StoreLeads gives you the stores. You still need a separate tool for the people. Tables ships both, with tech-stack filtering and AI enrichment, in one workspace. That's the short version. Here's why most ecommerce sales workflows stay clunky, and what changes when the whole loop sits in one tool.
Where StoreLeads stops
StoreLeads is good at what it does. It's a directory of ecommerce stores tracking the apps each store uses, traffic estimates, social signals, and store-level metadata. For research and TAM sizing, it works.
The catch is the database doesn't include personal contact information. StoreLeads says so on their own site. To reach the founder, head of ecommerce, or actual buyer with verified email and phone, you bring in a third-party data vendor like Apollo, ContactOut, or FullEnrich. StoreLeads integrates with these, but now you're paying for and managing two tools.
That's where most ecommerce sales workflows get clunky.
Tech-stack filtering at the page level
Tables uses Wappalyzer for technology detection. The depth is the same as StoreLeads on ecommerce, extended across every other category.
Filter for Shopify Plus, WooCommerce, BigCommerce, or any specific app on top. Klaviyo, Recharge, Gorgias, Yotpo, Shopify Markets, whatever the campaign needs. Build a tightly defined ICP in seconds.
If you sell a returns app, filter for Shopify stores running Klaviyo and not running Loop or Returnly. Your TAM is suddenly the exact list of stores that need you, not 50,000 generic Shopify stores.
Personal contact data, in the same workspace
Once the company list is built, Tables connects each store to verified personal contacts. Built into the same product. No second subscription.
Filter by job title, seniority, department. Pull founders, heads of ecommerce, marketing leads, whoever maps to your buyer. Verified emails and direct mobiles come with the contact.
Our data hits 98 percent email accuracy with a seven-day refresh cycle. Industry average for B2B data refresh sits around six weeks, which is why most cold lists bounce at 15 to 30 percent on send. Database size and bounce rate are inversely correlated more often than vendors want to admit. Apollo's own "Verified Emails" filter cuts their database from 275M to 96M when accuracy filters get applied.
Qualify with AI before you call
A list of 5,000 Shopify stores is too long for any rep to work through. The next step is qualifying it down to the 500 worth contacting.
Tables runs AI agents on every row. Ask anything you would Google. International shipping status. Recent hiring activity. Founder podcast appearances in the last 90 days. Main product line.
The agents browse the web, check LinkedIn, and pull the answer back into the row. No prompt engineering. No API keys. The whole list, in minutes.
Sync to HubSpot, owned and ready
Push the qualified list to HubSpot natively. Owner assignment, static fields, two-way sync, all working out of the box.
Reps land assigned and segmented in their existing pipeline, ready to work the moment the list arrives.
One workflow, one tool
Find Shopify stores. Filter by tech stack. Reveal the right people. Enrich with AI. Push to HubSpot, owned and ready.
If your team sells to ecommerce, that's the loop you run. The only question is whether you run it across two tools or one.
Related reading
- How to sell to companies running HubSpot
How to sell to companies running HubSpotIf your product plugs into HubSpot, filter by HubSpot, not by firmographics. That one change moves relevance from 30 to 90 percent.Read post - Tables vs StoreLeads, a side-by-side comparison
- Tables vs Apollo, a side-by-side comparison
