Prompt

What should I use to compare technical SEO audits before and after a migration?

Technology / SEO & AEO Tools1 observationLast seen Oct 21, 2026

Latest observation

Oct 21, 2026OpenAI APIWeb search: off

To compare technical SEO audits before and after a migration, use a structured audit framework with the same checklist, same crawl settings, and the same data sources on both sides.

Best approach

Use a combination of:

  1. A crawl tool
    Examples: Screaming Frog, Sitebulb, JetOctopus, Deepcrawl/Lumar
    Use it to compare:

    • indexability
    • status codes
    • redirects
    • canonicals
    • metadata
    • internal links
    • duplicate content
    • hreflang
    • sitemap coverage
  2. Google Search Console data Compare:

    • indexed pages
    • coverage/indexing errors
    • page-level performance
    • crawl stats
    • sitemaps
    • manual actions/security issues
  3. Analytics / log files To spot:

    • traffic drops
    • crawl behavior changes
    • bot access to old vs new URLs
  4. A before/after issue tracker or spreadsheet This is the most important part for comparison. Track each issue with columns like:

    • issue type
    • affected URL count
    • severity
    • pre-migration status
    • post-migration status
    • notes / fix applied

What to compare

Make sure both audits evaluate the same things:

  • crawlability
  • indexability
  • redirect mapping
  • canonical correctness
  • robots.txt rules
  • XML sitemap validity
  • internal linking depth
  • page speed / Core Web Vitals
  • structured data
  • hreflang
  • duplicate titles/meta
  • orphan pages

Recommended method

If you want the cleanest comparison, use:

  • Screaming Frog exports before and after
  • then compare in Excel/Google Sheets
  • or use a dashboard in Looker Studio / Power BI if you want ongoing reporting

Simple rule

Don’t compare two audits done with different tools or different crawl settings—that will make the results unreliable.

If you want, I can give you a migration SEO audit comparison template you can use in Sheets.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

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