Find E-commerce Brands with 100+ SKUs
Catalogue size drives the content workload more than headcount or revenue does. A brand with 500 SKUs has product descriptions, attribute tagging, alt text, and category copy to maintain across every refresh. This playbook ranks brands by the depth of that problem.
The problem
Firmographic prospecting tools surface employee count and revenue band. They don't crawl the storefront, so a thin-catalogue DTC brand and a 5,000-SKU operator look identical in the export. Catalogue-aware outbound has to start with catalogue-aware data.
How DataChi runs it
DataCHI crawls e-commerce storefronts at volume, counts SKUs, maps category structure, scores description quality, and infers the platform. The output is a ranked list with the catalogue facts attached, filtered to your ICP and refreshed as brands add inventory.
What's included
- Storefront crawling with SKU and variant counting
- Category and collection structure mapping
- Platform ID for Shopify, Magento, BigCommerce, and custom builds
- Catalogue-quality scoring on description length, attribute coverage, alt text
- Outbound written to the detected platform
- Continuous refresh as catalogues grow or shrink
Who it's for
AI product copy tools, PIM vendors, merchandising platforms, and catalogue management software.
Outcomes
- Targeting at catalogue-depth precision, not industry-code precision
- Openers that quote a real SKU count or attribute gap
- A working list that picks up new brands as they cross 100 SKUs
- Reps spending discovery time on fit, not on storefront audits
Related Marketing playbooks
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