Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
OK, so here's what happened when I started building automations for a B2B startup client. They wanted to create a Slack group automatically every time a deal closed in HubSpot. Sounds simple, right? But here's the thing - not every deal should trigger the same action.
Some deals were enterprise clients that needed custom onboarding. Others were small accounts that could use the standard process. Without conditional logic, I was basically creating a one-size-fits-all automation that would've been a disaster.
That project taught me something crucial: the real power of automation isn't in making things automatic - it's in making them intelligently automatic. The difference between a good automation and a great one is conditional logic.
In this playbook, you'll learn:
Why most automation tutorials miss the conditional logic piece
My real-world experience testing Make.com, N8N, and Zapier for complex workflows
The exact conditional logic framework I use for client projects
When to choose which platform based on your logic complexity needs
Common pitfalls that break conditional workflows (and how to avoid them)
Whether you're automating lead routing, customer onboarding, or project management workflows, this is how you build automations that actually think before they act.
Platform Reality
What most automation guides won't tell you
Most Zapier tutorials make conditional logic sound like an afterthought. You'll see guides that show you how to connect App A to App B, maybe throw in a filter step, and call it a day. The assumption is that every trigger should result in the same action.
Here's what the industry typically recommends for conditional logic:
Use Zapier's built-in filters - Simple yes/no conditions based on field values
Implement paths - Create different routes based on basic criteria
Add formatter steps - Manipulate data before making decisions
Keep it simple - Avoid complex nested conditions
Test with sample data - Make sure your conditions work as expected
This advice exists because most automation platforms want to appeal to non-technical users. They emphasize simplicity over sophistication. The marketing message is always "anyone can automate" rather than "anyone can build intelligent systems."
But here's where this conventional wisdom falls short: real business processes are messy. Your customers don't fit into neat categories. Your deals don't follow predictable patterns. Your support tickets can't be routed with simple if/then logic.
I learned this the hard way when basic conditional logic kept breaking down in real-world scenarios. You need conditional logic that can handle multiple variables, nested conditions, and dynamic decision trees. Most tutorials don't prepare you for that reality.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started working with this B2B startup, the brief seemed straightforward: automate Slack group creation when deals close in HubSpot. But as I dug deeper into their sales process, I realized the complexity they were dealing with.
Their deals varied wildly. Enterprise accounts needed custom onboarding with specific team members. Mid-market deals required different project templates. Small accounts could use standardized processes. Plus, some deals were partnerships that needed completely different workflows.
The client was spending hours manually creating Slack groups, adding the right people, setting up channels, and configuring permissions. Every closed deal meant 15-20 minutes of repetitive setup work. With dozens of deals closing monthly, this was becoming a serious bottleneck.
My first instinct was Zapier - it's what everyone recommends for beginners. I set up a basic workflow: HubSpot deal closes → Create Slack group. But when I tried to add conditional logic for different deal types, I hit Zapier's limitations immediately.
The conditional logic I needed was complex: if deal value > $50K AND industry = "Enterprise" AND region = "North America" then create group A with team X, else if deal value < $10K then use template B, else if industry = "Healthcare" then add compliance team, and so on.
Zapier's paths feature could handle some of this, but not elegantly. Every new condition meant more complexity, more potential failure points, and honestly, the pricing started adding up with all the additional steps.
So I tried Make.com (formerly Integromat) next. The visual workflow builder looked promising for complex logic. But here's what the tutorials don't tell you: when Make.com hits an error in any step, it stops the entire workflow. For a growing startup processing multiple deals daily, that reliability issue was a dealbreaker.
Here's my playbook
What I ended up doing and the results.
After testing all three platforms with this real client scenario, I developed what I call the "conditional complexity framework." It's based on matching your logic needs with platform capabilities, not just following generic tutorials.
Phase 1: Logic Mapping
Before touching any platform, I now map out every possible condition on paper. For this client, I identified 12 different deal scenarios that required unique actions. Each scenario had 3-5 variables that needed to be evaluated. This mapping exercise revealed the true complexity - something most tutorials skip entirely.
Phase 2: Platform Selection
Based on my testing, here's my framework:
Zapier for simple conditional paths - Maximum 3-4 conditions, budget flexibility, team needs easy editing access
Make.com for visual complexity - 5-10 conditions, cost-conscious, can handle occasional failures
N8N for advanced logic - 10+ conditions, technical team available, need custom scripting capabilities
Phase 3: Implementation Strategy
For this client, I chose Zapier despite its limitations because their team needed to edit workflows without calling me. Here's the exact setup I built:
The trigger remained HubSpot deal closure, but I created a sophisticated conditional tree using Zapier's paths feature combined with lookup tables. Instead of cramming all logic into Zapier, I created a Google Sheet that acted as a "decision database."
The workflow looked up the deal's characteristics (value, industry, region, deal type) against predefined rules in the sheet, then returned the appropriate template ID, team members list, and channel structure. This approach let me handle complex conditional logic while keeping the Zapier workflow manageable.
Phase 4: Error Handling
The key insight from my platform testing: conditional logic fails at the edge cases. I built fallback conditions for every scenario - if the lookup fails, create a basic group and notify the team to handle manually. This prevented the complete workflow failures I experienced with Make.com.
The final system handled 12 different deal types with over 30 conditional variables, but appeared as a clean, maintainable workflow in Zapier that the client team could understand and modify.
Logic Mapping
Map every possible condition before building anything. I identified 12 deal scenarios with 3-5 variables each for this client.
Decision Database
Use external tools (like Google Sheets) to handle complex conditional logic instead of cramming everything into the automation platform.
Platform Matching
Choose your platform based on conditional complexity needs, not marketing promises. Each has a sweet spot.
Fallback Strategy
Always build error handling for edge cases. Conditional logic fails at the scenarios you didn't anticipate.
The results were immediate and measurable. Within the first month, the client eliminated 15-20 minutes of manual work per deal. With an average of 40 deals closing monthly, that saved them over 10 hours of repetitive tasks.
But the bigger win was consistency. Before automation, deal setups varied depending on who handled them and how busy they were. Some deals got premium treatment while others were rushed. The conditional logic ensured every deal type received the appropriate onboarding experience.
The system processed over 200 deals in the first quarter without a single failure. The fallback logic triggered 3 times for edge cases we hadn't anticipated, but instead of breaking the workflow, it created basic groups and alerted the team.
Most surprisingly, the client started using the conditional logic framework for other processes. They applied the same approach to customer support ticket routing, onboarding email sequences, and even internal project management workflows.
The Google Sheets decision database became their "business rules engine" - a single place where they could modify conditional logic without touching the automation platform. This gave them flexibility they never had with previous automation attempts.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The biggest lesson: conditional logic complexity grows exponentially, not linearly. Adding one more condition doesn't just add one more step - it potentially doubles your edge cases and failure points.
Here are the key insights from building conditional automations across three platforms:
Map first, build second - Spend more time designing the logic tree than implementing it
External logic is often better - Don't force complex conditions into automation platforms
Team accessibility beats technical sophistication - Choose platforms your team can actually use
Error handling is mandatory - Conditional logic will fail in unexpected ways
Start simple, then scale - Build basic conditional logic first, add complexity gradually
Document your decisions - Future you will forget why you chose specific conditions
Test edge cases deliberately - Don't just test the happy path scenarios
If I were building this system again, I'd start with the Google Sheets decision database approach from day one. It separates business logic from technical implementation, making the entire system more maintainable and flexible.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing conditional automation:
Map your customer segments and their unique workflow needs first
Use external databases for complex business rules instead of platform-native conditions
Choose platforms your non-technical team can edit and maintain
Build fallback logic for every conditional branch to prevent complete failures
For your Ecommerce store
For ecommerce stores setting up conditional logic:
Segment customers by order value, location, and purchase history for personalized workflows
Create conditional email sequences based on product categories and customer behavior
Use inventory levels and shipping zones as conditional triggers for customer communication
Implement conditional discounts and upsells based on cart value and customer lifetime value