Platform
SERP and Google Search exports for research and monitoring
Whether you track queries, capture SERP features, or build research datasets, Dataspana turns search inputs into downloadable structured tables.
Why teams use Dataspana for Google Search
- Useful for SEO teams, analysts and growth operators
- Turn repetitive manual SERP copying into repeatable jobs
- Export to CSV/Excel for reporting and dashboards
What you can extract from Google Search
Columns vary by workflow; this reference lists fields teams commonly export for spreadsheet workflows.
| Field | Type | Description |
|---|---|---|
| query | Text | The submitted search string. |
| rank_absolute | Number | Position within the full SERP export. |
| rank_group | Number | Position within a feature block when applicable. |
| result_type | Text | organic, ad, local_pack, people_also_ask, etc. |
| title | Text | Blue link title text. |
| url | URL | Destination URL for the result. |
| domain | Text | Hostname extracted for pivot tables. |
| snippet | Text | Visible description snippet. |
| sitelinks | Text | Indented links when present. |
| people_also_ask | Text | Related questions block as text. |
| thumbnail_url | URL | Preview image URL when captured. |
| date_hint | Text | Freshness hints for news-like results. |
| language | Text | Detected language code if available. |
| device_context | Text | Mobile vs desktop context metadata. |
| serp_feature | Text | Feature bucket for reporting. |
| brand_mention | Text | Optional flag for tracked keywords. |
| competitor_overlap | Text | Optional join key for multi-query studies. |
| export_batch_id | Text | Job identifier for reproducibility. |
How to scrape Google Search in three steps
Open the Google Search 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 Search, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. Google Search 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 Search, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. Google Search 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 Search, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. Google Search 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 Search, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. Google Search 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 Search scraping
Dataspana uses credit-based pricing that applies across sources, including Google Search. 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 Search 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 Search 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 search export with related playbooks: LinkedIn scraper, Google Maps scraper, market research, competitor monitoring, web scraping ROI calculator.
Get started with Google Search 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 Search: 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 Search: 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 Search: 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 Search: 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 Search: 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 Search: 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
- Is this a rank tracker?
- Dataspana focuses on extracting structured SERP datasets from searches you configure. Many teams export results and join them with their own ranking logic.
- Can I export to Excel?
- Yes — CSV, XLSX and JSON exports are supported.
- Who is this for?
- SEO teams, researchers, agencies and founders who need repeatable Google Search datasets without maintaining scrapers.