Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
I was staring at my client's Make scenario that looked like someone had exploded a spaghetti factory. Twenty-seven modules doing the same basic task: routing leads from HubSpot to different team members based on their industry, deal size, and urgency level.
The "solution" their previous consultant built? A separate scenario for every possible combination. Manufacturing leads over $50K went to Sarah. Tech leads under $10K went to Mike. Urgent healthcare leads went to everyone. You get the picture.
It was a maintenance nightmare. Every time they wanted to change assignment rules, they had to update multiple scenarios. When Sarah went on vacation, they had to manually pause six different workflows. The system was technically working, but it was fragile and impossible to scale.
This is the exact problem conditional logic in Make solves, but here's the thing: most people treat it like basic if/then statements when it's actually a powerful workflow orchestration tool. After rebuilding their entire automation using proper conditional logic, we went from 27 scenarios to 3, reduced errors by 90%, and gave them a system they could actually manage.
Here's what you'll learn from my experience:
Why most Make tutorials teach conditional logic wrong
The 3-layer approach I use to build bulletproof conditional workflows
Real examples from my platform comparison work
How to debug conditional logic when things go sideways
My framework for scaling conditional workflows without chaos
Platform Basics
What every automation tutorial teaches about conditional logic
Every Make tutorial starts the same way: "Conditional logic is just if/then statements!" They show you the basic Filter module, explain how to set up simple true/false conditions, and call it a day.
The standard approach taught everywhere looks like this:
Use Filter modules to create basic conditions
Set up Router modules for multiple paths
Add simple operators like "equals" or "contains"
Chain conditions with AND/OR logic
Handle errors with basic fallback routes
This works great for simple scenarios like "if email contains 'urgent', send to priority queue." But it completely falls apart when you're dealing with real business logic that has multiple variables, nested conditions, and changing requirements.
The problem with this conventional wisdom? It treats conditional logic like a programming exercise instead of a business process design challenge. You end up with workflows that are technically correct but practically unmaintainable.
Most consultants build complex conditional trees that look impressive but require a PhD in Make to understand. When something breaks, you're stuck tracing through dozens of branches trying to figure out which condition failed. When requirements change, you're rebuilding half the scenario.
This is why so many businesses abandon their Make automations after a few months. They work initially, but they don't evolve with the business.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The breaking point came when my B2B startup client needed to automate their deal assignment process. They had three sales reps, four lead sources, two product lines, and three urgency levels. Simple math: that's 72 possible combinations if you build a scenario for each path.
Their previous solution was exactly that - a maze of individual scenarios that their team couldn't manage. When I looked at their Make organization, I found scenarios with names like "Tech-Leads-Large-Urgent-Sarah" and "Manufacturing-Leads-Small-Normal-Mike-Backup."
The system technically worked, but it was a house of cards. When they wanted to add a new lead source, they had to create 18 new scenarios. When deal size thresholds changed, they had to update dozens of conditions manually. When someone went on vacation, they had to remember which scenarios to pause.
My first instinct was to rebuild everything using nested Router modules - the "proper" way according to Make documentation. I spent two days creating an elegant tree structure with perfect conditional logic. It looked beautiful on paper.
But when I showed it to the client, their face said everything. "How do I change the deal size threshold?" they asked. I had to point to six different modules spread across three branches. "What if we add a new rep?" Another ten modules to update.
That's when I realized I was solving the wrong problem. They didn't need elegant conditional logic - they needed maintainable business logic. The solution wasn't about building the perfect workflow; it was about building a workflow that could evolve with their business without requiring a Make expert every time something changed.
Here's my playbook
What I ended up doing and the results.
Instead of building conditional logic into the workflow structure, I started treating it as data. This completely changed how I approached the problem.
Layer 1: Data-Driven Conditions
First, I moved all the conditional logic into a Google Sheet that became the "brain" of the automation. Instead of hardcoding "if deal size > $50K" into filters, I created a lookup table:
Lead Source | Product Line | Deal Size | Urgency | Assigned Rep | Backup Rep
Now when conditions needed to change, they edited the spreadsheet. The Make scenario simply looked up the appropriate action based on the incoming data. No more updating dozens of modules - just edit one row in a sheet.
Layer 2: Modular Logic Blocks
Instead of one mega-scenario with complex routing, I broke the logic into three focused scenarios:
Lead Processor: Enriches incoming leads and determines routing criteria
Assignment Engine: Queries the logic table and determines the action
Action Handler: Executes the assignment and handles exceptions
Each scenario had a single responsibility, making debugging and updates straightforward.
Layer 3: Exception Handling
Rather than trying to account for every possible edge case in the conditional logic, I built a robust exception handling system. When the logic table doesn't return a match, the system defaults to a "manual review" queue and alerts the team.
This approach meant we could start with 80% coverage and gradually add edge cases to the lookup table as we discovered them, rather than trying to build perfect logic upfront.
The Implementation Process
I started by mapping their current business rules into the spreadsheet format. This revealed immediately that they had conflicting rules and gaps in their logic that the old system was hiding.
Then I built the three-scenario system with robust error handling and logging. Every decision was tracked, so when something didn't work as expected, we could see exactly why.
The key insight was treating conditional logic as configuration rather than code. This made the system maintainable by non-technical team members and flexible enough to evolve with their business needs.
Error Handling
Always build fallback routes for unmatched conditions - don't let scenarios fail silently when logic doesn't cover edge cases.
Testing Strategy
Test conditional logic with edge cases first, not happy path scenarios - that's where your logic will break in production.
Documentation
Keep a decision tree diagram separate from Make - your team needs to understand the business logic without navigating the workflow.
Maintenance
Review and update conditional logic monthly - business rules drift over time and your automation should evolve with them.
The transformation was immediate and measurable. We went from 27 individual scenarios to 3 interconnected ones, reducing their Make operations consumption by 60%. More importantly, the system became manageable.
The client could now modify assignment rules by updating a spreadsheet instead of calling me to change Make scenarios. When they hired a new sales rep, it took 5 minutes to add them to the system instead of half a day building new automation paths.
Response time improved dramatically because the logic was centralized and optimized. Lead assignment that used to take 3-5 minutes now happened in under 30 seconds.
But the biggest win was reliability. The old system failed regularly when edge cases hit conditions that weren't perfectly defined. The new system gracefully handled exceptions and learned from them, with the team adding new rules to the lookup table as needed.
Six months later, they were running the entire system themselves with minimal support from me. That's the mark of well-designed conditional logic - it becomes invisible infrastructure that just works.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from rebuilding conditional logic systems for multiple clients:
Data-driven beats hardcoded: Moving logic to spreadsheets or databases makes systems maintainable by business users
Modular scenarios scale better: Three focused scenarios outperform one complex scenario every time
Exception handling is crucial: Plan for unmatched conditions from day one - they will happen
Test edge cases first: Happy path testing won't reveal where your logic breaks
Document business logic separately: Your team needs to understand the rules without diving into Make
Start simple, evolve gradually: 80% coverage that works beats 100% coverage that's unmaintainable
Monitor and iterate: Business rules change - your conditional logic should too
The biggest mistake I see is treating conditional logic as a set-it-and-forget-it solution. In reality, it's a living system that needs regular review and updates. Build with maintenance in mind, not just functionality.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this approach:
Start with lead routing and trial user flows
Use customer lifecycle stages as primary conditions
Build logic for feature flag management and user segmentation
For your Ecommerce store
For ecommerce stores using conditional logic:
Focus on order routing and customer segmentation first
Implement inventory-based conditional workflows
Use purchase history for personalized automation flows