Sales & Conversion
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
So I'm sitting in this meeting with an e-commerce client, right? They're celebrating because their Facebook Ads dashboard is showing a 2.5 ROAS. "Great results!" they say. But then I pulled up their actual revenue data, and guess what? Their organic traffic had skyrocketed the same month we launched SEO. Facebook was claiming credit for wins that SEO delivered.
This is the reality most businesses face: your paid ad tracking is probably lying to you. Not because the platforms want to deceive you, but because attribution in 2025 is fundamentally broken. iOS updates, cookie restrictions, and the rise of dark funnel customer journeys have made traditional tracking almost useless.
I've worked with dozens of SaaS startups and e-commerce stores, and I can tell you that the biggest mistake isn't having bad ads—it's believing your tracking data without questioning it. Most founders are making expensive decisions based on completely false attribution.
Here's what you'll learn from my experience fixing tracking disasters:
Why Facebook's reported ROAS jumped from 2.5 to 8-9 when I launched SEO (spoiler: attribution lies)
The "dark funnel" reality that breaks most tracking setups
A simple framework to separate real conversions from platform fantasy
When to trust your data and when to ignore it completely
Why product-channel fit matters more than perfect tracking
Industry Reality
What every marketer believes about attribution
Walk into any marketing conference or scroll through any growth blog, and you'll hear the same mantras repeated like gospel:
"Multi-touch attribution is the holy grail" - Everyone talks about first-touch, last-touch, and attribution modeling like it's going to solve all your problems. The industry pushes expensive attribution software that promises to track every touchpoint in your customer journey.
"Facebook/Google Analytics tells you the truth" - Most marketers religiously check their platform dashboards and make budget decisions based on reported ROAS and conversion metrics. "If Facebook says it converted, it must have converted."
"You need sophisticated tracking to scale" - The conventional wisdom says you can't optimize what you can't measure, so businesses invest heavily in complex tracking setups, pixels, and analytics tools.
"Cross-device tracking solves everything" - The industry solution to attribution problems is always "better tracking" - more pixels, more data, more sophisticated models.
"Direct traffic means people typed your URL" - Most analytics platforms categorize traffic as "direct" when they can't identify the source, and marketers accept this as people directly typing the website URL.
This conventional approach exists because it feels scientific and measurable. Marketers love data, and attribution models give us the illusion of control and understanding. The problem? In today's privacy-first, multi-device, dark funnel world, this approach is fundamentally broken. When iOS 14.5 launched and killed third-party tracking, most businesses kept using the same attribution mindset with half the data. The result? Completely wrong decisions based on incomplete information.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
OK, so this whole tracking nightmare became crystal clear when I started working with this e-commerce client. They were spending about €5K per month on Facebook Ads with a reported 2.5 ROAS. Nothing spectacular, but workable given their margins.
The thing is, their entire growth strategy was built around Facebook Ads. They'd been running campaigns for months, making budget decisions based on what Facebook was telling them. "Scale the audiences that convert, kill the ones that don't" - you know, the standard playbook.
But when I dug into their overall business, something felt off. Their revenue was growing, sure, but not in a way that matched the Facebook attribution. They had this massive catalog - over 1,000 SKUs - and customers needed time to browse and discover products. Facebook Ads demand quick decisions, but their strength was variety and discovery.
That's when I proposed something that made them uncomfortable: let's build a complete SEO strategy alongside the ads and see what happens. Not to replace Facebook, but to test my hypothesis that their attribution was broken.
Within one month of launching the SEO overhaul - website restructuring, content optimization, targeting long-tail keywords - something incredible happened. Facebook's reported ROAS jumped from 2.5 to 8-9. Overnight.
Now, any rational person would know that we didn't suddenly become geniuses at Facebook advertising. What actually happened was that SEO started driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins. People would see a Facebook ad, Google the brand later, browse organically, and convert. Facebook called it a "view-through conversion."
This was my wake-up call about the attribution lie that most businesses are living with.
Here's my playbook
What I ended up doing and the results.
After experiencing this attribution disaster firsthand, I completely changed how I approach tracking for clients. Instead of trying to fix broken attribution, I built a framework that works with reality instead of fighting it.
The "Dark Funnel" Acceptance Strategy
First thing I do now is set the right expectations. I tell every client that their customer journey probably looks like this: Google search → social media → retargeting ad → email → multiple touchpoints → conversion. Facebook might claim credit, but the reality is messier.
Instead of obsessing over which channel "gets credit," I focus on expanding visibility across all possible touchpoints. More distribution channels mean more opportunities for customers to discover and trust your brand, regardless of which touchpoint gets the "credit."
The Platform-Specific Validation Method
Here's the framework I use now: I never trust any single platform's attribution. Instead, I look at platform-specific signals that actually matter:
For Facebook Ads, I ignore ROAS completely for the first month. Instead, I track: link clicks, engagement rates, and cost per click. If people aren't clicking or engaging, the ads aren't working regardless of what attribution claims.
For Google Ads, I focus on impression share and quality scores. If you're not showing up for relevant searches or if Google thinks your ads are irrelevant, attribution won't save you.
For SEO, I track keyword rankings and organic click-through rates. If you're not ranking or if people aren't clicking your titles, the traffic won't convert.
The Business-Level Truth Test
But here's the most important part of my approach: I always validate platform claims against business-level metrics. If Facebook claims 8x ROAS but your overall revenue only grew 20%, something's wrong with the attribution.
I created what I call "channel isolation tests" - periods where we pause specific channels to see the real impact. Turn off Facebook for a week. Did revenue drop dramatically? If not, the attribution was inflated. Launch SEO and watch Facebook's "performance" improve? Classic attribution confusion.
The Product-Channel Fit Reality Check
Most importantly, I learned that tracking problems often mask deeper product-channel fit issues. That e-commerce client? Their products were perfect for SEO discovery but poorly suited for Facebook's quick-decision environment. No amount of tracking sophistication would have fixed that fundamental mismatch.
Now I spend more time on channel selection and less time on attribution perfection. Better to have rough metrics for the right channel than perfect tracking for the wrong one.
Attribution Reality
Facebook claiming credit for SEO wins isn't a bug—it's a feature of broken attribution in the privacy-first era
Dark Funnel Truth
Customer journeys are messy and multi-touch. Accept it instead of trying to track every interaction perfectly
Platform Isolation
Turn off channels temporarily to see real impact. Revenue drops reveal true attribution, not dashboard claims
Business-Level Validation
Always check platform claims against overall revenue. If ROAS jumps but revenue stays flat, attribution is lying
The results of this new approach have been eye-opening across multiple client projects. Instead of chasing perfect attribution that doesn't exist, focusing on business-level validation has led to much better decision-making.
For that e-commerce client, we stopped trying to "fix" Facebook attribution and instead embraced the omnichannel reality. We kept Facebook running for brand awareness and retargeting, but shifted budget toward SEO for discovery. Overall revenue increased 40% over six months, even though individual platform attribution became less "clear."
With SaaS clients, I've seen similar patterns. A B2B startup was convinced their Google Ads were underperforming based on attributed conversions. But when we analyzed their sales cycle, most leads took 3-4 months to convert through multiple touchpoints. Google was generating top-funnel awareness that LinkedIn and email were getting credit for closing.
The most surprising result? Teams make faster, better decisions when they stop obsessing over attribution perfection. Instead of spending hours in attribution rabbit holes, they focus on what actually drives business growth: reaching the right people with the right message through the right channels.
Business-level revenue tracking became the source of truth, not platform dashboards. This shift in perspective has saved clients thousands in wasted ad spend and countless hours of attribution confusion.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I've learned from dealing with broken attribution across dozens of client projects:
Attribution is broken, and it's not getting fixed. iOS updates, cookie restrictions, and privacy regulations have fundamentally changed how tracking works. Accept this reality instead of fighting it.
Platform dashboards lie, but business metrics don't. Always validate platform claims against overall revenue, traffic, and conversion trends. If Facebook claims success but revenue stays flat, trust the revenue.
Focus on channel fit, not tracking perfection. A rough estimate for the right channel beats perfect attribution for the wrong one. Some products work better with discovery (SEO), others with interruption (ads).
Dark funnel is the new normal. Customers research across multiple devices and platforms before converting. Trying to track every touchpoint is impossible and unnecessary.
Business-level testing trumps attribution modeling. Turn channels on and off to see real impact. This gives you better insights than any sophisticated attribution model.
Distribution beats attribution. Instead of perfectly tracking one channel, focus on being present across all the places your customers might discover you. Coverage matters more than credit.
When to ignore your data completely: If platform performance suddenly improves without changes to ads, if attribution doesn't match business results, or if reported conversions seem too good to be true—trust your business instincts over dashboard numbers.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups dealing with attribution confusion:
Track business metrics (MRR, churn, CAC) separately from platform attribution
Focus on lead quality over attributed conversions—platform attribution doesn't predict trial-to-paid rates
Test channels individually with business-level revenue tracking, not dashboard metrics
For your Ecommerce store
For e-commerce stores struggling with tracking:
Validate platform ROAS against actual profit margins and overall revenue growth
Test product-channel fit by analyzing time-to-purchase patterns across different traffic sources
Use business-level metrics to guide budget allocation, not platform attribution claims