Suganthan Mohanadasan

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I spent 48 hours stress testing the new Claude Analysis Tool to see if it could replace my Python scripts for SEO forecasting.

The results were not what I expected.

I gave it 2 years of raw click data from a declining e-commerce site. No cleaning. No formatting. Just a messy CSV.

Find the point of failure and forecast the recovery.

Claude didn't just plot a line. It identified a specific cannibalisation issue between the category pages and the new blog subfolder that started exactly 14 weeks ago.

It wrote the Python code, ran the regression, and visualized the overlap in a scatter plot.

→ 14% traffic loss attributed to internal competition.

→ 3 specific URL clusters identified as the culprits.

→ A 90-day recovery model based on de-optimising the blog posts.

The scary part? It took 45 seconds. My manual script takes 20 minutes to configure and another 10 to debug the data types.

But there is a catch.

If you don't prompt it to check for seasonal outliers, it ignores them entirely. It saw a "growth trend" in November that was actually just Black Friday noise. It’s a calculator, not a consultant.

We are moving into an era where the "how" of data analysis is becoming free. The value is now entirely in the "what" and the "why."

If you aren't using LLMs to crunch your GSC exports yet, you are working 10x harder for the same insight.

I wrote a breakdown of the 5 prompts I used to get these visualisations.

Link in the comments 👇

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