Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I was managing Facebook campaigns for a B2C Shopify client when something strange happened. Facebook claimed a 2.5 ROAS, but the client's bank account told a different story. The math just didn't add up.
That's when I realized most small businesses are flying blind with expensive tracking tools that lie to them. We're paying hundreds monthly for attribution platforms that can't even track a simple customer journey across devices. Meanwhile, we're making budget decisions based on data that's fundamentally broken.
The painful truth? Most tracking data is fiction dressed up as analytics. But after working with dozens of startups and ecommerce stores, I've discovered that the best tracking solutions aren't the expensive ones – they're the simple ones you build yourself.
Here's what you'll learn from my experiments with low-cost tracking:
Why traditional attribution models are broken (and what to track instead)
A $47/month tracking stack that outperforms $500+ enterprise solutions
The 3-step process I use to track true ROI without third-party cookies
How to build custom tracking that survives iOS updates and privacy regulations
Real examples from ecommerce and SaaS projects where simple tracking saved thousands
Reality Check
What every marketer has been sold
Walk into any marketing conference and you'll hear the same advice: "You need proper attribution." "Invest in a robust analytics stack." "Track everything across every touchpoint."
The industry has convinced us we need enterprise-level tracking to run successful campaigns. Here's what they typically recommend:
Multi-touch attribution platforms - Tools like HubSpot Attribution, Bizible, or Ruler Analytics that promise to track every customer touchpoint
Advanced pixel implementations - Complex Facebook Pixel setups with custom conversions and server-side tracking
Cross-device tracking - Expensive solutions that claim to connect user behavior across phones, tablets, and desktops
Customer data platforms (CDPs) - Tools like Segment or Mixpanel that unify data from multiple sources
Advanced analytics dashboards - Custom Google Analytics 4 setups with complex funnel tracking
This conventional wisdom exists because tracking vendors have convinced marketers that complexity equals accuracy. The more touchpoints you track, the better decisions you'll make, right?
Wrong. In practice, these complex systems create three major problems: they're expensive, they're inaccurate due to privacy restrictions, and they're so complicated that most teams can't actually use them effectively.
The reality is that since iOS 14.5 and increasing privacy regulations, traditional attribution has become less reliable, not more. We're paying premium prices for broken data.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came during a project with a Shopify client selling fashion accessories. They had over 1,000 SKUs and were spending about €3,000 monthly on Facebook ads with what appeared to be decent performance.
Facebook's attribution was reporting a 2.5 ROAS. Google Analytics showed different numbers. Their expensive attribution tool (costing $400/month) showed yet another set of metrics. Everyone was telling a different story.
My client was frustrated: "Which data should I trust? Facebook says the ads are working, but I'm not seeing the revenue growth I expected."
That's when I decided to ignore all the tracking platforms and go back to basics. I spent a week manually tracking their customer journey using simple tools:
Google Sheets - To log every sale with its source manually
Custom URL parameters - Simple UTM codes I could actually control
Direct customer surveys - Asking buyers how they found the store
The results were shocking. The actual ROAS was closer to 1.8, not 2.5. More importantly, we discovered that SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins.
This experience taught me that most attribution platforms aren't measuring what actually drives business results. They're measuring their own version of reality – and charging premium prices for the privilege of being wrong.
Here's my playbook
What I ended up doing and the results.
After that reality check, I developed a simple tracking system that costs under $50/month and provides more reliable data than enterprise solutions. Here's the exact setup I now use for all my clients:
The Core Stack (Total: $47/month)
Google Analytics 4 (Free) - But configured properly with custom events
Google Sheets + Apps Script ($0) - For custom data processing and manual validation
Zapier ($20/month) - To automate data flow between platforms
Typeform ($25/month) - For post-purchase surveys
Simple server logs ($2/month) - Basic hosting with access logs
Step 1: Implement "Trust but Verify" Tracking
Instead of trying to track everything, I focus on the metrics that actually matter for business decisions. I set up three data sources that cross-validate each other:
Platform data (Facebook, Google Ads) - What they claim
Website analytics (GA4) - What actually happened on-site
Revenue data (Shopify, Stripe) - What the bank account shows
When these three sources disagree (which they always do), I know exactly where the discrepancies are coming from.
Step 2: Custom UTM Strategy
I use a simple but consistent UTM parameter system:
utm_source = platform (facebook, google, email)
utm_medium = campaign type (ads, organic, direct)
utm_campaign = specific campaign name
utm_content = creative or audience identifier
The key is consistency. Every link gets tagged, and I use a simple Google Sheet to generate URLs automatically.
Step 3: Post-Purchase Survey System
This is the secret weapon. 24 hours after purchase, customers get a simple survey asking: "How did you first hear about us?" The response rate is typically 15-25%, giving us ground truth data.
I use Zapier to automatically send these surveys via Typeform, then compile responses in Google Sheets. This single data point often reveals attribution gaps that platforms miss.
Step 4: Revenue Reconciliation
Every Monday, I run a simple reconciliation: platform reported revenue vs. actual revenue vs. customer survey data. This takes 10 minutes and provides more insight than any complex attribution model.
Manual Verification
Always cross-check platform claims with actual revenue data
Platform Integration
Use Zapier to automate data flow between simple tools
Survey Strategy
Post-purchase surveys reveal true attribution better than pixels
Revenue Tracking
Weekly reconciliation catches discrepancies before they impact decisions
Using this simple system across multiple clients, I consistently found that actual performance was 15-30% different from what expensive tracking platforms reported.
For the Shopify fashion client, the real breakthrough came when we stopped obsessing over Facebook's attribution and focused on total revenue growth. Within two months:
We identified that organic traffic was 40% higher than any platform reported
Customer surveys revealed that 60% found the store through Google search, not ads
We shifted budget from Facebook to SEO and saw overall revenue increase by 35%
The simple tracking system cost $47/month instead of the $400+ they were paying for "advanced" attribution. More importantly, the decisions we made based on this data actually drove business results.
Another client, a B2B SaaS startup, used the same approach and discovered their expensive attribution tool was missing 70% of organic signups. They cancelled the tool, implemented the simple system, and made better growth decisions with clearer data.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key insights from building low-cost tracking systems across dozens of projects:
Simple beats complex - The most accurate data often comes from the simplest tracking methods
Survey data is gold - Asking customers directly beats any algorithmic attribution model
Revenue reconciliation is essential - If your tracking doesn't match your bank account, fix the tracking
UTM consistency matters more than complexity - A simple, consistent system outperforms a complex, inconsistent one
Manual verification catches lies - Spend 10 minutes weekly checking if platform data matches reality
Cost doesn't equal accuracy - The cheapest solution is often the most reliable
Focus on actionable metrics - Track what you can actually optimize, not what looks impressive in reports
The biggest lesson? Attribution is broken, but business metrics aren't. Focus on total revenue, customer lifetime value, and actual growth rather than chasing perfect attribution.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups, implement this system to track trial-to-paid conversion:
Set up post-signup surveys asking "How did you hear about us?"
Track trial-to-paid rates by actual source, not platform attribution
Use UTM parameters in all email campaigns and content links
Weekly reconciliation of MRR growth vs. marketing spend
For your Ecommerce store
For ecommerce stores, focus on revenue tracking over vanity metrics:
Implement post-purchase surveys to identify true traffic sources
Track lifetime value by acquisition channel, not just first purchase
Use simple UTM codes for all promotional campaigns
Monthly revenue reconciliation across all platforms and actual sales data