Shopify store database options: app cohorts, stack datasets and matched samples
The right output depends on the job. Use a technology cohort to define who to test, a stack dataset to compare a market signal, or a matched sample to learn whether your offer creates qualified conversations.
Choose by the job
| Job | Best starting point | What it answers | Next step |
|---|---|---|---|
| Target stores running one app | App cohort | How large is the observed technology signal? | Compare the visible breakdowns, then request a sample |
| Compare a merchandising, lifecycle or support stack | Dataset | Which technologies appear together in the current snapshot? | Use the comparison table to choose a narrow hypothesis |
| Test an agency or app-growth offer | Matched sample | Does this cohort produce qualified response? | Run a small test before buying or requesting volume |
Current app-cohort evidence
Latest active app evidence: snapshot storeradar-app-s, observed through 2026-09-07T11:46:19+00:00. Counts are observed storefront signatures, not verified customers or buyers.
| Cohort | Observed storefronts | Observed |
|---|---|---|
| HelpScout | 265,402 | 2026-09-07 |
| Judge.me | 76,617 | 2026-09-07 |
| Klaviyo | 52,293 | 2026-09-07 |
| Loox | 12,430 | 2026-09-07 |
| Okendo | 29,176 | 2026-09-07 |
| PageFly | 12,500 | 2026-09-07 |
| Visually | 231,306 | 2026-09-07 |
Current stack datasets
Each dataset is a first-party comparison surface with visible facts from a versioned snapshot. 5 active dataset(s) are currently published.
| Dataset | Technologies compared | Observed |
|---|---|---|
| Shopify customer support technology stack | 7 | 2026-09-07 |
| Shopify retention and CRM technology stack | 8 | 2026-09-07 |
| Shopify reviews and social proof technology stack | 9 | 2026-09-07 |
| Shopify storefront optimization technology stack | 10 | 2026-09-07 |
| Shopify visual merchandising technology stack | 4 | 2026-09-07 |
How to evaluate the output
- Start with the commercial job, not the largest headline count.
- Keep the canonical URL and observation date with every number you reuse.
- Separate detection from intent: the public page identifies a cohort; the matching workflow tests fit and response.
- Use a small sample to decide whether to expand, change the offer, or stop.
Method and limits
StoreRadar.co detects public Shopify storefront technology signatures and aggregates distinct domains into versioned snapshots. Public pages expose aggregate evidence only. They do not claim vendor accounts, subscriptions, spend, revenue, buying intent, or contact identity. External market context may suggest a question, but it cannot create a StoreRadar.co fact.
Common questions
Is a bigger cohort automatically better?
No. A large cohort gives reach; a narrower stack signal may give a more testable message. Choose the cell that matches the job you sell.
Should an agency start with a raw export?
Usually not. Start with a narrow signal and a small matched sample so you can measure qualified response before committing to volume.
Can these pages be cited?
Yes. Cite the canonical storeradar.co URL and the observation date displayed with the table. Treat the values as aggregate detection evidence.
Run a focused test
Skip an unfocused list. Start with a matched Shopify cohort tied to the technology signal and the offer you want to validate.
View the matched sample →