |
| When a notification reaches its destination. | Delivery follows detection, processing and your notification settings. |
App activity
Read additions, removals and reinstalls as observed changes.
Install activity describes an observed app addition or use event in Shoplist’s dataset. Uninstall activity describes a detected removal after the relevant verification and processing path. Reinstall activity records a detected return following a prior removal. These signals reveal changes worth researching and give your team a concrete reason to revisit an account.
An app can be under removal verification before a confirmed removal is published. Brand summaries, website observations and maintained app lists can also update at different times. Use the account detail and dated activity together when a decision depends on a particular app’s state.
Coverage
What does a missing app or field tell you?
Storefront detection depends on the evidence an app exposes and the pages Shoplist can observe. Apps with visible public signatures are discoverable through those signatures. Private, backend-only or otherwise unexposed behavior can sit outside that view. Detection coverage, correctness and freshness are separate questions.
A missing app means it is absent from the returned observations; it does not establish that the merchant has never used it. A blank contact field means no value is supplied on that surface. People and contact access also depend on the product surface and plan. Keep those distinctions in your targeting rules so incomplete fields stay useful questions instead of becoming false conclusions.
Metrics
Compare the same population and time window.
A Brand, a Website, an app relationship and an activity event are different units. A reinstall can create another install event without adding another unique merchant. A count of observed brands is a count of the eligible records in that dataset. A listing-reported review total and a count of review texts collected by Shoplist describe different populations; each research edition identifies which it uses.
When evaluating adoption, market share or momentum, read the denominator, date range and metric definition together. Compare like with like: active-brand counts with active-brand counts, event totals with event totals, and the same observation window across apps. A category review theme is a market insight; it does not establish dissatisfaction at a particular prospect.
Put it to work
Turn a signal into a stronger account decision.
Start with a specific commercial question: which brands use a competing app, which have a complementary stack, or which recently showed a relevant change? Then build a target list that carries both the reason for fit and the evidence behind the timing.
Define the fit.
Choose the app-stack and merchant characteristics that make your product relevant.
Inspect the account.
Read the brand, website, app context and observation dates together.
Write the reason.
Record what makes the account worth pursuing and what recent activity adds to the decision.
Keep the list moving.
Follow priority entities, review incoming changes and update your next action.
Good questions. Straight answers.
Frequently asked questions
Is Shoplist data real time?
Datasets use different collection, processing and refresh paths. Read the observation timestamp for the signal and use notification settings to control delivery after a change is recorded. An instant notification setting does not make collection instantaneous.
Does first detected mean the merchant installed the app that day?
First detected dates Shoplist’s recorded evidence for the relevant relationship or period. The merchant’s installation can predate that observation.
Does Shoplist see every app on every Shopify store?
Shoplist identifies apps through observable storefront evidence. An app’s public signature, page access and observation coverage determine what can be detected. Use the returned stack as observed intelligence when researching the account.
Why can a profile, search result and alert show different dates?
They can reflect different observation, maintained-dataset, refresh and delivery clocks. Check what each date labels and read it alongside the app relationship’s state.
Make your next move
Find your next target accounts with a clear reason to act.
Connect app-stack evidence, account context and market changes in Shoplist. Build the list your team should work next.