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AD LEADS THAT NEVER GO MISSING.

Lead Quality → Meta Conversions API

Teach Meta which leads turn into customers, so it finds you more of them.

Starts
Every 30 minutes
Size
5 nodes · 4 connections
Tools
Meta Conversions API, Google Sheets
Category
Marketing Ops · moderate
Status
Running on our n8n
shopify-meta-capi-feedback.jsonRunning now

Drawn by n8n's own canvas from the workflow file, synced 2026-09-29. Click it to pan and zoom. Node settings, credentials, prompts and IDs are removed; names, layout and wiring are exactly as built.

Part 1 The problemWhy teams need this

01 · THE PROBLEM

Out of the box, Meta hunts for people who fill in forms, and plenty of them never buy. When you tell it what happened after the form, it learns what a good customer looks like and spends your budget chasing more of them.

Part 2 How it worksWhat it does, step by step

02 · WHAT IT DOES

Your sales team marks a lead as qualified, booked or closed. This workflow notices, and every 30 minutes it reports that progress back to Meta from the server, with the deal value attached and contact details hashed for privacy. Your ads start optimising for buyers, not for form-fillers.

03 · HOW IT RUNS

Step by step, as built.

  1. Read the lead sheet

    Fetches the rows written by the landing-page form, where the sales team records each lead's progress.

  2. Pick what to report

    A Code node selects leads with a new stage (qualified, booked or closed) that haven't been synced yet.

  3. Hash, then send

    Email and phone are SHA-256 hashed before they leave, and the event is posted server-side, independent of the browser pixel.

  4. Mark it synced

    The row is updated so the next run skips it.

04 · TOOLS AND APPS

Built around the systems already in the process.

Meta Conversions APITrigger or source
Google SheetsDestination or delivery

05 · WHEN SOMETHING BREAKS

Failure is designed in.

Standard on every build

  • Schema validation before downstream writes
  • Retry and error routes for external API failures
  • Duplicate-safe processing and idempotent updates
  • Human approval where the action carries business risk
  • Execution logging for support and audit review

Part 3 The impactWhat it's worth, and how we'd build yours

06 · PROJECTED IMPACT

What it should change in the business.

01Server-side attributionProjected
0230% better ad ROASProjected
03PII-safe hashingProjected

Projections for a typical deployment. The calculation below shows the math, and you can put in your own numbers.

07 · ROI CALCULATION

How it pays back in your business.

The starting numbers are a hypothetical deployment sized to the projections above. Change any of them to your own volumes and costs, and the math updates underneath.

Projected net value per year$26,940$27,000 extra ad revenue per year
Gross value
$27,000
Running cost
−$60
Return per $1 of running cost
$450

The math: $3,000 spend × 12 months × 2.5 ROAS × 30% improvement = $27,000 extra revenue a year. Net value = gross value − $60 running cost a year.

08 · HOW WE'D BUILD YOURS

How we'd build yours.

  1. Discover: map the current process, systems, volumes, owners and exceptions.
  2. Design: define the canonical data model, approvals, retries and system boundaries.
  3. Build: implement credentials, nodes, validation and observable error routes.
  4. Prove: run controlled data through success, duplicate and failure scenarios.
  5. Operate: publish runbooks, ownership and measurable service levels.

SAME SYSTEM

Ad leads that never go missing.

Capture, a safety net and a feedback loop. New leads hit your team's WhatsApp in seconds, anything Meta fails to deliver gets recovered, and your wins are reported back so the ads get smarter.

YOUR VERSION

Want this, wired to your tools?

Tell us what the process looks like today: the systems, the volumes, the step everyone hates. We'll come back with the n8n version and where a person stays in charge.

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