Growth & Strategy
Personas
Ecommerce
Time to ROI
Short-term (< 3 months)
Last month, I sat in a client meeting where their Facebook Ads dashboard showed a beautiful 4.2 ROAS. The marketing manager was celebrating. The founder was ready to double the budget. There was just one problem: their actual revenue had barely moved in three months.
This wasn't an isolated incident. After working with dozens of e-commerce stores and SaaS companies, I've seen this story play out repeatedly. The ad platforms tell you one thing, your bank account tells you another, and somehow you're supposed to make business decisions based on this conflicting data.
The uncomfortable truth? Most ad tracking is fundamentally broken, and the attribution models we rely on are often complete fiction. But here's what the industry won't tell you: this isn't necessarily your fault, and there are ways to navigate this mess.
In this playbook, you'll discover:
Why attribution lies became the norm (and why platforms benefit from this)
The real-world experiments I ran to expose tracking discrepancies
A practical framework for making decisions despite imperfect data
Alternative measurement approaches that actually work
When to trust (and when to ignore) your ad platform data
This isn't about finding the "perfect" tracking solution—it's about building a business that thrives despite the attribution chaos. Because at the end of the day, what matters isn't what your dashboard says, it's what your customers actually do.
Reality Check
Why everyone pretends attribution works
Walk into any marketing conference or open any growth blog, and you'll hear the same mantras repeated endlessly: "Track everything!" "Attribution is king!" "Data-driven decisions only!" The industry has built an entire mythology around the precision of digital advertising measurement.
Here's what every marketing guru will tell you is essential:
Multi-touch attribution models that track every touchpoint in the customer journey
Pixel-perfect tracking across all platforms and devices
Real-time optimization based on conversion data
Cross-platform attribution to understand the full funnel
Detailed customer journey mapping from first click to purchase
This conventional wisdom exists because it sounds logical and gives marketers the illusion of control. Platform vendors love promoting these ideas because it keeps you dependent on their tools and justifies their ad spend recommendations.
The problem? Most of this is theater. The tracking infrastructure that powers these recommendations is fundamentally flawed, privacy regulations have broken traditional attribution, and the "data-driven" decisions we're making are often based on incomplete or misleading information.
Yet the industry continues to pretend that attribution works perfectly because admitting the truth—that we're often flying blind—would undermine the entire performance marketing ecosystem. But what if there's a better way to navigate this reality?
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 an e-commerce client who was spending €15k monthly on Facebook Ads. Their dashboard showed consistent 2.5 ROAS, but something felt off. Despite months of "successful" campaigns, their overall revenue growth was minimal.
This was a fashion retailer with over 1,000 SKUs—exactly the type of complex catalog where attribution typically breaks down. They were running multiple campaigns across Facebook, Google, and some influencer partnerships, all claiming credit for the same sales.
The client's challenge wasn't unique: they had limited margins, a competitive market, and needed real growth, not vanity metrics. But every platform was telling them a different story about which channels were working.
My first instinct was to implement "better" tracking. I spent weeks setting up enhanced conversion tracking, configuring UTM parameters, and building attribution models. It was a complete waste of time.
The more sophisticated I made the tracking, the more contradictory the data became. Facebook claimed credit for sales that Google said came from organic search. Google attributed conversions to ads that users had clicked weeks ago. Meanwhile, a significant portion of revenue was showing up as "direct" traffic with no attribution at all.
That's when I realized the fundamental problem: we were trying to force a square peg into a round hole. Facebook Ads' quick-decision environment was fundamentally incompatible with this client's browsing-heavy shopping behavior.
The real issue wasn't the tracking—it was the mismatch between their product catalog complexity and the advertising channels we were using.
Here's my playbook
What I ended up doing and the results.
Instead of trying to fix the tracking, I designed an experiment to expose exactly how inaccurate our attribution really was. Over three months, we systematically tested different measurement approaches while running controlled campaigns.
The Hold-Out Test
First, we ran a geographic holdout test. We paused all Facebook Ads in specific regions for two weeks while maintaining them everywhere else. If Facebook's attribution was accurate, we should have seen a proportional drop in revenue in those regions.
The result? Revenue dropped by only 15% in holdout regions, while Facebook had been claiming credit for 60% of sales. This meant Facebook was massively over-attributing its impact.
The Channel Pause Experiment
Next, we systematically paused different advertising channels for one week each while tracking overall revenue impact. We discovered that:
Pausing Google Ads had minimal impact on overall sales
Stopping Facebook Ads reduced revenue by about 25% (not the 60% they claimed)
The biggest impact came from pausing email marketing (which wasn't being "credited" by any ad platform)
The Direct Traffic Deep Dive
We also analyzed what was actually driving the "direct" traffic that made up 40% of their revenue. Through customer surveys and enhanced analytics, we discovered that most "direct" visitors were actually:
Returning customers who had bookmarked the site
Word-of-mouth referrals from satisfied customers
People who had seen their products on social media but typed the URL directly
The Real Revenue Driver
The most shocking discovery? The majority of sustainable revenue growth was coming from organic channels that weren't being tracked by any advertising platform. SEO, email marketing, and customer retention were driving more actual revenue than all the paid advertising combined.
This led us to completely restructure their growth strategy, moving budget away from attribution-obsessed paid channels toward building sustainable organic growth engines.
Channel Testing
We ran systematic holdout tests across different regions and timeframes to measure real impact
Platform Lies
Facebook claimed 60% attribution but holdout tests showed only 15% actual impact
Hidden Drivers
Email marketing drove more revenue than all paid ads but got zero platform attribution
Measurement Truth
Customer surveys revealed the real sources behind "direct" traffic attribution black holes
The results of our attribution reality test completely changed how we approached marketing measurement. Instead of chasing perfect attribution, we found ways to make better decisions with imperfect data.
The Real Impact Numbers:
Facebook's claimed ROAS: 2.5x | Actual measured impact: 1.4x
Google Ads claimed attribution: 40% | Real revenue impact when paused: 8%
Email marketing platform attribution: 12% | Actual revenue impact: 45%
The timeline was eye-opening. It took only two weeks of holdout testing to expose the attribution lies that had been guiding months of budget decisions. Within six weeks, we had enough data to completely restructure their marketing mix.
The most unexpected outcome? Once we stopped optimizing for platform-reported metrics and started focusing on overall business growth, their actual profit margins improved by 35%. They were spending less on advertising but generating more sustainable revenue.
This experience taught me that attribution accuracy matters less than business results accuracy. The best measurement approach isn't the most sophisticated—it's the one that leads to better business decisions.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After running dozens of similar experiments across different clients, here are the key lessons that emerged:
Embrace the Dark Funnel - Accept that 40-60% of customer journeys will never be perfectly trackable, and build your strategy accordingly
Test Real Impact, Not Reported Metrics - Holdout tests and channel pause experiments reveal more truth than any attribution model
Focus on Business Metrics - Overall revenue, profit margins, and customer lifetime value matter more than platform-specific ROAS
Don't Ignore Organic Growth - Email, SEO, and word-of-mouth often drive more sustainable growth than paid advertising
Attribution Theater is Expensive - The time and money spent on sophisticated tracking could be better invested in actual growth activities
Customer Surveys Beat Pixels - Asking customers how they found you is often more accurate than any tracking system
Platform Incentives Drive Attribution Lies - Ad platforms are businesses that benefit from over-reporting their impact
The biggest mindset shift? Stop trying to track everything perfectly and start focusing on what you can control. The companies that grow fastest aren't the ones with the best attribution—they're the ones that build strong products and sustainable distribution channels.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies dealing with attribution challenges:
Focus on trial-to-paid conversion rates over ad attribution
Track customer lifetime value by acquisition cohort
Use NPS scores to identify organic growth drivers
Test product-led growth before scaling paid ads
For your Ecommerce store
For e-commerce stores questioning their tracking accuracy:
Run geographic holdout tests to validate platform claims
Survey customers about discovery channels
Focus on profit margins over reported ROAS
Invest in email and retention over attribution tracking