Australian Building Supply Data Scrape
Budget: $30 – $250 AUD
I’m building a master database of every building-supply retailer in Australia and need a clean, comprehensive Excel sheet that I can drop straight into our system. For each shop, capture its exact business name, full street address, and reliable contact details (phone and, where available, email or website). I want the data to be section based on different states and cities.
Accuracy is critical because this file becomes the single source of truth for our internal database. I’m not asking for product-range information at this stage; the focus is strictly on solid location and contact data. Please pull from multiple public sources—Google Maps, business directories, industry associations, council registers, etc.—to ensure nationwide coverage and eliminate duplicates or outdated listings.
Deliverables
• One Excel workbook with a separate tab for raw data and, if helpful, a cleaned tab ready for import.
• Columns clearly labelled (Name, Address, Suburb, State, Postcode, Phone, Email/Website).
• No duplicate records; consistent formatting of phone numbers and addresses.
I’ll run a quick spot-check on a random sample of entries, so completeness and correctness will determine final acceptance. If you’ve tackled large-scale web scraping, especially for Australian retail sectors, I’d love to see a brief note on your approach and any relevant examples.
Accuracy is critical because this file becomes the single source of truth for our internal database. I’m not asking for product-range information at this stage; the focus is strictly on solid location and contact data. Please pull from multiple public sources—Google Maps, business directories, industry associations, council registers, etc.—to ensure nationwide coverage and eliminate duplicates or outdated listings.
Deliverables
• One Excel workbook with a separate tab for raw data and, if helpful, a cleaned tab ready for import.
• Columns clearly labelled (Name, Address, Suburb, State, Postcode, Phone, Email/Website).
• No duplicate records; consistent formatting of phone numbers and addresses.
I’ll run a quick spot-check on a random sample of entries, so completeness and correctness will determine final acceptance. If you’ve tackled large-scale web scraping, especially for Australian retail sectors, I’d love to see a brief note on your approach and any relevant examples.