Make.com with GoHighLevel: The Definitive Guide

GoHighLevel (GHL) has established itself as one of the most powerful all-in-one platforms for CRM, marketing automation, sales pipelines, messaging, and appointment management. For agencies, SaaS operators, and service-led businesses, it offers an unparalleled consolidation of tools that previously required fragmented stacks. However, as businesses mature, complexity increases.

Leads originate from multiple advertising platforms. Sales cycles become multi-stage and non-linear. Reporting requirements move beyond dashboards into forecasting and attribution. AI shifts from novelty to operational necessity. Data must flow reliably across finance, delivery, analytics, and compliance systems.

This is where Make.com (formerly Integromat) becomes strategically essential.

While Zapier is known for its simplicity and linear workflows, Make is preferred by advanced GoHighLevel users because it offers:

• Visual, multi-step logic on a drag-and-drop canvas

• Complex branching, iterators, and routers

• Advanced data transformation (arrays, regex, maths)

• Lower cost per operation at high volumes

• Deep API and webhook control

In modern stacks, the relationship is clear:

• GoHighLevel is the system of record

• Make is the orchestration and intelligence layer

This article provides a definitive, real-world guide to how Make is actually used with GoHighLevel at scale, particularly by AI-first agencies, SaaS operators, and data-driven organisations.

Understanding the Architectural Roles of GHL and Make

Before diving into use cases, it is important to define responsibilities clearly.

GoHighLevel’s Role

GHL excels at:

• Storing contacts and conversations

• Managing pipelines and opportunities

• Running event-driven workflows

• Handling messaging (SMS, email, WhatsApp)

• Managing calendars and appointments

It is optimised for execution at the CRM layer.

Make.com’s Role

Make excels at:

• Orchestrating multi-system logic

• Handling volume and complexity

• Enriching, transforming, and validating data

• Coordinating AI decision-making

• Synchronising data across platforms

Make is not a replacement for GHL workflows. It is the engine that connects, governs, and scales them.

1. Advanced Lead Processing and AI-First Automation

One of the most powerful reasons advanced GHL users adopt Make is its suitability for AI-first lead handling.

AI Lead Scoring and Filtering

Traditional CRMs route leads based on rigid rules: form source, dropdown values, or basic tags. This approach breaks down with open-ended inputs and nuanced buyer intent.

Make enables AI-driven lead qualification.

Typical Architecture

1. A lead submits a GHL form or sends an inbound message

2. A GHL workflow fires a Custom Webhook to Make

3. Make sends structured lead data to an AI model (e.g. OpenAI)

4. The AI evaluates:

   • Intent strength

   • Urgency

   • Fit for the offer

   • Likelihood to convert

5. Make routes the lead dynamically:

   • High-intent leads → VIP pipeline

   • Medium-intent leads → standard nurture

   • Low-intent or irrelevant leads → archive

Business Impact

• Sales teams only see leads that justify human time

• Response speed improves without increasing headcount

• Close rates rise due to better prioritisation

This is not theoretical. It is now a common pattern among high-performing GHL agencies.

Contextual AI Auto-Replies (Human-in-the-Loop)

Make enables context-aware AI messaging, which goes far beyond simple autoresponders.

Advanced Flow

• A message arrives in GHL (SMS, chat, WhatsApp)

• Make retrieves:

  • Full conversation history

  • Contact properties

  • Pipeline stage

  • Previous objections or notes

• This context is sent to an AI model

• The AI drafts a tailored response aligned to:

  • Tone of conversation

  • Stage in buying journey

  • Known objections

• The response is written back to GHL as:

  • A draft message

  • An internal suggestion

  • Or a note for approval

This approach preserves control while dramatically reducing response effort.

Lead Enrichment and Data Augmentation

Make is widely used as a pre-CRM intelligence layer.

Enrichment Workflow

• New contact created in GHL

• Make sends email or domain to:

  • Clearbit

  • Apollo

  • FullContact

• Retrieved data includes:

  • LinkedIn profile

  • Job title

  • Company size

  • Industry

• GHL contact is updated with:

  • Custom fields

  • Tags

  • Routing logic

Why This Matters

Better data leads to:

• Smarter segmentation

• More accurate lead scoring

• Higher-quality sales conversations

Without enrichment, CRMs decay rapidly into incomplete datasets.

2. Multi-Step Data Synchronisation and the “Golden Record”

Make Excel where one event must update many systems simultaneously.

The “Golden Record” Sync Pattern

In advanced stacks, no single platform owns all data. Instead, one system becomes the golden source for each entity.

With GHL as the CRM source of truth, Make synchronises updates outward.

Example

When an opportunity stage changes in GHL, Make can:

• Update a Google Sheets row

• Change a ClickUp task status

• Update a deal in HubSpot or Salesforce

• Log the change in Airtable

• Notify internal teams

All within one scenario, with unified logic and error handling.

This avoids data drift and reconciliation issues that plague simpler integrations.

E-commerce Deep Integration

Native GHL e-commerce integrations often abstract order data. Make allows granular, revenue-grade syncs.

Supported platforms include:

• Shopify

• WooCommerce

• BigCommerce

Common Enhancements

• Sync the last purchase date

• Maintain lifetime value (LTV)

• Track order count and frequency

• Capture product-level data

This enables:

• Revenue-based segmentation

• Accurate reactivation campaigns

• Better forecasting inside GHL

3. Agency Operations and SaaS-Mode Automation

For agencies running GoHighLevel SaaS Mode, Make is often non-optional.

Automated Client Onboarding

Manual account provisioning does not scale.

Typical Flow

1. Client pays the first invoice via Stripe or PayPal

2. Make detects the payment

3. Make executes GHL API calls to:

   • Create a sub-account

   • Load the correct Snapshot

   • Apply defaults and permissions

   • Invite users

4. Internal onboarding workflows are triggered automatically

This reduces provisioning time from hours to minutes and eliminates errors.

Cross-Subaccount Reporting

GHL is excellent at single-account reporting. Agencies need portfolio-level visibility.

Make aggregates data across:

• 10s or 100s of GHL sub-accounts

Metrics commonly aggregated:

• Lead volume

• Appointment bookings

• Conversion rates

• Revenue

Data is centralised into:

• Google Sheets

• Airtable

• BigQuery

• Looker Studio

This enables executive-level reporting that GHL alone cannot deliver.

Interactive Internal Notifications

Make allows interactive Slack or Discord notifications.

Example

• Slack message includes buttons:

  • “Approve Lead”

  • “Reject Lead”

• Button click sends webhook to Make

• Make updates GHL (stage, tag, assignment)

This creates closed-loop operational workflows without CRM logins.

4. Advanced Waiting, Watching, and Lifecycle Logic

GHL workflows are excellent at reacting to events. Make excels at monitoring the state over time.

Watching Specific Field Changes

Zapier often triggers on any record update. Make can:

• Watch only specific fields

• Trigger only on meaningful changes

This dramatically reduces noise and unnecessary automation runs.

Scheduled Data Hygiene and Lifecycle Management

Make supports scheduled scenarios independent of triggers.

Common Use Cases

• Weekly scan for contacts with no activity in 90 days

• Automatically:

  • Apply “Cold” tag

  • Move to re-engagement pipeline

  • Trigger win-back sequences

This keeps CRMs accurate and pipelines realistic.

5. Sales, Revenue, and Financial Automation

Make is preferred when financial logic becomes complex.

Supported systems include:

• Stripe

• Xero

• QuickBooks

• Chargebee

• Paddle

Advanced Revenue Workflows

• Create subscriptions from deals

• Calculate MRR and ARR

• Detect churn events

• Run commission calculations

• Trigger dunning workflows

These workflows often exceed the capabilities of native CRM automation.

6. Advertising, Attribution, and Offline Conversions

Make is widely used to build robust attribution pipelines.

Offline Conversion Tracking

Capabilities include:

• Capturing GCLID, FBCLID, GBRAID, WBRAID

• Mapping CRM lifecycle stages

• Sending qualified or revenue conversions to:

  • Google Ads

  • Meta Ads

  • LinkedIn Ads

Because Make supports retries, batching, and validation, it is more reliable at scale than simpler tools.

7. Webhooks, APIs, and Custom Systems

Make offers full API-level orchestration.

Capabilities include:

• Custom HTTP requests

• OAuth token management

• Pagination handling

• Rate-limit control

• Error retries and logging

This makes Make ideal for:

• Proprietary systems

• Legacy software

• Vertical-specific platforms

Make vs Zapier for GoHighLevel: A Strategic Comparison

Pro Tip: The Webhook Bridge (Critical Best Practice)

The most reliable pattern is:

• Trigger a Custom Webhook from a GHL workflow

• Push data to Make at the exact moment required

• Avoid polling wherever possible

This approach:

• Reduces latency

• Improves reliability

• Lowers cost

• Enables advanced logic

Conclusion: Make, as the Orchestration Backbone for GoHighLevel

Make transforms GoHighLevel from a powerful CRM into a scalable, AI-driven operating system.

It is the correct choice when:

• Logic is complex

• Volume is high

• AI is central to operations

• Data quality matters

• Long-term scalability is required

In advanced environments, Make is not “another automation tool”.
It is the orchestration backbone that allows GoHighLevel to operate at an enterprise level.