Manually checking a handful of competitor sites every week for SEO regressions or a tech-stack change (a new CMS, a new analytics tool, a new payment provider showing up) doesn't scale, and it's exactly the kind of repetitive check that should just run itself. Here's how to automate it end-to-end with n8n, without writing a scraper.
What "monitoring a competitor" actually requires
Two signals matter most for competitive/SEO monitoring, and they're both annoying to get reliably on your own:
- An SEO health score — are they missing meta descriptions, is their robots.txt blocking something it shouldn't, do they have broken canonical tags. This normally means building (or paying for) an auditing tool.
- Detected tech stack — did they switch from Shopify to a custom storefront, did they add a new analytics or A/B testing tool, did they start using a new CMS. This normally means either manually inspecting page source/headers, or maintaining your own library of technology fingerprints (script src patterns, meta generator tags, cookie names) — which is a genuinely large, constantly-changing dataset to keep current.
Both of these are solved problems as external APIs, which means the actual automation work is just: read a list of URLs, call an API, compare to yesterday's result, alert on meaningful changes.
The n8n workflow
The shape of this workflow is simple enough to build in an afternoon:
- Schedule Trigger — once a day.
-
Google Sheets (Read) — a
Competitorstab with columnsurl,last_score,last_stack. - Loop over each row.
- HTTP Request (SEO audit) and HTTP Request (tech stack) — two parallel calls per URL.
- Merge the two responses.
-
Compare the new score/stack against
last_score/last_stackfrom the sheet — flag if the score dropped more than a threshold (5 points is a reasonable default) or the detected stack changed at all. - IF node — only continue if something actually changed.
- Slack/email alert (optional) — post the diff.
- Google Sheets (Update) — write the new score/stack back for tomorrow's comparison.
That's the entire scenario. No scraper code, no maintaining your own tech-stack fingerprint database, no parsing HTML yourself.
The part people usually get wrong: comparing state correctly
The easy mistake is comparing the current result to some fixed baseline instead of the previous day's result — which either misses gradual regressions (a score that drops 2 points a day for a week is a 14-point drop nobody notices) or floods you with noise on volatile pages. Reading last_score/last_stack from the same row you're about to update, comparing, then overwriting, is what makes this a rolling day-over-day diff instead of a one-time snapshot.
For the tech-stack comparison specifically, sort the detected technology list before comparing (sorted(stack).join(',') or equivalent) — an API returning the same technologies in a different order between runs will otherwise look like a "change" when nothing actually changed.
Where the API calls come from
For the two HTTP Request nodes above, you need something that returns both an SEO audit score and a tech-stack fingerprint from a single URL — the Web Metadata & Contact Extractor API covers both endpoints (/api/v1/seo-audit and /api/v1/tech-stack) as part of its free tier (1,000 requests/month), which is enough headroom to monitor a meaningful watchlist of competitors daily without hitting limits.
I've published this exact workflow — Google Sheets read, parallel API calls, day-over-day comparison, Slack alert, sheet update — as a ready-to-import n8n template, so you don't have to wire the nodes up from the description above. Drop in your own RapidAPI key and Google Sheet, point it at a Competitors tab with a url column, and it runs daily on its own.
The API itself is open source (MIT license) if you'd rather self-host the extraction logic instead of using the hosted version — the n8n workflow works the same either way, you'd just point the HTTP Request nodes at your own instance.
