Exporting and Analyzing ScreamingCAT Data in Sheets or Excel

A crawler is only as good as the data you can pull from it. Learn the best methods for ScreamingCAT export data to Sheets or Excel for deep analysis.

Why Your Export Workflow Matters (And Why Most Are Wrong)

Let’s be direct: a crawl without analysis is just a noisy log file. You’ve pointed our ridiculously fast Rust-based crawler at a domain and now you have a mountain of data. Congratulations. The job isn’t done; it has just begun.

The real value of a technical audit lies in your ability to interpret, manipulate, and present that data. This is where your process for ScreamingCAT export data becomes critical. Simply hitting ‘Export’ and emailing a giant CSV to a client is malpractice.

This guide will walk you through the correct, efficient, and scalable ways to get data out of ScreamingCAT and into the tools where real analysis happens: Google Sheets and Microsoft Excel. We’ll cover the fundamentals and then graduate to the advanced techniques that separate the pros from the amateurs.

If you’re just getting started and haven’t even run your first crawl, you might want to pause here. Go read our Quick Start guide to installing and configuring ScreamingCAT, then come back when you’re ready to play with your new dataset.

The Core of ScreamingCAT Export Data: Understanding Your Options

Before you can analyze anything, you need to get the data out. ScreamingCAT offers several export options, but for spreadsheet analysis, your world revolves around the CSV (Comma-Separated Values) format. It’s universal, lightweight, and every spreadsheet program on earth can handle it.

Within the ScreamingCAT interface, you’ll find ‘Export’ buttons everywhere. The main ‘Export’ button at the top gives you a complete dump of the main table view you’ve configured. However, the real power is in the tab-specific exports.

Don’t export everything at once. That’s a rookie mistake. A focused audit requires focused datasets. Need to analyze response codes? Go to the ‘Response Codes’ tab and export that specific report. Investigating title tags? The ‘Page Titles’ tab is your destination. This approach keeps your files manageable and your analysis clean.

• Internal Tab: Your primary export for all crawled URLs and their associated data points (status codes, indexability, word count, etc.).
• Response Codes Tab: Essential for quickly identifying and filtering for 301s, 404s, and 5xx server errors.
• Page Titles / Meta Descriptions Tabs: The bread and butter for on-page SEO. Export these to find missing, duplicate, or non-optimal lengths in bulk.
• H1 / H2 Tabs: Perfect for content structure analysis and identifying pages with missing or multiple H1 tags.
• Images Tab: A goldmine for finding large images, missing alt text, and broken image links.

From Raw CSV to Actionable Insights in Google Sheets

Google Sheets is the weapon of choice for many SEOs due to its collaborative nature and cloud-based convenience. Getting your ScreamingCAT export data into Sheets is straightforward: `File > Import > Upload`.

Once imported, the first step is to freeze the top row (`View > Freeze > 1 row`). This is non-negotiable. Scrolling through hundreds of columns without headers is a form of self-torture.

Now, the analysis can begin. Use the `Filter` function to isolate specific issues. For example, filter the ‘Indexability’ column to ‘Non-Indexable’ to see everything blocked by robots.txt, meta tags, or canonicals. Combine this with a filter on ‘Status Code’ for ‘200’ to find healthy pages that are unintentionally blocked from indexing.

For more advanced workflows, pivot tables are your best friend. You can quickly group issues by type. For instance, create a pivot table with ‘Status Code’ as the rows and ‘Count of URL’ as the values to get an instant summary of all response codes across the site.

Taming the Beast: Advanced ScreamingCAT Export Data in Excel

For massive datasets—we’re talking hundreds of thousands or millions of URLs—Excel often outperforms Google Sheets. Its secret weapon for handling a large ScreamingCAT export data file is Power Query.

Power Query (found under the ‘Data’ tab as ‘Get & Transform Data’) is an ETL (Extract, Transform, Load) tool built into Excel. Instead of manually cleaning your CSV each time, you can build a repeatable query that cleans, filters, and even merges data sources for you. It’s the key to scalable and error-free reporting.

Imagine you’ve exported ScreamingCAT’s ‘internal_all.csv’ and you have another CSV from Google Analytics with page-level traffic data. With Power Query, you can merge these two files by the URL column. This allows you to create reports that show, for example, all non-indexable pages that received organic traffic in the last 30 days—a critical issue to fix.

Forget VLOOKUP. It’s 2024. Use `XLOOKUP`. It’s faster, more flexible, and less prone to breaking if you insert a column. Your formula for enriching crawl data with traffic data might look something like this:

=XLOOKUP(A2, 'GA Data'!$A:$A, 'GA Data'!$B:$B, "No Traffic", 0)

The difference between a good SEO and a great SEO is their ability to efficiently manipulate data. Power Query is a force multiplier for data manipulation.

An Opinionated SEO Copywriter

Automating Your Reporting with Looker Studio

Spreadsheets are for analysis, but dashboards are for reporting. Taking your cleaned-up ScreamingCAT data from Google Sheets into Looker Studio (formerly Data Studio) creates a dynamic and shareable report for clients or stakeholders.

The process is simple. Use your Google Sheet as a data source in Looker Studio. Once connected, you can build visualizations that update automatically whenever you refresh the data in the Sheet.

This is perfect for creating ongoing site health dashboards. You can track the number of 404 errors over time, monitor the distribution of canonicalized vs. indexable pages, or visualize your internal linking structure with a Sankey chart. The possibilities are endless and far more impactful than a static spreadsheet.

Need inspiration for what to include in your dashboard? Take a look at the structure of our comprehensive SEO audit report template. Replicate those key sections in Looker Studio for a powerful, automated reporting solution.

Common Pitfalls and How to Avoid Them

Your workflow is only as strong as its weakest link. Many promising analyses die a slow death due to simple, avoidable mistakes. Here are the most common ones we see.

First, exporting unnecessary columns. A full crawl can have over 100 columns of data. If you only need URL, status code, and indexability, don’t export the other 97. It bloats your file size and slows down your spreadsheet. Configure your view in ScreamingCAT *before* you export.

Second, a lack of documentation. You create a brilliant pivot table and a dozen filters to uncover a critical insight. A month later, you can’t remember how you did it. Document your steps, either in a separate tab in your sheet or in a project management tool. Your future self will thank you.

Finally, treating every data point as equally important. A single 404 is not a catastrophe. A pattern of 404s in a key user journey is. Context is everything. Prioritize your findings based on business impact, not just the raw numbers in your export.