How to Use AI to Do Cross-Dataset SEO Analysis of Your Google Search and YouTube Data

I always thought creating a lot of content was the key to SEO but I’ve had blogs with over 100+ posts that have not gotten me anywhere with SEO. Turns out, I need to write the right articles with low competition keywords with high volume. The research behind the post is more important than writing content. But the research takes time!

In the ever-evolving world of digital marketing, understanding your website’s performance across multiple platforms has become increasingly complex. As someone who manages websites with hundreds of blog posts, I’ve struggled with the same question many content creators face: “How do I know what content to create next, and which existing content to improve?”

The answer, I’ve discovered, isn’t about creating more content—it’s about having better insights into your existing data. And the breakthrough came when I started using AI to perform cross-dataset analysis of my Google Analytics, Search Console, YouTube, and other performance metrics.

Get my prompts for cross data set analysis here!

Why Traditional SEO Analysis Falls Short

For years, I approached SEO analysis the conventional way: examining individual data sources in isolation. I’d look at Google Analytics for user behavior, Search Console for keyword performance, and YouTube Studio for video metrics. This approach provided basic insights:

• Which pages get the most traffic
• What keywords bring visitors to my site
• How long people watch my videos
• Which countries my visitors come from

But these siloed insights were limited. They didn’t reveal the interconnections between different platforms or help me understand the complete user journey. Most importantly, they didn’t answer my fundamental question: “What content should I focus on next?”

The Power of Cross-Dataset Analysis with AI

The game-changer was when I started using AI-powered code editors not for coding, but for analyzing my marketing data. Tools like Windsurf, Cursor, and similar AI editors can process multiple CSV files simultaneously and identify patterns across datasets that would be nearly impossible to spot manually.

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Here’s what cross-dataset analysis revealed that single-dataset analysis couldn’t:

Content Format Preferences Across Platforms

By comparing my YouTube and website data, I discovered that certain content formats perform dramatically differently depending on the platform:

• YouTube audiences prefer shorter, how-to content with visual demonstrations
• Website visitors engage more with in-depth comparisons and longer-form content
• Some topics perform exceptionally well on both platforms, while others only shine on one

This insight immediately changed my content strategy. Instead of creating the same content for both platforms, I began tailoring formats to each platform’s audience preferences.

Traffic Source Quality Analysis

Cross-referencing Google Analytics with Matomo data (a privacy-focused analytics alternative) revealed that:

• YouTube visitors spent 43% more time on my site than visitors from other sources
• They viewed 2.1 more pages per session on average
• They were 3x more likely to subscribe to my newsletter

This insight led me to prioritize YouTube content creation, knowing it would drive not just more traffic, but higher-quality traffic to my website.

Content Gap Identification

By comparing Search Console impressions with YouTube search data, I identified topics where:

• People were searching for my expertise on both platforms
• I had content on one platform but not the other
• High-impression queries existed with no matching content

This “content gap” analysis became my content creation roadmap, ensuring I was creating content that already had proven demand.

How to Perform Your Own Cross-Dataset SEO Analysis with AI

Ready to gain these insights for your own content? Here’s a step-by-step guide to performing cross-dataset analysis using AI tools:

Step 1: Gather Your Data

First, you’ll need to export data from all relevant platforms:

Google Analytics:

1. Navigate to the reports you want to analyze
2. Click “Share this report” at the top right
3. Select “Download” and choose CSV format
4. Repeat for acquisition, engagement, and other relevant reports

Google Search Console:

1. Go to the Performance report
2. Adjust the date range (I recommend at least 3 months of data)
3. Click the Export button (↓) and select CSV
4. Export separate reports for Queries, Pages, Countries, and Devices

YouTube Studio:

1. Go to Analytics
2. Click “See more” for detailed reports
3. Use the export button to download CSV files for:
   • Reach and engagement
   • Audience demographics
   • Revenue (if applicable)
   • Traffic sources

Additional Data Sources (Optional):

• Matomo visitor logs
• Social media analytics
• Email campaign performance
• Heatmap data

The key is to gather diverse datasets that can reveal different aspects of your content performance. The more varied your data sources, the richer your insights will be.

Step 2: Analyze with an AI Editor

Once you’ve gathered your data, it’s time to let AI find patterns across your datasets:

1. Open an AI-powered code editor like Windsurf, Cursor, or similar
2. Create a new project or workspace
3. Drag and drop all your CSV files into the editor’s file explorer
4. Select all the files you want to analyze
5. Drag the selected files into the chat interface

Now comes the fun part—asking the AI to analyze your data. Start with a general prompt:

Analyze all the data from Google Analytics and Google Search Console and give me suggestions on how to improve traffic for my site.

This will give you basic insights similar to what you might get from traditional analysis. The real magic happens when you ask for cross-dataset analysis:

Can you uncover any insights by combining several data sets? Look for patterns across my Google Analytics, Search Console, and YouTube data.

Step 3: Ask Targeted Cross-Dataset Questions

To get the most valuable insights, ask specific questions that require the AI to compare data across platforms:

Content Performance:

Which topics perform well on both my website and YouTube? Which perform well on only one platform?

Audience Behavior:

Are users who come from YouTube engaging differently than those from Google Search? How do their behaviors compare?

Content Format Preferences:

Based on engagement metrics, what type of content format do my YouTube viewers prefer versus my website visitors?

Geographic Targeting:

Which countries show high engagement on YouTube but low website traffic? How can I better convert YouTube viewers from these regions?

Channel Synergy:

For blog posts that have companion YouTube videos, how does their performance compare to standalone blog posts?

Step 4: Actionable Strategy Recommendations

After analyzing the data, ask the AI to provide specific recommendations:

Based on this cross-dataset analysis, what are the top 5 content pieces I should create next? For each, should it be a blog post, YouTube video, or both?

Or:

Which existing content should I prioritize improving, and what specific improvements would have the biggest impact?

Step 5: Create a Professional Report (Optional)

If you need to present your findings to clients or team members, you can generate a professional-looking report:

1. Copy the AI’s analysis
2. Use a tool like v0.dev to turn the text into a visual report
3. Prompt it with something like: You are an SEO expert consultant. You have analyzed this data and created a report. Transform this report into a professional website for presenting to a client.

Real-World Examples: Cross-Dataset Insights That Changed My Strategy

Let me share some specific cross-dataset insights that completely changed my content strategy:

Finding 1: Platform-Specific Content Performance

By comparing YouTube and website data, I discovered my PDF extraction content was the #1 performer on both platforms. However, I also found that:

• My automation tool comparisons (n8n vs. Make vs. Zapier) performed extremely well on my website but had mediocre YouTube performance
• My quick tutorial videos on browser extensions performed exceptionally on YouTube but drove minimal website traffic

Strategy Change: I now create platform-optimized versions of each topic, with different formats tailored to each platform’s audience. For website content that performs well, I create companion YouTube videos focusing on visual demonstrations rather than comparisons.

Finding 2: Visitor Quality by Source

Cross-referencing Google Analytics, Matomo, and YouTube data revealed that YouTube visitors were my most valuable traffic source:

• YouTube visitors had an average session duration of 3:42 (compared to 1:58 for search visitors)
• They viewed 3.4 pages per session (vs. 1.7 for search visitors)
• They had a newsletter signup rate of 4.7% (vs. 1.3% for search visitors)

Strategy Change: I increased my YouTube production schedule from monthly to weekly, prioritizing it over blog content. I also added YouTube-specific calls-to-action sending viewers to specific landing pages designed for their interests.

Finding 3: Content Length and Engagement Correlation

Analyzing video length data alongside website engagement metrics revealed:

• YouTube viewers preferred videos between 7-12 minutes
• Website visitors engaged most with articles that took 5-7 minutes to read
• Long-form YouTube content (15+ minutes) resulted in higher website visit duration when users clicked through

Strategy Change: I restructured my content creation process to match these preferences, creating mid-length videos that direct viewers to comprehensive website resources.

Tools I Recommend for AI-Powered SEO Analysis

While I’ve mentioned several tools throughout this article, here’s a summary of what I recommend:

For Data Analysis:

• Windsurf – Great UI and provides detailed analysis across multiple file types
• Cursor – Strong for technical analysis with excellent parsing capabilities
• Claude – Excellent at understanding complex data relationships

For Data Collection:

• Google Analytics 4 – Your primary source of user behavior data
• Google Search Console – Essential for understanding search performance
• YouTube Studio – For all YouTube metrics
• Matomo – Provides privacy-focused analytics with more granular visitor data

For Report Generation:

• v0.dev – Turn text analysis into visual reports
• Pitch.com – Create professional presentations from your AI analysis

Conclusion: The Future of SEO Analysis is Cross-Platform and AI-Powered

The days of analyzing individual data sources in isolation are over. The most valuable insights come from understanding how your content performs across platforms and how different audience segments interact with your brand ecosystem.

By leveraging AI to perform cross-dataset analysis, you can:

1. Identify content gaps across platforms
2. Understand which content formats work best on each platform
3. Discover which traffic sources bring your most valuable visitors
4. Create platform-specific content strategies that maximize engagement
5. Focus your efforts on content with proven demand

The best part? This approach shifts your content strategy from guesswork to data-driven decisions. Instead of creating content and hoping it performs well, you’ll create content you already know your audience wants, in the format they prefer, on the platforms where they’re most likely to engage with it.

So gather your data, fire up an AI editor, and start asking questions that span across your datasets. The insights you gain will transform not just your content strategy, but your entire approach to growing your online presence.