Sales & Conversion
Personas
Ecommerce
Time to ROI
Short-term (< 3 months)
I was staring at my Facebook Ads dashboard at 2 AM, watching thousands of dollars burn with zero tracked conversions. The traffic was there, the sales were happening, but according to my tracking pixels, I might as well have been throwing money into a black hole.
This wasn't my first rodeo with tracking issues, but it was the most expensive one. After working with dozens of e-commerce clients and managing hundreds of thousands in ad spend, I've learned that tracking pixels failing isn't just a technical issue - it's often a fundamental misunderstanding of how attribution actually works in 2025.
The uncomfortable truth? Most businesses are optimizing for perfect tracking in an imperfect world, missing the bigger picture of what really drives growth. Here's what you'll learn from my painful (and expensive) experiments:
Why perfect tracking is a myth and what to focus on instead
The real reasons pixels fail (hint: it's not always technical)
My systematic approach to tracking troubleshooting that actually works
How to make strategic decisions when your data is incomplete
The framework I use to diagnose attribution issues in under 30 minutes
Let's dive into what most tracking guides won't tell you.
Industry Reality
What the marketing world wants you to believe
Walk into any digital marketing conference or scroll through any ads-focused Facebook group, and you'll hear the same mantras repeated like gospel:
"Perfect tracking is achievable" - Just install the right tools and configure everything properly
"Attribution windows matter" - Set your 7-day click, 1-day view correctly and you're golden
"More data equals better decisions" - Track everything, measure everything, optimize everything
"Platform reporting is reliable" - If Facebook says it drove the conversion, it did
"Technical fixes solve attribution problems" - Update your pixel, use server-side tracking, problem solved
This conventional wisdom exists because it's comforting. Agencies can sell more complex tracking setups, platform vendors can claim credit for results, and everyone feels like they're "doing marketing right" when their dashboards are full of data.
But here's where this approach falls apart in practice: we're living in a post-iOS 14.5 world where customer journeys are inherently dark and messy. Privacy regulations, ad blockers, cross-device behavior, and platform limitations mean your tracking will always be incomplete.
The obsession with perfect tracking often leads businesses to make terrible strategic decisions. I've seen companies kill profitable campaigns because the attribution looked weak, while doubling down on channels that "tracked well" but delivered poor actual results.
The transition to my different approach happened when I realized that chasing perfect tracking was making me a worse marketer, not a better one.
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 running a €50,000 monthly ad budget across Facebook and Google. Everything looked perfect on paper - pixels installed correctly, conversions API set up, Google Analytics configured properly. Yet the client was convinced their ads weren't working because the attribution data looked terrible.
Facebook was claiming a 2.5 ROAS, but when we looked at their actual revenue and ad spend, the real ROAS was closer to 4.5. Google was taking credit for conversions that clearly came from Facebook traffic. And about 40% of their actual sales weren't being attributed to any marketing channel at all.
The breaking point came when we almost killed their best-performing Facebook campaign because the pixel data suggested it wasn't profitable. Only a last-minute correlation analysis between ad spend and revenue saved us from making a massive mistake.
This client's situation wasn't unique. I'd seen similar attribution nightmares across multiple e-commerce projects:
A fashion store where 60% of conversions showed up as "direct traffic"
A SaaS company where Facebook claimed impossible conversion rates
An electronics retailer where Google Shopping showed negative ROI despite driving obvious sales
The pattern was clear: the more I relied on platform tracking, the worse my strategic decisions became. I was optimizing for attribution instead of actual business results.
That's when I realized the problem wasn't technical - it was philosophical. I was treating tracking like a measurement tool when I should have been treating it like one data point among many.
Here's my playbook
What I ended up doing and the results.
After burning through enough ad budget to buy a nice car, I developed a systematic approach that acknowledges the reality of imperfect tracking while still making data-driven decisions. Here's the exact framework I now use with every client:
Step 1: The Revenue Reality Check
Before looking at any platform data, I establish the ground truth. I track total revenue, total ad spend, and overall ROAS at the business level. This becomes my baseline for evaluating whether attribution data makes sense.
For that €50K/month client, this revealed the disconnect immediately. Their actual business metrics showed strong performance while platform attribution suggested mediocrity.
Step 2: The Attribution Audit
I built a simple tracking diagnostic that checks for common failure points:
Are conversions firing at all? (Check the pixel helper)
Are values being passed correctly? (Compare reported vs actual)
Is there a timing disconnect? (Check conversion delays)
Are there technical blockers? (Ad blockers, consent issues)
Step 3: The Cross-Channel Analysis
Instead of believing any single platform's story, I look for patterns across all channels. When Facebook claims credit for a conversion that Google also claims, the truth is usually more complex than either platform admits.
Step 4: The Correlation Test
This is where the magic happens. I plot daily ad spend against daily revenue for each channel. Real impact shows up as correlation, regardless of what the attribution says. Channels that truly drive results will show a positive correlation between spend and revenue.
Step 5: The Dark Funnel Mapping
I accept that a significant portion of the customer journey happens in the dark. Instead of trying to track everything, I focus on understanding the customer journey patterns and making strategic decisions based on directional data rather than precise attribution.
For complex e-commerce setups, I also implement incrementality testing - turning channels on and off to measure true impact. This cuts through attribution noise and reveals what's actually driving results.
Technical Fixes
Check pixel installation, server-side setup, and conversion API configuration. Most issues stem from implementation problems.
Correlation Analysis
Plot daily spend vs revenue by channel. Real impact shows as correlation regardless of attribution claims.
Dark Funnel Acceptance
Accept 40-60% of journeys are untrackable. Focus on directional data and business-level metrics instead.
Incrementality Testing
Turn channels on/off systematically to measure true impact. This reveals what attribution can't capture.
The results of this approach were dramatic. For the €50K/month client, we:
Identified that Facebook was actually delivering 4.5 ROAS, not 2.5
Discovered Google Shopping was profitable despite "negative" tracking data
Increased overall ad spend by 40% with confidence in actual performance
Reduced time spent on tracking troubleshooting by 80%
More importantly, decision-making became faster and more accurate. Instead of waiting for "perfect" data, we could make strategic moves based on business reality.
The unexpected outcome? Accepting imperfect tracking actually improved our marketing performance. We stopped optimizing for attribution and started optimizing for results.
This approach has now been tested across multiple clients and consistently delivers better outcomes than traditional attribution-focused methods.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from years of attribution battles:
Perfect tracking is impossible in 2025 - Plan your strategy around this reality, not against it
Platform data is biased - Every platform has incentives to over-claim attribution
Business metrics trump attribution metrics - Focus on actual revenue and profit, not tracked conversions
Correlation reveals truth - Spend vs revenue correlation cuts through attribution noise
The dark funnel is real - Accept that 40-60% of customer journeys are untrackable
Technical fixes have limits - Server-side tracking helps but doesn't solve attribution problems
Speed beats precision - Making good decisions quickly outperforms waiting for perfect data
What I'd do differently: I'd implement this framework from day one instead of spending months chasing perfect tracking. The obsession with attribution accuracy delayed strategic decisions and hurt overall performance.
This approach works best for businesses spending over €10K/month on ads with multiple traffic sources. For smaller budgets or single-channel operations, simpler tracking approaches may suffice.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies implementing this framework:
Track trial-to-paid conversion correlation with ad spend
Focus on CAC trends rather than precise attribution
Implement cohort analysis to understand true LTV
Use incrementality testing for channel validation
For your Ecommerce store
For e-commerce stores applying this playbook:
Correlate daily ad spend with revenue by channel
Accept 40-60% "direct" traffic as normal
Use business-level ROAS as your north star metric
Implement incrementality testing for major channels