Hire an AI store campaign tracking and return reviewer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A GA4 score out of 100, with each fix and its click path
• A Meta tracking score out of 100, the server route that fits you and the steps to set it up
• A tagged link sheet and naming rules, so Shopify can match sales to each campaign
• A return ladder per platform and a score out of 100 for how far to trust each one

How it does the job well

• Agree the question and the decision it supports before analysing.
• Check the data first: date range, grain, missing values, duplicates and definitions.
• Compare like with like: same period length, definitions and segments.
• Separate what the data shows from what it might mean, and give confidence levels.

Mistakes it avoids

• Reporting a change without checking whether tracking or definitions changed.
• Treating correlation as cause.
• Averages that hide important segments or outliers.

What a good result looks like

An example, not your task: You are about to raise ad spend and the numbers in GA4, Meta and Shopify do not agree.

• GA4 check. A score out of 100, each setting marked pass or fail, and the click path to fix it.
• Meta tracking check. A score out of 100, the server route that fits you, setup steps, and duplicate and consent checks.
• Link sheet. Naming rules, one tagged link per channel and page, and the rules that match orders to a campaign.
• Return check. Each platform's return next to your store totals, the traps found, and a trust score out of 100.
• HAND-BACK SUMMARY. Each step's file, any step not run and why, and every MISSING line in one list.

How the work is checked

• Each step passed its own checks, or the run stopped at the step that failed and says why.
• The steps ran in order, and each one used the result of the one before.
• Nothing was invented to fill a gap. Every gap is a MISSING: line.
• Nothing was sent, published or paid for without a clear yes.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Check GA4. Checks the day-one settings, the store events and the thank-you page key event, and gives the click path for each fix. Menu names change, so confirm each on screen.
• Check Meta tracking. Compares an app, the Conversions API Gateway and server Tag Manager, then proves browser and server events share one ID so a sale counts once.
• Tag every link. Writes naming rules and a link sheet for each channel, plus the rules that let Shopify's marketing area match orders to a campaign.
• Compare with your store. Puts each platform's reported return next to your own new-customer revenue, finds why they differ and says how much to trust each channel.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id set-up-tracking-you-can-trust-before-you-scale only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/set-up-tracking-you-can-trust-before-you-scale/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• How GA4 is installed (Shopify Google channel, an app, Tag Manager or custom code) and which domains customers pass through
• Your GA4 settings and the events you can see in its live view
• How Meta events are sent now, and who can build changes
• Your campaign names, dates, channels and the page each link points to
• After two weeks, platform ROAS and store totals for the same dates

Tools and access

• Google Analytics 4: The property being checked. Admin, DebugView and Realtime are used.
• Google Tag Manager: Its preview mode shows whether a tag fired before you publish.
• Meta Events Manager: Shows browser and server events, event IDs, the test events screen and event match quality.
• Google Tag Manager (web and server): Preview mode shows whether tags fired, and the server container shows the tag status.
• Shopify marketing area: Where the match rules, activities and attribution models are set.
• Google Analytics 4: A second view of the same tagged traffic.
• Any good chat AI: Any current chat AI can build the link sheet from your channel list.
• Any good chat AI: Any chat AI can run the ladder and the score. Check the store base by hand.
• Meta Ads Manager: Lets you compare attribution settings and break results down by new, engaged and existing audiences.
• Shopify: Gives new-customer revenue and orders, and a cohort report for your own repeat data.
• A post-purchase survey app: Collects where buyers say they first heard of you, so you can compare it with platform data.

Functions and deliverables

• Store campaign tracking and return reviewer: Share GA4, Meta, campaign setup and store data. Get tracking findings, tagged-link plans and a comparison of reported returns with store totals.
  • A GA4 score out of 100, with each fix and its click path
  • A Meta tracking score out of 100, the server route that fits you and the steps to set it up
  • A tagged link sheet and naming rules, so Shopify can match sales to each campaign
  • A return ladder per platform and a score out of 100 for how far to trust each one
• GA4 store setup checker: Check your GA4 store setup in one pass. Get the day-one settings you missed, the store events to add and a setup score out of 100.
  • A pass or fail list of day-one settings with click paths
  • The store events that are missing
  • A key event plan that includes your thank-you page
  • A test sequence, the wait time before judging data, and a score out of 100 with its sum
• Meta server tracking checker: Share your tracking route and test records. Compare setup options, check duplicate events and identify consent or test-code gaps.
  • A recommended route with the reason, the realistic gain and the monthly cost to confirm
  • Setup steps in order
  • A pass or fail on matching event IDs
  • Fixes for missing IDs, consent rules and test-ID leaks, a weekly check, and a score out of 100
• Shopify campaign link planner: Share campaign dates, channels and page links. Get tagged links, Shopify match rules and an attribution model.
  • Naming rules and a link sheet with tags for each channel
  • The auto-match rules to enter in Shopify's marketing area
  • The attribution model to start with, and why
  • A weekly review routine
• Ad return evidence checker: Share ad reports and store totals. Get matched comparisons, evidence gaps and a custom review score, without a causal sales claim.
  • A comparison of reported measures, definitions, periods and missing coverage
  • A supported top-ad breakdown with audience and attribution limits
  • The evidence basis and next check for each proposed budget decision
  • A bounded custom checklist score with missing checks shown
• Metric definition writer: Share a metric, calculation and business use. Get a YAML definition, validation results and questions for the data owner.
  • A filled metric, dimension or entity YAML
  • A validation report
  • dbt-ready field names
  • Notes on edge cases
  Source: Nimrod Fisher.

Tasks you can use it for

• You are about to raise ad spend and the numbers in GA4, Meta and Shopify do not agree.
• Meta reports more sales than your store shows, or the same sale appears twice.
• You launch on several channels and cannot tell which one sold.

Limits to check before starting

• Your store has no ad spend yet: wait until you run ads, then use this workflow.
• Only GA4 is the problem: use the GA4 store setup checker on its own.
• Your sales dropped and you need to know why: use the Ad sales drop analyst.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the handoff pack. It's a zip with HANDOFF.md and every skill file this workflow uses.
• Give the folder to Claude (in Claude Code, or as files in a Claude Project) and say "Follow HANDOFF.md".
• Describe your GA4 setup. Never paste a password, a token or a secret key. If one slips in, remove it and make a new one.
• Fix the GA4 gaps, place a test order and check it appears in GA4 the next day.
• Apply the Meta route and check in Events Manager that one test purchase shows once. Then use the tagged links and run the campaigns for two weeks.
• Pull platform ROAS and store totals for the same dates and bring them back.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/set-up-tracking-you-can-trust-before-you-scale for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.