B2B Data Cleaning / Lead Normalization
BB data cleaning lead-normalization pipeline
A data cleaning pipeline that turns messy inquiry text, phone numbers, emails, country names, product categories...
Reference context
The pipeline is designed for B2B teams whose CRM or spreadsheet is full of inconsistent data. Hexastruct creates field taxonomies, cleaning rules, missing-field flags, negative-signal filters, and a clean handoff to Feishu, Telegram, CRM, or sales sheets.
This page is a clearly labeled benchmark, demo, or reference playbook unless separately confirmed as a Hexastruct closed client project.
Operating logic
- Collect inquiry records from form, email, social, sheet, or manual list
- Extract fields with AI and rule checks
- Normalize country, contact, category, quantity, and urgency
- Merge duplicates while keeping source history
- Score and route clean records with missing-field notes
Expected outputs
- Clean lead schema
- Deduplication logic
- Product taxonomy
- Missing-field report
- CRM-ready record and JS lead file format
Why this matters for Google and buyers
Specific case pages help search engines understand Hexastruct services by category, market, workflow, and buyer problem. For human buyers, the same structure makes the offer easier to trust and easier to brief.
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Share your product category, target market, current lead source, and the manual step you want to remove first.
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