Best-Practice Tips for Using Make.com Effectively
Make (formerly Integromat) is a powerful visual automation platform. However, its real value is unlocked not by simply connecting apps, but by engineering workflows that are deterministic, scalable, and resilient.
This guide outlines proven best practices for using Make effectively—covering workflow design, data integrity, debugging, cost control, and advanced automation patterns. Whether you are building simple integrations or complex AI-driven systems, these principles will help you avoid automation debt and scale with confidence.
Strategic & Design Principles
Design the Process Before the Scenario
Before building anything in Make, define the process clearly:
Trigger → Data Preparation → Logic → Actions → Outcomes
Identify inputs, outputs, edge cases, and failure states upfront. This disciplined approach prevents rework, reduces complexity, and ensures the scenario behaves predictably as volume increases.
Optimise for Determinism, Not Convenience
Avoid “best effort” automations. Make performs best when workflows are explicit and deterministic—where each path, condition, and outcome is intentionally defined. Convenience-driven shortcuts often lead to inconsistent behaviour and difficult debugging later.
Build for Scale From Day One
Always assume usage will grow. Scenarios should be designed to:
Filter aggressively
Avoid unnecessary operations
Use routers instead of duplicating scenarios
Scaling is far easier when efficiency is embedded at the design stage.
Scenario Structure & Performance
Filter Early, Filter Hard
Filters should be placed immediately after the trigger to:
Remove test or internal data
Block incomplete submissions
Prevent unnecessary downstream operations
This is the single most effective way to control costs and improve performance in Make.
Normalise Data at the Start
Standardise data as early as possible:
Phone numbers → E.164 format
Dates → ISO 8601
Text → trimmed and lower-case
Clean data improves CRM matching, deduplication accuracy, reporting consistency, and downstream logic.
Use Routers for Logical Branching
Routers allow you to handle multiple outcomes within a single scenario. They are ideal for:
Routing by country, product, or service
Handling multiple lead types or workflows
This approach improves maintainability and prevents operational sprawl.
Name Every Module Clearly
Replace default module names with descriptive, functional labels, such as:
“Create Contact in HubSpot”
“Check Existing Deal by Email”
Clear naming is essential once scenarios exceed ten modules and dramatically reduces troubleshooting time.
Design & Efficiency Techniques
Rename Your Modules
Right-click and rename every module. In complex scenarios, meaningful names are far easier to debug than generic labels like “HTTP [3]” or “Gmail [2]”.
Use the “Set Variable” Module
Centralise shared values such as:
Company name
Base URL
Environment flags
This allows you to update a value once instead of editing multiple modules later.
Keep Scenarios Modular
Avoid building large “mega-scenarios”. If a workflow exceeds 15 modules, consider splitting it into smaller scenarios connected via webhooks or scenario inputs. Modular design improves stability and reusability.
Debugging & Testing
Run Individual Modules
Use “Run this module only” to test a single step without executing the entire scenario. This speeds up development and isolates issues quickly.
Move the Starting Point
You can reposition the starting (clock) icon to test from the middle of a workflow—useful when debugging downstream logic.
Inspect Raw Data
Using tools such as the Make DevTools Chrome extension allows you to inspect the raw JSON passing between modules, making it easier to diagnose mapping or formatting issues.
Use Unpassable Filters During Testing
Insert a filter that cannot pass (for example, 1 exists = false) before actions that trigger real-world effects such as emails, payments, or CRM updates. This allows safe testing without side effects.
Error Handling & Reliability
Enable Incomplete Executions
Turn on Allow storing of incomplete executions in scenario settings. This ensures data is preserved if a temporary failure occurs, allowing you to fix and resume without data loss.
Implement Retries with Break Handlers
For API-dependent modules, use Break error handlers with controlled retries and backoff periods. This improves reliability when external systems experience transient issues.
Use Ignore for Non-Critical Steps
For logging or secondary actions, apply the Ignore directive so non-essential failures do not disrupt critical business processes.
Data Integrity & State Management
Always Use Unique Identifiers
Wherever possible, rely on:
Contact IDs
Deal IDs
Order IDs
Avoid matching solely on names or emails without fallback logic.
Add Deduplication Logic Explicitly
Never assume the destination system will deduplicate correctly. Always:
Search first
Update if found
Create only if not found
Use Data Stores for Workflow Memory
Data Stores are ideal for:
Tracking processed records
Preventing double-processing
Maintaining state between runs
Cost & Operations Management
Minimise Operations, Not Steps
Cost is driven by operations, not module count. Pay close attention to:
Search modules
Iterators over large datasets
Unfiltered triggers
One inefficient module can cost more than several lightweight ones combined.
Use Scheduling Intelligently
Avoid real-time execution unless necessary. Batch processing every 5–15 minutes often delivers better stability, lower cost, and improved error recovery.
Advanced Automation Techniques
Use Iterators and Aggregators Carefully
Iterators are powerful but risky at scale. Always:
Filter before iterating
Limit bundle sizes where possible
Prefer Webhooks Over Polling
When supported, webhooks are faster, cheaper, and more reliable than scheduled polling triggers.
Separate Core Logic From Destinations
Design reusable logic layers that can feed multiple destinations, such as:
CRMs
Google Sheets
Advertising platforms
Reporting tools
This improves flexibility and reduces duplication.
AI & Automation Strategy
Treat AI as a Processing Layer
Use AI modules for:
Data summarisation
Classification
Content generation
Do not rely on AI for core business logic without validation and safeguards.
Always Validate AI Output
Add checks before using AI-generated data in:
CRM records
Customer communications
Advertising platforms
Governance & Documentation
Version Your Scenarios
Duplicate scenarios before major changes and maintain clear versions (e.g. stable vs testing). This significantly reduces downtime risk.
Document Assumptions Inline
Use notes to explain:
Why specific filters exist
Why is fallback logic required
This is essential for long-term maintenance and team handover.
Final Perspective
Make should be treated as a workflow orchestration engine, not a simple integration tool. The most successful automations prioritise data quality, deterministic logic, error resilience, and operational efficiency over speed of initial setup.
When designed correctly, Make becomes a scalable foundation for CRM integration, marketing automation, AI workflows, and business operations—capable of supporting serious growth without fragility.
