Practical/Lab – How to work with dataforSEO for SERP collection – Lesson Preview
This hands-on lab shows you exactly how to collect, merge, and analyze Google SERP data, the backbone of advanced semantic keyword research. You’ll learn two proven methods for scraping search results: a no-code workflow using SERP API and a scalable Python-based process with DataForSEO’s API.
The lesson walks you through setting up both approaches safely and efficiently, understanding Google’s guidelines on SERP scraping, and transforming the exported data into structured, analysis-ready files. By the end, you’ll know how to extract rich search insights such as who ranks for your target keywords, which SERP features dominate your space, and how search intent connects to content types and formats.
Whether you’re analyzing 100 keywords or 100,000, this session helps you move from raw search data to clear, actionable intelligence, turning the SERP into a strategic research tool.
What you’ll learn (why it matters)
- Collect SERP data at scale — because large datasets reveal ranking and feature trends.
- Use SERP API and DataForSEO — because each offers flexible, cost-effective ways to access Google results.
- Work with APIs and Python scripts — because automation saves hours of manual collection.
- Merge and clean SERP data — because structured files power better keyword analysis.
- Identify SERP patterns and competitors — because visibility and content type insights inform smarter strategy.
- Visualize search data effectively — because clear visuals turn complexity into insights.
Key concepts (with mini-definitions)
- SERP Scraping —extracting structured data (results, features, snippets) from Google’s search results.
- SERP API — a lightweight Google Search API that allows up to 100 monthly queries for free.
- DataForSEO API — a professional-grade API for large-scale SERP collection with flexible task management.
- No-Code Method — using prebuilt tools or templates to scrape data without programming.
- Batch Processing — submitting and retrieving keyword groups in segments for efficiency.
- SERP Features — special result types like snippets, videos, or AI overviews on Google.
- Share of Voice / Search — a measure of how often your domain appears in search results compared to competitors.
- Entity and Sentiment Analysis — evaluating mentions and tone in titles and descriptions to assess ranking patterns.
Tools mentioned
SERP API, DataForSEO API, Python, BigQuery, Excel, Sublime Text and Looker Studio.
Practice & readings
- Recreate the SERP API no-code workflow using the free unofficial app and merge files with the provided Python script.
- Use the DataForSEO Google SERP template to post and retrieve 200 keyword batches.
- Optional: Explore Lazarina’s Machine Learning-Enabled SERP Analysis blog post and Looker Studio template.
Key insights & takeaways
- Working directly with APIs delivers richer, cheaper data than paid wrappers.
- Automating SERP collection saves time and improves consistency.
- Google’s own results reveal intent, entity relevance, and ranking logic.
- Combining technical and no-code methods bridges skill gaps across teams.
- Clean, organized SERP data underpins advanced semantic keyword strategies.
Ready for the next step? Start your learning journey with MLforSEO
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Learn how to large-scale SERP collection and analysis and save hours of manual keyword research.