Platform
Google Maps lead lists — from places search to spreadsheet
Local lead generation is a volume game: categories, cities, radii and niches. Dataspana helps you operationalize Maps-based prospecting by exporting structured rows you can call, email and enrich.
Why teams use Dataspana for Google Maps
- Great for agencies building repeatable vertical + geo lists
- Export-friendly columns for ops and outbound tooling
- Works alongside your existing enrichment stack
What you can extract from Google Maps
Columns vary by workflow; this reference lists fields teams commonly export for spreadsheet workflows.
| Field | Type | Description |
|---|---|---|
| place_id | Text | Stable identifier for the listing when available. |
| business_name | Text | Public name shown on Maps. |
| primary_category | Text | Top-level category label. |
| secondary_categories | Text | Additional category tags. |
| full_address | Text | Formatted address string. |
| street | Text | Street line when split fields exist. |
| city | Text | City component. |
| region | Text | State / province component. |
| postal_code | Text | ZIP or postal code. |
| country | Text | Country name or ISO code. |
| latitude | Number | Decimal latitude. |
| longitude | Number | Decimal longitude. |
| phone | Text | Published phone number if shown. |
| website | URL | Business website URL when listed. |
| rating | Number | Average star rating. |
| review_count | Number | Total reviews displayed. |
| hours_summary | Text | Open hours text or structured snippet. |
| maps_url | URL | Link back to the Maps listing. |
| search_territory | Text | City or query context used for the job. |
How to scrape Google Maps in three steps
Open the Google Maps workflow, paste your URLs or parameters, then confirm the preview column layout. Run the job when the estimated credit count matches your budget. Download CSV, Excel, or JSON and load the file into Sheets, Snowflake, or your outbound sequencer.
Use cases
Lead generation
When teams operationalize operations on Google Maps, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. Google Maps workflows reward repeatable queries because downstream CRM hygiene depends on stable keys. Dataspana keeps the interface form-driven so operators can delegate reruns without engineering tickets. In practice, iteration speed matters more than perfect coverage on day one: ship a first-pass list, enrich, then return for a second pass with tighter filters. That pattern reduces wasted credits and keeps datasets aligned with the business question rather than the scraper mechanics. Slice 1 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review. Slice 2 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review. Slice 3 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review.
Competitive research
When teams operationalize operations on Google Maps, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. Google Maps workflows reward repeatable queries because downstream CRM hygiene depends on stable keys. Dataspana keeps the interface form-driven so operators can delegate reruns without engineering tickets. In practice, iteration speed matters more than perfect coverage on day one: ship a first-pass list, enrich, then return for a second pass with tighter filters. That pattern reduces wasted credits and keeps datasets aligned with the business question rather than the scraper mechanics. Slice 2 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review. Slice 3 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review. Slice 4 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review.
Recruiting and talent sourcing
When teams operationalize operations on Google Maps, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. Google Maps workflows reward repeatable queries because downstream CRM hygiene depends on stable keys. Dataspana keeps the interface form-driven so operators can delegate reruns without engineering tickets. In practice, iteration speed matters more than perfect coverage on day one: ship a first-pass list, enrich, then return for a second pass with tighter filters. That pattern reduces wasted credits and keeps datasets aligned with the business question rather than the scraper mechanics. Slice 3 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review. Slice 4 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review. Slice 5 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review.
Market analysis
When teams operationalize operations on Google Maps, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. Google Maps workflows reward repeatable queries because downstream CRM hygiene depends on stable keys. Dataspana keeps the interface form-driven so operators can delegate reruns without engineering tickets. In practice, iteration speed matters more than perfect coverage on day one: ship a first-pass list, enrich, then return for a second pass with tighter filters. That pattern reduces wasted credits and keeps datasets aligned with the business question rather than the scraper mechanics. Slice 4 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review. Slice 5 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review. Slice 6 also highlights how analysts compare operations signals across accounts, merge with territory spreadsheets, and document assumptions for compliance review.
Pricing for Google Maps scraping
Dataspana uses credit-based pricing that applies across sources, including Google Maps. Start with 100 free credits, then upgrade when volume grows. Review the live pricing section on the homepage or open the dedicated pricing page for plan tiers.
Google Maps scraping compared to alternatives
Below is a qualitative snapshot comparing Dataspana with Apify, PhantomBuster, Bright Data for typical no-code marketing workflows.
| Product | No-code | Pricing model | Supported workflows | Export formats |
|---|---|---|---|---|
| Dataspana | Form-first UI, minimal setup | Credits / bundles aligned to rows | Broad marketplace-style sources | CSV, XLSX, JSON |
| Apify | Strong for developers; no-code exists | Subscription + usage | Large actor marketplace | CSV, JSON, many integrations |
| PhantomBuster | Flow automation focus | Subscription tiers | Social + growth automations | CSV primarily |
| Bright Data | Enterprise proxy + datasets | Enterprise contracts | Web data at scale | Many enterprise formats |
Legal and compliance
You are responsible for complying with Google Maps terms, GDPR, CCPA, and any sector rules that apply to your accounts. Use Dataspana only for data you have the right to collect, store, and process.
Dataspana does not provide legal advice; involve counsel when launching new geographies or regulated industries.
Contextual guides across Dataspana
If you are building multi-channel lists, combine this google maps export with related playbooks: LinkedIn scraper, Google Search / SERP, lead generation, market research, Instagram scraper.
Get started with Google Maps scraping
Create a free account, run a small pilot job, and validate the columns with your downstream owners before scaling.
Operational addendum 1 for Google Maps: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 2 for Google Maps: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 3 for Google Maps: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 4 for Google Maps: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 5 for Google Maps: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 6 for Google Maps: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Related solutions
Frequently asked questions
- What is a Google Maps scraper used for?
- Teams use Maps exports to build outbound lists, audit categories, compare operators across cities and monitor local markets.
- Can I use this for multi-city prospecting?
- Yes — many teams run separate jobs per city/vertical and merge spreadsheets, then dedupe in their CRM or data tooling.
- Do I need developers?
- No. Dataspana is designed for non-technical users: pick Google Maps, complete the form, download results.