AI-ready digital analytics

AI can now answer questions about your website data. Whether the answers are right depends on the data underneath.

Google has built Analytics Advisor into GA4, and Google's official MCP server lets ChatGPT, Claude and Gemini query your analytics directly. Asking "why did conversions drop last week?" no longer needs an analyst to build a report.

The catch: an AI model reads your setup literally. Vague event names, undocumented conversions, duplicate tracking or personal data in URLs all become confident, wrong answers. And the questions your team types into these tools go to an AI vendor.

I get your analytics ready, so AI gives you answers you can trust, without creating a privacy problem.

What makes analytics "AI-ready"

Clean, well-described data. Clear event and parameter names, documented key events, consistent naming across properties, and custom definitions with descriptions an AI can understand.

Accurate tracking. No duplicate events, broken conversions or polluted referrals. AI won't question bad data; it will explain it.

Secure access. Read-only connections, the right accounts, and a clear view of which data goes to which AI vendor.

Context beyond GA4. Your most valuable questions span ad spend, CRM and sales data. That needs BigQuery and a documented data model AI can query.

What Meliorum offers

  1. AI-readiness audit: A review of your GA4 property and GTM container to assess how well an AI model can interpret them: naming, documentation, data quality, key events, and personal data that shouldn't be there. You get a scored report and a prioritised list of fixes.

  2. Provide data context and rules: Without the proper context for what matters to your business and what the dimensions and metrics mean, the AI will be prone to either giving you generic insights or just making stuff up. We can set up documentation for AI’s so they follow your processes, rules and goals.

  3. Secure AI connections: Setting up GA4 and GTM connections to the AI tools your team uses (including Google's MCP server) with read-only, least-privilege access. We run this setup across multiple accounts in my own practice.

  4. BigQuery data layer for AI: documented views and metric definitions over your GA4 export, joined with cost and CRM data where needed, so AI tools query consistent, agreed numbers rather than improvising.

  5. Team enablement: Practical sessions on asking good questions, checking AI answers against the source, and knowing when not to trust the output.

Why it matters now

  • Analytics Advisor is rolling out to GA4 properties. Your team may already be using it.

  • Early reviews flag errors. Testers have found Analytics Advisor describing GA4 features that don't exist, and the official MCP server struggling with longer conversations.

  • Data governance. Analytics Advisor's interface notes that chat activity may be used to improve the product. Knowing what's in your data before AI reads it is part of meeting your privacy obligations.

FAQs

  • Not to start. Analytics Advisor and the MCP server work on standard GA4 data. BigQuery matters when you want AI to combine sources or answer questions GA4's reports can't.

  • It can be, with read-only access, no personal data in GA4, and a deliberate choice of vendor. That's part of the setup.

  • No. It makes routine questions faster so analysts spend time on the work that needs judgement.