Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
When I first started managing campaigns for a growing e-commerce client, I was obsessed with perfect attribution. Every dollar needed to be tracked, every touchpoint mapped, every conversion credited to the right channel. I built elaborate dashboards, implemented multiple tracking pixels, and spent hours reconciling data between platforms.
Then something interesting happened. After implementing a comprehensive SEO strategy for this client, their Facebook ROAS jumped from 2.5 to 8-9 overnight. Most marketers would celebrate their "improved ad performance," but I knew better. The reality? SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins.
This experience taught me that most businesses oversimplify the customer journey. They want to believe it's linear: see ad → buy product. But real customer behavior is messy. A typical journey actually looks like Google search → social media browsing → retargeting ad → review site research → email nurture sequence → multiple touchpoints across channels.
Here's what you'll learn from my journey through attribution hell and back:
Why traditional attribution models lie to you (and why that's actually okay)
The dark funnel reality that makes perfect tracking impossible
What metrics actually matter for multi-channel growth
A practical framework for measuring what matters without going insane
How to embrace attribution chaos and still make smart decisions
If you're tired of chasing attribution ghosts and want a more practical approach to multi-channel measurement, this playbook will save you months of frustration. Let's dive into why I stopped believing in attribution fairy tales and what I do instead.
Industry Reality
What every marketer thinks they need
The marketing industry has sold us a beautiful lie: that we can track every customer interaction across every channel with perfect precision. Walk into any agency or read any marketing blog, and you'll hear the same gospel:
"Attribution is everything." Set up your pixels correctly, use the right attribution models, and you'll know exactly which channels drive your best customers. First-touch, last-touch, linear, time-decay, data-driven – pick your poison and achieve marketing nirvana.
The typical attribution setup looks like this:
Install Facebook Pixel, Google Analytics, and platform-specific tracking
Choose an attribution model (usually last-click because it's "simple")
Build dashboards showing which channels get "credit"
Optimize budget allocation based on attributed conversions
Scale the "winning" channels and cut the "losers"
This approach exists because we crave certainty. Marketing leaders want clean reports showing exactly where their budget went and what it returned. Agencies need to prove their value with concrete attribution data. Everyone wants to believe in the myth of the trackable customer journey.
But here's where conventional wisdom falls apart: the dark funnel is real, and it's much bigger than most people realize. Studies show that 45-70% of the customer journey happens in untrackable spaces – direct URL entries, word-of-mouth recommendations, offline conversations, private messages, and cross-device browsing that no pixel can follow.
The result? You're optimizing for the 30-55% of the journey you can see while ignoring the majority that happens in the shadows. It's like judging a movie by watching random scenes and calling yourself a film critic.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My perspective on attribution changed during a project with a Shopify e-commerce client who was heavily dependent on Facebook Ads. Their numbers looked decent on paper – a 2.5 ROAS with a €50 average order value. Most marketers would call that acceptable, but something felt off.
The client had over 1,000 SKUs, all quality products, but their strength was variety – customers needed time to browse, compare, and discover the right product. Facebook Ads' quick-decision environment was fundamentally incompatible with this shopping behavior. Despite "decent" ROAS, the client wasn't seeing the growth they expected.
Instead of doubling down on ad optimization, I proposed a complete SEO overhaul. We rebuilt the website architecture, optimized for discoverability, and created content targeting long-tail keywords for their extensive catalog. The goal was to capture customers who had the time and intent to explore their full range.
Then the weird thing happened. Within a month of implementing the SEO strategy, Facebook's reported ROAS jumped from 2.5 to 8-9. The client was ecstatic about their "improved ad performance," but I knew what was really happening.
The attribution models were lying. Here's what was actually occurring:
Customers would Google search for specific products (organic traffic)
Browse the site and maybe add items to cart
Leave without purchasing
See a Facebook retargeting ad later
Click the ad and complete the purchase
Facebook claimed credit for the entire sale, but SEO did the heavy lifting. The organic search created awareness and consideration. The retargeting ad just provided the final nudge. Without the SEO foundation, those Facebook ads would have continued struggling with cold traffic.
This taught me a fundamental truth: attribution models don't show you what's working – they show you what gets credit according to arbitrary rules set by platforms with obvious incentives to overstate their impact.
Here's my playbook
What I ended up doing and the results.
After the Facebook attribution fiasco, I completely rebuilt my approach to multi-channel measurement. Instead of chasing perfect attribution, I developed a framework that embraces the chaos while still providing actionable insights.
Step 1: Track Holistic Business Metrics First
Rather than starting with channel attribution, I begin with overall business health metrics:
Total revenue growth month-over-month
Customer acquisition cost trends
Lifetime value improvements
Organic vs. paid traffic ratios
Brand search volume increases
These metrics tell you if your marketing is working overall, regardless of which platform claims credit.
Step 2: Use Attribution as Directional Data, Not Gospel
I still set up proper tracking, but I treat attribution data like weather forecasts – helpful for general direction, but don't bet your life on the specifics. Here's what I actually track:
Platform-Level Performance: Each channel gets evaluated on its own merits within its ecosystem. Facebook ROAS, Google Ads conversion rates, email open rates, SEO traffic growth – but I don't try to compare them directly through attribution.
Assisted Conversions: Google Analytics' assisted conversion reports show which channels play supporting roles. This reveals the dark funnel activity that pure attribution misses.
Time-Lag Analysis: Understanding the typical delay between first touch and conversion helps explain attribution discrepancies. B2B SaaS might have 30-90 day consideration periods, while e-commerce could be 1-7 days.
Step 3: Implement Incrementality Testing
The only way to truly understand channel impact is through controlled experiments:
Geographic Holdouts: Run campaigns in some regions while holding back others, then compare performance. This shows true incremental impact beyond attribution claims.
Budget Pulse Tests: Temporarily increase or decrease spend in individual channels and measure the overall business impact, not just platform-reported metrics.
Channel Pauses: Strategically pause channels for short periods to see how other channels compensate. This reveals hidden interdependencies.
Step 4: Map the Real Customer Journey
Instead of relying on attribution models, I survey actual customers about their journey:
"How did you first hear about us?"
"What made you decide to purchase today?"
"What other touchpoints do you remember?"
This qualitative data often reveals the true influence network that no pixel can capture.
Step 5: Focus on Channel Ecosystem Health
Rather than fighting for attribution, I optimize each channel for its unique strengths:
SEO: Focus on long-term brand building and capturing high-intent searches. Don't expect immediate conversions, but measure traffic quality and engagement depth.
Paid Ads: Optimize for platform-specific metrics first, then evaluate contribution to overall business growth. Accept that some impact will be uncredited.
Email: Measure engagement and lifetime value improvements, not just direct conversion attribution.
Social Media: Track brand awareness, engagement quality, and assisted conversions rather than fighting for last-click credit.
Attribution Reality
First-party data collection through surveys and customer interviews reveals more actionable insights than any attribution model. You learn the real reasons people buy and the actual touchpoints that influenced them.
Channel Ecosystems
Each channel has unique strengths that don't always show up in attribution data. SEO builds long-term brand equity, paid ads provide quick validation, and email nurtures relationships – optimize for these strengths, not attribution credit.
Incrementality Testing
The only way to truly measure channel effectiveness is through controlled experiments – geographic holdouts, budget pulse tests, and strategic pauses reveal true incremental impact beyond what platforms claim.
Dark Funnel Acceptance
45-70% of the customer journey happens in untrackable spaces. Instead of fighting this reality, build strategies that acknowledge and work with the dark funnel rather than pretending it doesn't exist.
The results of embracing attribution chaos were surprisingly liberating. Instead of spending hours reconciling data discrepancies, I could focus on what actually moved the business forward.
For the e-commerce client: By recognizing that SEO was the real driver behind improved Facebook performance, we doubled down on content creation and site optimization. Organic traffic increased 300% over six months, and the sustainable growth reduced their dependence on paid advertising.
For other clients: This approach revealed hidden channel synergies. One B2B SaaS client discovered that their LinkedIn content strategy dramatically improved Google Ads quality scores because it increased branded search volume. Attribution models would never show this connection.
Business Impact: Clients stopped making reactionary budget decisions based on attribution fluctuations. Instead, they invested in long-term channel ecosystem health, leading to more sustainable and predictable growth.
The biggest win? Marketing teams became less stressed and more strategic. When you stop chasing attribution perfection, you can focus on building genuinely effective marketing systems that work with customer behavior rather than against it.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from two years of attribution reality checks:
Attribution models optimize for what's measurable, not what's valuable. Just because something gets credit doesn't mean it deserves it.
Platform incentives corrupt attribution data. Facebook, Google, and others have billions of reasons to overstate their impact.
Customer behavior is messier than any tracking system. Cross-device browsing, word-of-mouth, and offline conversations will always create dark funnel gaps.
First-party data beats third-party attribution. Surveys and customer interviews reveal the real journey better than any pixel.
Channel synergies matter more than individual performance. SEO improves paid ad performance, email enables social retargeting, content builds brand equity that helps everything.
Incrementality testing is the only source of truth. Controlled experiments show what attribution models can't – true causal impact.
Holistic business metrics provide better guidance than channel attribution. Focus on overall CAC, LTV, and revenue growth rather than last-click credit disputes.
When this approach works best: Multi-channel businesses with longer consideration periods, complex product catalogs, or B2B sales cycles.
When to stick with traditional attribution: Single-channel businesses, impulse purchase products, or situations where the customer journey is genuinely simple and trackable.
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 chaos:
Track trial-to-paid conversion rates by source, but don't ignore assisted conversions
Survey churned users to understand the real customer journey
Focus on overall CAC trends rather than channel-specific attribution
Use cohort analysis to understand long-term channel quality
For your Ecommerce store
For e-commerce stores struggling with attribution:
Implement post-purchase surveys asking "How did you hear about us?"
Use Google Analytics assisted conversions to see channel interactions
Test geographic holdouts to measure true incremental impact
Optimize for channel ecosystem health, not attribution credit