100K Lead Qualification & Sector Segmentation System

Overview

Inside Saizen’s lead-generation work, I built and operated a high-volume qualification pipeline that started with more than 100,000 raw online leads and reduced them to roughly 20,000 commercially usable records across 16 sectors.

What It Delivers

• Large-scale extraction of more than 100,000 raw business and contact records.
• Normalization of inconsistent names, company fields, locations, sectors, websites, and contact details.
• Deduplication across overlapping records and repeated sources.
• Verification and filtering designed to remove incomplete, irrelevant, invalid, or low-confidence leads.
• Qualification logic that reduced the raw pool into approximately 20,000 higher-quality opportunities.
• Sector classification across 16 markets, ranging from cybersecurity and hardware to restaurants and salons.
• Structured datasets prepared for research, segmentation, outreach, CRM import, and campaign execution.

What I Did

• Designed the end-to-end data pipeline from raw extraction through cleaning, validation, qualification, and sector segmentation.
• Defined the rules used to decide whether a lead was commercially usable.
• Handled high-volume data engineering, verification, deduplication, enrichment, and output structuring.
• Balanced technical filtering with sales relevance so the final dataset was not merely clean, but actionable.
• Organized the final records into sector-specific groups capable of supporting different outreach strategies.

Outcomes

• A raw dataset exceeding 100,000 leads converted into a controlled qualification system.
• Approximately 20,000 validated and segmented leads retained after filtering.
• Sixteen sector-specific datasets created for targeted commercial use.
• A reusable framework for turning high-volume scraped data into CRM-ready opportunity pools.
• Clear separation between volume metrics and genuine lead quality.

Proven Impact

• 100,000+ raw leads processed.
• Approximately 20,000 qualified leads produced.
• 16 sectors covered.
• Approximately 20% of the raw pool retained as qualified records based on the supplied totals.
• Significant reduction in manual review, verification, and sector-tagging workload.

Why It Matters

• Lead generation fails when raw volume is mistaken for commercial value.
• The system transformed scraping into a complete data-engineering and qualification process.
• Sector segmentation made the dataset usable for differentiated campaigns rather than one generic outreach list.
• The work demonstrates the ability to operate across data volume, data quality, sales logic, and downstream activation.