Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I watched a client celebrate their "improved" Facebook ad performance while secretly knowing the truth was much more complex. Their Facebook attribution dashboard showed a ROAS jump from 2.5 to 8-9 after we implemented an SEO strategy. The reality? Facebook was claiming credit for organic wins in what's called the "dark funnel."
This isn't uncommon. Most businesses are living in attribution fantasyland, believing their tracking tools give them the complete picture of customer journeys. But here's what I learned after working with dozens of clients: attribution lies, distribution doesn't.
The real customer journey looks nothing like your neat attribution reports. It's messier, spans multiple touchpoints, and often happens outside your tracking capabilities. While everyone chases the perfect attribution solution, smart businesses are adapting their strategy to work with—not against—this reality.
In this playbook, you'll discover:
Why traditional attribution tools fail in today's privacy-first world
The real experiments I ran to understand cross-channel impact
A practical approach to measuring what actually matters
Tools and frameworks that work when tracking breaks down
How to make marketing decisions without perfect attribution
This isn't about finding the "perfect" tracking solution—it's about building a measurement system that actually helps you grow your business.
Attribution Reality
What the industry keeps promising (but can't deliver)
Walk into any marketing conference and you'll hear the same promises: "Complete customer journey visibility," "True multi-touch attribution," and "End-to-end conversion tracking." The attribution software industry has built a $2 billion market on the premise that you can track everything.
Here's what every tool vendor will tell you:
First-touch attribution shows initial awareness drivers
Last-touch attribution reveals final conversion triggers
Multi-touch models distribute credit across all touchpoints
Advanced algorithms can solve attribution complexity
Real-time dashboards provide complete journey insight
This conventional wisdom exists because businesses desperately want control and clarity. CFOs demand ROI proof, marketing teams need budget justification, and everyone wants to optimize based on "data-driven" decisions. The attribution tool industry feeds this desire by promising the impossible: complete visibility into complex human behavior.
But here's where it falls apart in practice:
iOS 14.5 killed Facebook's tracking accuracy. Third-party cookie deprecation broke cross-site tracking. GDPR and privacy regulations limited data collection. Cross-device journeys became invisible. The "dark funnel" grew larger as customers research privately before converting.
Yet most businesses still make major marketing decisions based on attribution reports that capture maybe 60% of the actual customer journey. They're optimizing for ghosts while real opportunities slip through the cracks.
The industry solution? More sophisticated tools, better algorithms, and "AI-powered attribution." But sophistication can't solve a fundamental data collection problem. When the foundation is cracked, building higher doesn't help—it just creates a more expensive illusion of control.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came through an e-commerce client who was heavily dependent on Facebook Ads. Their setup looked solid on paper: 2.5 ROAS, €50 average order value, decent traffic numbers. Most marketers would call that acceptable performance.
But when I dug into their business model, the math didn't add up. Their margins were tight, customer acquisition costs were climbing, and they were sitting on over 1,000 SKUs that weren't getting discovered through their single-channel approach. The Facebook attribution dashboard showed clear "wins," but the bank account told a different story.
The first experiment: Adding SEO while keeping Facebook running
Instead of optimizing their existing Facebook campaigns, I convinced them to run a parallel SEO strategy. We completely overhauled their website structure, optimized their massive product catalog for search, and created content targeting long-tail keywords that Facebook ads couldn't efficiently capture.
Here's where it got interesting. Within a month of the SEO implementation, their Facebook attribution dashboard started reporting ROAS numbers of 8-9. The client was ecstatic—until I explained what was actually happening.
The attribution mirage revealed
Facebook wasn't suddenly performing better. SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins. Customers were discovering products through search, researching on multiple devices, then eventually clicking a retargeting ad before purchasing. Facebook saw the final click and took full credit.
This taught me that the real customer journey wasn't "see ad → buy product"—it was much messier:
Google search for the problem
Social media browsing and discovery
Retargeting ad exposure
Review site research
Email nurture sequence
Multiple touchpoints across channels
The attribution tools couldn't capture this complexity. They were optimizing for the wrong metrics while the real growth drivers remained invisible.
Here's my playbook
What I ended up doing and the results.
Step 1: Embrace the Dark Funnel Reality
Instead of fighting attribution limitations, I developed a framework that works with them. The key insight: stop trying to track and control every interaction. Start focusing on expanding visibility across all possible touchpoints, regardless of which gets the "credit."
My approach became: "Distribution everywhere they already are."
Step 2: Build Channel Independence Testing
For this e-commerce client, I created separate measurement periods:
Month 1: Facebook ads only (baseline measurement)
Month 2: Facebook ads + SEO implementation
Month 3: SEO only (paused Facebook to isolate impact)
Month 4: Both channels running with holdout testing
This revealed that SEO was driving 40% more qualified traffic than Facebook attribution suggested. The attribution tools were giving Facebook credit for SEO conversions because customers often saw both touchpoints before purchasing.
Step 3: Implement Multi-Signal Measurement
Rather than relying on single attribution tools, I created a measurement stack using:
Business-Level Metrics:
Total revenue growth by time period
Customer acquisition cost trends
Lifetime value changes by acquisition source
Brand search volume increases
Channel-Specific Signals:
Organic traffic growth and keyword rankings
Direct traffic increases (often attributed SEO impact)
Email list growth from organic signups
Social media engagement and follower growth
Step 4: Create Attribution-Independent Optimization
Instead of optimizing based on last-click attribution, I focused on:
Channel-specific KPIs: SEO rankings, ad click-through rates, email open rates
Business impact metrics: Overall conversion rate, average order value, customer retention
Leading indicators: Brand search trends, direct traffic patterns, engagement quality
This approach revealed opportunities that attribution tools missed. For example, certain product categories performed significantly better through organic search than paid ads, but this insight was buried in Facebook's attribution claims.
Channel Testing
Run clean A/B tests by turning channels on/off during specific periods to isolate true impact, not just attribution claims.
Business Metrics
Focus on overall revenue, CAC, and LTV trends rather than channel-specific attribution reports that often mislead.
Multi-Signal Stack
Combine platform analytics, Google Analytics, business intelligence tools, and manual tracking for complete visibility.
Attribution Modeling
Use multiple attribution models (first-touch, last-touch, linear) and compare them to understand the full customer journey story.
The Real Numbers Behind Attribution Reality:
After implementing this multi-signal approach across several e-commerce clients, the patterns became clear. Traditional attribution tools typically missed 30-50% of the actual customer journey, especially for businesses with longer sales cycles or multiple product lines.
For the e-commerce client mentioned, here's what we discovered:
Facebook attribution claimed: 80% of conversions from paid ads
Reality through holdout testing: 45% paid ads, 35% SEO, 20% direct/email
Business impact: 60% increase in total revenue when both channels ran together
The synergy effect was real but invisible to single-channel attribution. Customers discovered products through SEO, got retargeted through Facebook, and converted through a combination of touchpoints that no single tool could accurately track.
Unexpected Discovery: The highest-value customers typically had the messiest attribution paths. They researched extensively, compared alternatives, and converted weeks after first discovery. These customers had 40% higher lifetime values but were often mis-attributed to last-click channels.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The 5 Hard Truths About Cross-Channel Attribution:
Perfect attribution is impossible. Privacy regulations, cross-device usage, and the dark funnel mean you'll never see the complete customer journey. Plan for 60-70% visibility at best.
Attribution tools optimize for themselves. Facebook wants to show Facebook wins. Google wants to show Google wins. They're not neutral measurement systems—they're marketing tools for their own platforms.
Business metrics trump attribution metrics. If overall revenue, profit margins, and customer quality are improving, don't obsess over which channel gets "credit." Focus on what drives business results.
Correlation often matters more than causation. When multiple channels run simultaneously and business grows, the synergy effect is often more valuable than individual channel performance.
The best strategy assumes broken attribution. Build channel independence, diversify traffic sources, and optimize for business outcomes rather than attribution scores. This approach works whether tracking is perfect or completely broken.
When This Approach Works Best: Complex products, longer sales cycles, multiple touchpoints, privacy-conscious audiences, and businesses with diverse channel strategies.
When Traditional Attribution Still Works: Simple products, single-session conversions, single-channel strategies, and businesses with complete data control.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS Startups:
Track trial-to-paid conversion by acquisition source, not just initial attribution
Measure brand search growth as leading indicator of attribution-independent growth
Use cohort analysis to understand true customer lifetime value by channel mix
Focus on user activation metrics rather than last-click conversion attribution
For your Ecommerce store
For Ecommerce Stores:
Implement server-side tracking for more accurate conversion data
Use first-party data collection to build attribution-independent customer profiles
Track repeat purchase rates by acquisition source for true channel value
Monitor direct traffic increases as proxy for brand-building channel impact