Analytics Tracking: Build, Audit & Debug

Build, audit, or debug GA4, GTM, Google Ads, Meta, and consent tracking with production-ready GTM code and implementation checklists your team can ship.

Broken tracking costs more than no tracking: missing conversion parameters zero out GA4 revenue, double-fired events inflate metrics, and misconfigured pixels corrupt campaign data.

Who It's For

• SaaS and e-commerce marketing teams tracking signups, upgrades, and purchases
• Analytics leads auditing a setup that looks off but need help finding where it's broken
• Developers wiring GA4, GTM, ad pixels, or consent tracking for the first time
• Anyone whose GA4 revenue, conversions, or Google Ads ROAS don’t match reality

Three Ways to Use It

Build from scratch — event taxonomy, GA4 setup, GTM container outline, conversion config for Google Ads and Meta, EU consent mode, and a phased roadmap with owner and effort tags.

Audit existing tracking — 0–100 scored report across five dimensions. Every finding is P0/P1/P2 with exact fix, owner, and effort estimate.

Debug a specific issue — probability-ranked root cause list, such as AJAX checkout changes, GTM publish issues, consent shifts, or missing revenue parameters, then a layer-by-layer decision tree with copy-paste verification commands.

What You Get

• Tracking plan for GA4, GTM, Google Ads, Meta, and consent setup
• Probability-ranked root causes for broken events, low revenue, ROAS mismatches, and conversion gaps
• Copy-paste GTM code, dataLayer examples, and verification commands
• 0–100 audit score with P0/P1/P2 findings, owners, and effort estimates
• Phased implementation checklist your team can ship

Example Input / Output

Debug input

checkout_completed fires, but revenue is always $0 in GA4. Google Ads conversions are 40% higher.

Output

Root causes ranked by probability, copy-paste dataLayer fix, 14-item verification checklist, and proactive user_id gap flag.

Build input

Set up GA4 and GTM for my Next.js SaaS with Google Ads, Meta, and EU users on Cookiebot.

Output

13-event taxonomy, GTM container outline, conversion config, Consent Mode code block, and 3-week roadmap with owners.

What to Send

Describe what looks wrong, or just say what you want to set up.

Examples:

• “GA4 revenue looks too low”
• “Google Ads ROAS doesn’t match GA4”
• “Set up tracking for my Shopify store”
• “Audit our current GA4/GTM setup”

A single sentence is enough. Add your tech stack, ad channels, or consent tool only if you have them.

What Makes This Different

vs manual debugging

Without a structured approach, debugging tracking issues means guessing. This gives you a probability-ranked root cause list — so you fix the most likely cause first, not the last thing you changed.

vs generic analytics consultants

Consultants audit and recommend. This delivers copy-paste GTM code, dataLayer examples, verification commands, and a phased checklist — output your team can act on, not a report to interpret.

vs general AI

General AI can suggest what might be wrong, but rarely structures the work into a 0–100 scored audit, probability-ranked root causes, or a P0/P1/P2 finding list with owners and effort estimates.

Key Differentiators

• Probability-ranked root causes — not a generic checklist
• 0–100 audit score with P0/P1/P2 findings, owners, and effort estimates
• Copy-paste GTM code and dataLayer fixes — not just suggestions
• Covers GA4, GTM, Google Ads, Meta, and consent in one session