B2B Sales Research & Data Automation
Data automation pipeline discovering, filtering, and deduplicating prospect business leads for an industrial manufacturer.
Project Architecture & Case Study
The Challenge & Scope
Manual discovery of target manufacturing facilities (chickpea roasters, nut processors, coffee roasters, dragée producers) across industrial zones was time-consuming. Search engine results were heavily polluted by retail shops and cafes, and duplicate records hindered the outreach workflow.
Technical Architecture & Solution
I designed a targeted data pipeline combining Python, open-source harvesting tools, and map API endpoints. Configured targeted query batches across 14 major manufacturing hubs. Engineered rule-based exclusion filters for retail entities and a multi-key deduplication algorithm merging place_id, CID, and normalized phone numbers.
Results & Key Deliverables
Candidate enterprises across designated industrial clusters were deduplicated, filtered, and formatted into clean UTF-8 Excel (with BOM) files. The sales team's prospect research shifted from days of manual searching to an automated, structured data flow.
About the Project
An end-to-end data automation pipeline engineered for an industrial food processing machinery manufacturer to streamline B2B lead discovery and sales outreach. The system scans designated industrial zones, eliminates retail noise with rule-based filters, and outputs clean Excel/JSONL datasets ready for the sales team.
Architected, designed, and engineered entirely by Ahmet Mert Yiğitbaşı.
Technologies
- Python 3.10
- Data Pipeline
- Google Maps API
- Pandas
- Regex & Data Cleaning
- JSON Lines & Excel Export