Platform
Export LinkedIn data to CSV or Excel — no code
Dataspana is built for teams that need LinkedIn data in a spreadsheet: prospect lists, hiring pipelines, account research and monitoring — without maintaining scrapers or integrations.
Why teams use Dataspana for LinkedIn
- Form-based jobs: paste URLs or run supported LinkedIn extraction workflows
- Structured columns you can filter, enrich and load into your CRM
- Pay for results — start with free credits
What you can extract from LinkedIn
Columns vary by workflow; this reference lists fields teams commonly export for spreadsheet workflows.
| Field | Type | Description |
|---|---|---|
| profile_url | URL | Canonical profile link when available. |
| full_name | Text | Display name as shown on the profile. |
| headline | Text | Professional headline / title line. |
| location | Text | City, region or country string when exposed. |
| current_company | Text | Primary employer shown on the profile. |
| current_title | Text | Role title aligned with the experience section. |
| about | Text | Long-form summary text when present. |
| industry | Text | Industry tag or inferred category. |
| connections | Number | Approximate network size if displayed. |
| followers | Number | Audience count for creators and public figures. |
| experience_json | JSON | Structured history of roles when exported. |
| education_json | JSON | Schools and degrees when exported. |
| skills | Text | Comma-separated or list-like skills field. |
| languages | Text | Languages listed on the profile. |
| profile_image_url | URL | Avatar URL when accessible. |
| company_domain | Text | Inferred domain from employer context. |
| last_activity_hint | Text | Lightweight freshness signal when available. |
| export_batch_id | Text | Internal job identifier for reconciliation. |
| source_query | Text | The input URL or query used to generate the row. |
How to scrape LinkedIn in three steps
Open the LinkedIn 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 LinkedIn, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. LinkedIn 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 LinkedIn, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. LinkedIn 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 LinkedIn, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. LinkedIn 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 LinkedIn, they usually start with a narrow hypothesis, validate the export shape, then widen inputs once stakeholders trust the columns. LinkedIn 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 LinkedIn scraping
Dataspana uses credit-based pricing that applies across sources, including LinkedIn. 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.
LinkedIn 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 LinkedIn 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 linkedin export with related playbooks: Google Maps scraper, Google Search / SERP, lead generation, market research, competitor monitoring.
Get started with LinkedIn scraping
Create a free account, run a small pilot job, and validate the columns with your downstream owners before scaling.
Operational addendum 1 for LinkedIn: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 2 for LinkedIn: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 3 for LinkedIn: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 4 for LinkedIn: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 5 for LinkedIn: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 6 for LinkedIn: archive the filter set, credit estimate, and approver alongside the CSV so future audits explain why each row was collected.
Operational addendum 7 for LinkedIn: 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
- Do I need to write code or manage API keys?
- No. Dataspana is a no-code workflow: pick LinkedIn, fill the form, run the job, then download your file.
- What file formats can I export?
- You can download results as CSV, Excel (XLSX) or JSON for downstream tools and automations.
- Is LinkedIn scraping allowed?
- You are responsible for complying with LinkedIn’s terms, applicable laws, and your internal policies. Use Dataspana only for data you have the right to collect and use.