SEO testing is a powerful way to drive organic performance—but it’s rarely quick. From pulling massive datasets to identifying opportunities, implementing changes, and tracking results, the process is time-consuming, especially at scale.
That’s where AI comes in. By weaving AI into SEO testing workflows, we can automate the tedious parts, speed up data analysis, and boost efficiency without compromising accuracy.
If you’re looking to make your SEO testing faster, more scalable, and a lot more manageable, AI can help you get there.
The Role of AI in SEO Testing
SEO testing usually follows a four-step process:
Collect data to identify opportunities
Form a hypothesis based on insights
Implement changes
Measure impact
AI can enhance each stage—from automating data pulls to generating test ideas and improving how we report on outcomes.
Let’s break down how AI fits into each step of the SEO testing workflow.
A Note on Data Privacy
Before diving in, a critical caveat: data privacy.
Large language models (LLMs) like ChatGPT can theoretically retain input data, which makes directly inputting client or proprietary SEO data risky. For SEOs handling sensitive keyword sets or internal analytics, this is a deal-breaker.
Our solution? Use ChatGPT to generate code, not process data.
For example, we use ChatGPT to write Python scripts that run in Google Colab. That way, the data stays local (in Google Drive or your machine), and ChatGPT never sees it. You get the benefits of AI without compromising privacy.
Automating Data Extraction & Filtering with AI
Sorting keyword rankings, traffic metrics, and performance data from platforms like GSC and GA4 can eat up hours. Spreadsheets quickly become bottlenecks.
AI streamlines this. With ChatGPT, you can generate custom Python scripts that:
Filter keywords by position (e.g., 4–10 striking distance terms)
Segment keywords by intent, SERP features, or site structure
Create data visualizations to surface patterns faster
The best part? Even if you don’t code, you can still get started. Ask ChatGPT to generate the script, plug it into Google Colab, and go.
Here’s how to do this step by step.
Step-by-Step AI Workflow for Data Extraction
1. Export Your Data
Pull keywords and rankings from your SEO tools and export as a UTF-8 CSV to avoid encoding issues.
2. Set Up Google Colab
Instead of filtering manually in Sheets, use AI to generate a Colab notebook. For example, let’s say you want to isolate striking distance keywords. Here’s how your prompt can look like:
3. Filter for Striking Distance Terms
Ask ChatGPT to write a script that filters keywords ranking between positions 4–10. You can adjust the range later depending on your use case. Compared to manually sorting spreadsheets, this is much faster, especially at scale.
4. Layer on Additional Filters
Want keywords with “Local” intent with rankings between 4 and 10? Just add a filter condition in the script.
You can ask ChatGPT to help modify or stack filters as needed.
5. Visualize Keyword Opportunities
Instead of scrolling through rows, let AI create charts that show ranking distribution by URL, subfolder, or keyword cluster.
If we generate a script to extract the subfolder from URLs, we can then ask ChatGPT to help us come up with a code to visualize this data by subfolder.
Visuals help both SEOs and clients quickly grasp where the opportunities lie.
6. Export the Refined Dataset
Once you’ve isolated high-impact terms, export your final list for optimization or testing.
This workflow alone can shave hours off your week—and help you focus on what actually moves the needle.
Additional Tools & Troubleshooting with AI
You’re not limited to keyword filtering. You can also leverage existing libraries and tools to enhance your SEO testing workflows:
Spelling correction (e.g., JamSpell or PySpellChecker)
Data wrangling (e.g., fixing encoding or cleaning messy exports)
Troubleshooting script errors
If a tool like JamSpell doesn’t work in Colab, ask ChatGPT - or even try another model like Claude. When JamSpell gave us issues, Claude suggested switching to PySpellChecker, and it worked perfectly for spotting typos in our keywords.
Generating Hypotheses & Test Ideas with AI
Once you've extracted and filtered your data, the next step is ideation—turning insights into actionable tests. Here are two ways you can use AI to spark test ideas and streamline the ideation process:
Using SEOTesting.com’s ChatGPT Integration
SEOTesting.com integrates ChatGPT directly into its platform to help generate:
Title tag and meta description suggestions
H1 alternatives
Content and subtopic ideas
For example, if a page is ranking for “Bend Oregon SEO”, ChatGPT might suggest turning that into a case study or long-form content hub.
And if the default suggestions don’t quite hit, you can refine the prompts for more tailored recommendations:
The integration makes brainstorming faster and lets you track performance post-implementation.
Using Microsoft Clarity CoPilot for UX Test Ideas
Beyond metadata and topic ideas, AI can uncover potential UX-related test ideas too. Microsoft Clarity’s CoPilot integration allows SEOs to:
Summarize insights from heatmaps and session recordings: CoPilot can process up to 10 recordings at a time to highlight common navigation patterns, click behavior, and friction points.
Identify frustration events: AI pinpoints rage clicks, excessive scrolling, and other signals of poor UX that might impact SEO performance.
Chat with AI to analyze and brainstorm: Users can ask Clarity’s CoPilot, “What are the most common frustration events on this page?” and receive actionable insights.
You can use this data to help come up with page layout tests, internal link tests, and even content refreshes.
Implementing Changes with AI Assistance
While AI can’t (and shouldn’t) automate SEO test implementation entirely for you, it can speed things up. Here are a couple of tools to consider for this:
GPT for Sheets
GPT for Sheets is an extension that integrates ChatGPT directly into Google Sheets, allowing you to automate tasks such as writing metadata or optimizing product descriptions at scale.
Custom GPTs
You can use custom GPTs to help you with other types of testing as well. Schema Advisor, for example, can help you build or validate structured data, generate schema ideas, or troubleshoot markup issues.
Streamlining SEO Test Reporting with AI
Even the most insightful SEO tests can fall short if their results aren’t clearly communicated. This is where AI can step in, not just to streamline reporting, but to enhance how we deliver insights.
AI can automate summaries, generate effective data visualizations, and format reports in a way that’s tailored for different stakeholders. Not every stakeholder needs the same level of technical depth. You can ask AI to adapt the language and detail of insights depending on your audience, whether it's an exec who wants a high-level summary or a marketing manager who needs to dig into the numbers.
Rather than manually compiling data and struggling to structure it, you can use AI to produce clean, actionable reports with key takeaways front and center.
You could set up a Google Colab notebook to handle the data wrangling and report formatting. Or, develop a flexible report template that organizes your findings clearly, both visually and narratively. The point is to focus less on repetitive formatting and more on the insights themselves.
If you aren't sure how to choose the right charts or visualizations, AI can help. Just prompt it directly with something like:
“I need to create a report on Microsoft Clarity data for the marketing team of the site I’m working on. They’ll be sharing this internally, so it needs to be clear and easy to pass along. I'll be reporting on Sessions, Pages per Session, Scroll Depth, Time Spent, Dead Clicks, and Quick Backs. How can I format this report to make the most sense to both teams? What visualizations should I include?”
AI can suggest the best ways to visualize each metric, helping you present your test results in a way that makes sense to technical and non-technical audiences alike.
Final Thoughts
AI is transforming how we approach SEO testing by automating data extraction, improving test hypothesis generation, and streamlining reporting. However, AI should be used strategically, not as a replacement for human expertise.
AI is a tool, not a strategy. Use it to enhance your workflow, not override your expertise. Always validate its output, and remember: the insights and context you bring to the table are what actually drive results.
When used strategically, AI can take your SEO testing from tedious to transformative.
Article by
Celeste Gonzalez
Celeste Gonzalez is a recognized thought leader in local SEO and testing. As the Director of RooLabs, the experimental SEO testing division at RicketyRoo, she pioneers data-driven strategies that push the boundaries of "traditional SEO." A passionate advocate for advancing the SEO industry, Celeste is a writer, speaker, and judge for the 2024 Search Engine Land Awards. Her expertise extends to content marketing, UX optimization, and split testing. In addition, Celeste serves as a Course Creator for the Local SEO curriculum on the Wix SEO Hub, empowering businesses and professionals with actionable strategies for local search success.
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