Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Picture this: You wake up to see Facebook claiming your ROAS jumped from 2.5 to 8-9 overnight. No changes to the ads, no budget increases, nothing. Your first thought? "Holy shit, I've cracked the code!" Your second thought, if you're experienced enough, is probably closer to the truth: "This can't be right."
I've seen this exact scenario play out with multiple clients, and here's what's actually happening - your attribution is fundamentally broken. Facebook is taking credit for conversions that came from other channels. It's not a bug, it's a feature designed to make their platform look better than it actually is.
The uncomfortable truth? Most businesses are making major budget decisions based on attribution data that's as reliable as a weather forecast for next year. And the cost of these tracking discrepancies isn't just confused reporting - it's misallocated budgets, killed campaigns that were actually working, and doubled-down spend on channels that are getting false credit.
Here's what you'll learn from my experience dealing with attribution chaos across multiple client projects:
Why Facebook's attribution model is designed to lie to you (and how to spot it)
The real reasons behind tracking discrepancies that nobody talks about
How I discovered the "dark funnel" was driving 70% of actual conversions
My framework for embracing attribution uncertainty instead of fighting it
The distribution strategy that made attribution problems irrelevant
Because here's the thing - the businesses winning right now aren't the ones with perfect tracking. They're the ones who've learned to make smart decisions despite imperfect data.
Reality Check
What the tracking "experts" won't tell you
Walk into any marketing conference or open any growth hacking blog, and you'll hear the same tired advice about tracking discrepancies: "Fix your pixels," "Set up proper UTM parameters," "Use first-party data," "Implement server-side tracking." The industry treats attribution problems like they're technical issues that can be solved with better implementation.
Here's what every marketing guru will tell you to do:
Implement conversion tracking properly - Make sure your Facebook pixel, Google Analytics, and other tracking codes are firing correctly
Use UTM parameters religiously - Tag every single link so you can track traffic sources accurately
Set up server-side tracking - Bypass browser-based limitations with backend conversion tracking
Choose the right attribution model - Debate whether first-touch, last-touch, or multi-touch attribution is "correct"
Audit your tracking regularly - Run monthly reports to identify and fix discrepancies
This conventional wisdom exists because it feels logical and actionable. Marketing teams love having something concrete to "fix." Agency reports look more professional when they can point to technical solutions. And tracking platform vendors obviously benefit from selling you more sophisticated measurement tools.
But here's where this approach falls short in practice: You're trying to solve a fundamental problem with tactical solutions. The issue isn't your tracking setup - it's that the modern customer journey is inherently unmeasurable. People research on their phones, discuss with colleagues, sleep on decisions, get retargeted across multiple devices, and finally convert on a different browser three weeks later. No amount of pixel optimization is going to capture that complexity.
The industry keeps pushing technical solutions because admitting the truth would be uncomfortable: attribution is mostly guesswork, and the smartest businesses have learned to make decisions despite that uncertainty, not because they've eliminated it.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about the moment I realized everything I thought I knew about attribution was wrong. I was working with an e-commerce client who had been running Facebook ads for months with a consistent 2.5 ROAS - not great, but profitable enough to keep the campaigns running.
The client was getting frustrated with the performance, especially because their margins were tight. They were considering killing the Facebook ads entirely and focusing their budget elsewhere. That's when I suggested we try something different - implementing a comprehensive SEO strategy to build organic distribution channels.
Within a month of launching the SEO overhaul, something "magical" happened. Facebook's reported ROAS jumped from 2.5 to 8-9. The client was ecstatic. "You're a genius!" they said. "The Facebook ads are finally working!"
But I knew better. We hadn't changed anything about the Facebook campaigns. Same budget, same audiences, same creative. The timing was too perfect - right when our SEO traffic started gaining momentum.
Here's what was actually happening: Facebook's attribution model was claiming credit for conversions that were driven by our SEO efforts. Someone would search for the brand or product, land on our optimized pages through organic search, and convert. But because they had seen a Facebook ad at some point in the past 30 days, Facebook took full credit for the sale.
This experience taught me that tracking discrepancies aren't just technical glitches - they're a fundamental feature of how attribution models work. Every platform wants to make itself look as good as possible, which means overclaiming credit for conversions. The result? Your "high-performing" channels might be getting false credit, while your actually effective channels remain invisible in the reports.
The most eye-opening part was when I dug deeper into the data. Our direct traffic had increased significantly, organic search was bringing in qualified leads, and even referral traffic had picked up. But Facebook was taking credit for all of it because of their default attribution window. We weren't just dealing with minor discrepancies - we were dealing with a complete misrepresentation of what was driving growth.
Here's my playbook
What I ended up doing and the results.
After this revelation, I completely changed how I approach attribution for clients. Instead of trying to fix the "broken" tracking, I developed a framework that assumes attribution will always be imperfect and makes smart decisions despite that uncertainty.
Step 1: Embrace the Dark Funnel Reality
The first mindset shift was accepting that most of the customer journey happens in what I call the "dark funnel" - interactions you can't track. Someone might:
See your Facebook ad on mobile, then Google your brand name on desktop later
Discuss your product with colleagues who do their own research
Browse your site in incognito mode after clearing cookies
Get a referral recommendation that never shows up in your analytics
Instead of fighting this reality, I started planning for it. The goal became building visibility across all possible touchpoints, rather than trying to track and control every interaction.
Step 2: Focus on Distribution, Not Attribution
Rather than obsessing over which channel gets credit, I shifted focus to expanding distribution channels. The philosophy became simple: if customers can discover you in more places, attribution becomes less critical because you're not dependent on accurately measuring any single channel.
For that e-commerce client, instead of trying to "fix" the Facebook attribution, we doubled down on the strategy that was actually working:
Complete website restructuring for SEO optimization
Development of a full e-commerce SEO strategy
Content creation focused on search intent, not just brand messaging
The results spoke for themselves, even if Facebook wanted to take credit for everything.
Step 3: Create Multiple Truth Sources
Instead of relying on any single attribution model, I started cross-referencing multiple data sources:
Google Analytics for overall traffic patterns and behavior flow
Platform native analytics (Facebook, Google Ads) for spend efficiency
Customer surveys asking "How did you first hear about us?"
Branded search volume as a proxy for overall awareness
Direct traffic trends indicating brand strength
When I saw direct traffic increasing alongside "improved" Facebook performance, it confirmed that something else was driving growth.
Step 4: Test Channel Pausing, Not Optimization
The most reliable way to understand true channel impact became strategic pausing. Instead of endlessly optimizing based on questionable attribution data, I would temporarily pause channels and measure the actual impact on overall conversions.
With that e-commerce client, when we eventually paused Facebook ads for two weeks, overall conversions dropped by only 15% - nowhere near what the "8-9 ROAS" would suggest. This confirmed that Facebook was massively overclaiming credit.
Step 5: Build for Coverage, Not Control
The final piece of my framework was shifting from trying to control and track every touchpoint to ensuring broad coverage across all possible discovery channels. This meant:
SEO for people actively searching
Content marketing for education and awareness
Social media for community and engagement
Email marketing for retention and referrals
Paid ads for quick reach and testing
The goal wasn't to perfectly track each channel's contribution - it was to ensure that no matter how customers wanted to discover and research your business, you'd be findable and compelling.
False Confidence
Attribution models are designed to make platforms look good, not give you accurate data
Channel Testing
Pause campaigns to see real impact rather than trusting reported metrics
Dark Funnel
Accept that most customer journeys happen outside trackable interactions
Coverage Strategy
Build visibility everywhere rather than optimizing based on questionable attribution
The results of embracing attribution uncertainty rather than fighting it were immediately obvious. That e-commerce client saw their overall revenue grow significantly, not because Facebook suddenly started working better, but because we'd built a diversified acquisition engine that didn't depend on any single channel working perfectly.
More importantly, budget allocation decisions became much smarter. Instead of pouring more money into Facebook because it reported great ROAS, we invested in the SEO strategy that was actually driving growth. Direct traffic continued climbing, organic search brought in higher-intent customers, and the business became much less vulnerable to any single platform's algorithm changes or policy updates.
The most surprising outcome was how much less stressful marketing became. When you accept that attribution will always be imperfect, you stop making panicked decisions based on daily reporting fluctuations. Instead, you focus on long-term trends and overall business growth.
And yes, we kept running Facebook ads - but at a much smaller scale and with realistic expectations. They became one tool in a larger toolkit rather than the hero channel that reporting claimed they were.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key insights that changed how I approach tracking discrepancies for every client:
Attribution is marketing, not measurement - Every platform's attribution model is designed to make that platform look as good as possible. Treat reported metrics as biased marketing materials, not objective truth.
The dark funnel is bigger than you think - In complex B2B sales or considered purchases, 70%+ of the customer journey happens outside of trackable interactions. Plan for this reality rather than fighting it.
Distribution beats attribution - Businesses that focus on being discoverable everywhere worry less about tracking everything perfectly. Broad coverage reduces dependence on any single channel's accuracy.
Channel pausing reveals truth - The most reliable way to understand true channel impact is temporarily pausing it and measuring actual business impact, not relying on attribution reports.
Multiple truth sources tell the real story - Cross-reference platform analytics with customer surveys, branded search volume, and direct traffic trends to get a fuller picture.
Sudden "improvements" are usually false - When channel performance dramatically improves without any changes to that channel, another activity is likely driving the growth.
Technical fixes won't solve fundamental problems - Better pixels and UTM parameters can't track cross-device, cross-platform customer journeys that span weeks or months.
The biggest mindset shift was learning to make confident marketing decisions despite imperfect data, rather than waiting for perfect attribution that will never come.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups dealing with tracking discrepancies:
Track trial signup sources separately from paid conversion sources
Survey customers during onboarding about discovery methods
Monitor branded search volume as awareness proxy
Focus on content distribution over perfect attribution
For your Ecommerce store
For ecommerce stores managing attribution chaos:
Cross-reference platform ROAS with overall revenue trends
Test channel pausing during low-stakes periods
Build SEO foundation to reduce paid dependency
Use customer surveys at checkout for discovery insights