Generate Leads from Google Search with n8n & Scrappa

Transform Google search results into qualified leads automatically with this powerful n8n workflow that leverages Scrappa's advanced search capabilities to build comprehensive prospect databases from organic search data.

What this workflow does

This automated lead generation system streamlines your prospecting process through three key steps:

• Executes targeted Scrappa Google Search queries based on your specific prospecting criteria
• Normalizes and structures the top organic search results into organized data rows containing rank, title, URL, snippet, and source query information
• Outputs clean, actionable lead data ready for integration with Google Sheets, Airtable, HubSpot, or your existing CRM system

Use cases

Perfect for sales teams, marketing professionals, and business development specialists who need to:

• Build targeted prospect lists for specific niches, geographic locations, or industries
• Identify companies showing buyer intent through their online presence and search visibility
• Research competitors and market opportunities in specific verticals
• Create comprehensive lead databases for outbound sales campaigns
• Automate the manual process of collecting and organizing search-based prospect research

Technical details

This n8n workflow utilizes the following components:

• n8n-nodes-scrappa: Community node for Google search automation and data extraction
• Code node: Custom logic for data processing and normalization
• Manual trigger: On-demand workflow execution for flexible lead generation timing

Setup requires installing the n8n-nodes-scrappa community node, configuring your Scrappa API credentials within n8n, and customizing the search query parameters to match your target market, geographic focus, or buyer-intent keywords. The workflow outputs structured lead data that seamlessly integrates with popular CRM platforms and spreadsheet applications.

Ideal for automation engineers and SaaS operators looking to scale their lead generation efforts while maintaining data quality and relevance.