1. Define the problem you solve before choosing filters
A Shopify email agency and a store-migration consultant need different lists. The first may care about an existing email platform and the merchant’s product category. The second may care about a commerce platform, an integration constraint, or a store’s current workflow. Write one sentence that describes your service, your ideal merchant, and the evidence that makes a conversation relevant.
For an illustrative campaign, an agency might target beauty merchants in the United States that already use Klaviyo. This is a targeting hypothesis—not evidence that those merchants have poor email performance or want to buy a service. Technology data helps you ask a more relevant question; it does not reveal a company’s private priorities.
2. Build a search with a small number of useful criteria
In Feediqo, choose ecommerce store leads during onboarding, then select Shopify as the commerce platform. Add the country or countries you serve, a category, and the installed app relevant to your offer. If you need a contact immediately, add “has email.”
Alternatives within one field broaden the audience, while different fields narrow it together. Selecting two countries means either country can match. Adding an app then requires a matching store to satisfy both the country condition and the app condition. Avoid adding every available filter before you have seen the first sample.
- Platform: Shopify.
- Market: a country you can actually support.
- Category: a business category relevant to your service.
- Installed app: a technology signal connected to your offer.
- Contact availability: an email if your next step needs one.
3. Treat store-size signals as context, not financial proof
Available product counts, traffic estimates and revenue estimates can help you separate very different merchant profiles. They should not become a claim about a prospect’s audited sales or purchasing budget. A busy store may have a small team, and a specialist store may sell high-value products with comparatively few visits.
If you use a revenue filter, choose a currency and meaningful lower and upper bounds. Do not compare a value recorded in one currency with a threshold intended for another. Leave a size filter off when your offer does not depend on that signal, or when missing data would exclude otherwise suitable businesses.
4. Review the store before revealing a contact
Open several profiles and visit the public store websites. Check that the domain is active, the catalog fits the category, and the business still appears relevant. Review available apps and technology signals in their recorded context; a past observation does not prove that an integration remains active today.
Look for a specific, observable reason for outreach. A suitable business model or a relevant integration is a better starting point than a generic sentence claiming you “noticed issues” that you have not actually verified. Reject mismatches before spending credits or building a large export.
5. Save a reusable audience and a reviewed prospect list
Save the search when its criteria describe an audience you will revisit. Save selected merchants into a prospect list when you have reviewed them and want to keep those particular records. The two serve different purposes: a saved search stores criteria; a list stores selected prospects.
One Feediqo credit unlocks the available contact details for a store, and opening that same revealed record again does not spend another credit. Free accounts can export selected records from their fixed preview; paid plans unlock full-database CSV exports. Each batch supports up to 25 records.
6. Make the next action specific and responsible
Record why each merchant is a fit in your notes, choose a pipeline status, and set a follow-up date where appropriate. Keep a suppression list outside any new campaign, verify contact details before use, and respect requests not to be contacted. An available public email is not evidence of consent or deliverability.
Evaluate the segment by the quality of conversations it produces, not by how many rows you can export. If the first sample is weak, change one targeting assumption and review another small sample before expanding.