What's inside our Australian restaurant database — a sample walkthrough

If you're considering one of our packs, here's what you actually receive — and what to do with it.

What's in the ZIP

Every paid pack ships as a single ZIP containing:

• A CSV in UTF-8 with a header row
• An XLSX with the same columns plus a second sheet describing the schema
• A README.txt with sourcing notes and the verification cycle

The Australia master pack additionally includes a JSON file with the same data, useful for importing into APIs or NoSQL stores.

The schema

Every record contains the following fields:

| Column | Description | |---|---| | business_name | Trading name as displayed publicly | | legal_name | Registered entity name (where available) | | street_address | Street address | | suburb | Suburb | | state | Two-letter state code (NSW, VIC, etc.) | | postcode | 4-digit Australian postcode | | phone | Primary phone (E.164 where available) | | email | Publicly listed email — may be null for venues that don't publish one | | website | Primary website URL | | cuisine | Primary cuisine / category | | cuisine_secondary | Secondary cuisine, where applicable | | latitude | Decimal latitude | | longitude | Decimal longitude | | google_rating | Average Google rating (0–5) | | review_count | Number of Google reviews at time of verification | | last_verified | ISO date of last verification |

The verification process

Every quarter, our pipeline:

1. Re-scrapes publicly listed sources for each record
2. Cross-references the result against at least two independent sources
3. Flags records where any of (phone, address, website) has changed
4. Removes records where the venue can no longer be verified as trading

Records that fail verification are marked inactive rather than deleted, so you can compare quarter-to-quarter churn if you have an existing snapshot.

Loading it into a CRM in five minutes

The CSV is shaped to be import-friendly. Pipedrive, HubSpot, Close, Attio, Salesforce — all accept this format directly.

A typical first import:

1. Create a custom object called Venue (or repurpose Company).
2. Map columns: business_name → name, email → primary email, phone → primary phone, website → website, suburb + state → location.
3. Use cuisine as a tag or pick-list field — this is what you'll segment campaigns on.
4. Set last_verified as a custom date field so you can filter out stale records before sending.
5. Run a deduplication pass against your existing customer list.

Want to try it first?

Download a free 25-row sample — same schema, real Australian venues, sent to your inbox in seconds.