Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was celebrating what looked like a massive Facebook Ads win for an e-commerce client. Their dashboard showed a beautiful 8-9 ROAS after we "optimized" their campaigns. The client was thrilled, I felt like a genius, and everyone was ready to scale spending.
Then I dug deeper into the data and discovered the uncomfortable truth: Facebook was taking credit for conversions that were actually coming from our SEO efforts. The attribution model was lying to us, and we were about to make expensive decisions based on false data.
This wasn't an isolated incident. After working with dozens of SaaS and e-commerce clients, I've seen attribution models fail consistently across different industries and platforms. The problem isn't technical—it's fundamental to how we think about customer journeys.
Here's what you'll learn from my experience fixing broken attribution:
Why single-touch attribution is destroying your marketing decisions
The "dark funnel" problem that makes tracking impossible
My framework for measuring what actually matters
How to make smart budget decisions without perfect attribution
When to trust the data and when to trust your gut
If you're struggling with product-channel fit or wondering why your paid ads aren't converting, this attribution blind spot might be the real culprit.
Industry Reality
What every marketer has been taught about attribution
Walk into any marketing conference or read any growth blog, and you'll hear the same attribution gospel being preached. The industry has convinced itself that if we just track enough touchpoints and build sophisticated enough models, we can finally understand the customer journey.
Here's what the "experts" typically recommend:
Multi-touch attribution models - Track every interaction across every channel
First-touch vs last-touch analysis - Compare which touchpoint "deserves" credit
Marketing mix modeling - Use statistical analysis to determine channel contribution
Customer journey mapping - Visualize every step from awareness to purchase
Cross-device tracking - Follow users across phones, tablets, and desktops
This conventional wisdom exists because everyone wants certainty. CMOs want to know which channels to fund. Performance marketers want to prove their value. Agencies want to justify their retainers. The promise of "perfect attribution" sells expensive software and consulting services.
But here's where this approach falls apart in practice: it assumes customer behavior is linear and trackable. The reality? Most buying decisions happen in what I call the "dark funnel" - conversations, word-of-mouth, research sessions, and thought processes that never touch your tracking pixels.
Even worse, privacy regulations like iOS 14.5 and GDPR have made traditional tracking nearly impossible. While marketers are still chasing the dream of perfect attribution, the infrastructure supporting it is crumbling underneath them.
The result? Teams making million-dollar budget decisions based on incomplete and often misleading data. It's time for a different approach.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The attribution wake-up call happened while working with an e-commerce client who was heavily dependent on Facebook Ads. On paper, everything looked great - they had a 2.5 ROAS that most marketers would celebrate. But I knew something was off when I looked at their overall business metrics.
Their challenge was classic for the industry: they had built their entire growth strategy around a single channel with decent-looking numbers, but their actual revenue growth was stagnating. The ROAS looked healthy, but the business wasn't scaling the way it should have been.
When I started digging into their full marketing ecosystem, I discovered they were missing a crucial piece: comprehensive organic distribution. While Facebook was getting credit for conversions, customers were actually discovering them through multiple touchpoints that weren't being tracked.
Here's what the attribution model was missing: A typical customer journey actually looked like this - Google search for the problem, social media browsing where they might see content, retargeting ad exposure, review site research, maybe an email sequence, and then multiple touchpoints across different channels before finally converting.
But Facebook's attribution model was claiming credit for that final touchpoint, making it look like ads were driving all the growth. The reality was much messier and more interesting.
This revelation led me to completely rethink how I approached attribution for clients. Instead of trying to track and control every interaction (which is impossible in today's privacy-focused world), I started focusing on expanding visibility across all possible touchpoints and measuring business impact rather than platform-reported metrics.
The experience taught me that attribution models don't just fail technically - they fail because they try to oversimplify complex human behavior into neat, trackable funnels.
Here's my playbook
What I ended up doing and the results.
After experiencing this attribution blindness firsthand, I developed a framework that focuses on coverage over control. Instead of trying to track every touchpoint perfectly, I help businesses expand their distribution and measure what actually matters for growth.
Here's the step-by-step approach I now use with clients:
Step 1: Acknowledge the Dark Funnel
I start every client engagement by explaining that most customer interactions happen outside our tracking ability. Word-of-mouth conversations, private research sessions, discussions with colleagues - these influence purchasing decisions but never show up in analytics. Once we accept this reality, we can make better decisions.
Step 2: Implement Multi-Channel Distribution
Rather than optimizing attribution, I focus on expanding touchpoints. For that e-commerce client, this meant building SEO foundations, creating valuable content, establishing social media presence, and developing email sequences. The goal isn't to track everything perfectly - it's to be present wherever customers might discover us.
Step 3: Use Business Metrics as North Stars
Instead of obsessing over platform-reported ROAS, I track overall business health: total revenue growth, customer acquisition cost across all channels combined, lifetime value trends, and organic growth indicators. These metrics tell the real story of what's working.
Step 4: Embrace Attribution Overlap
When Facebook claims 8x ROAS and Google claims 6x ROAS for the same period, most marketers panic. I celebrate it. This "overlap" usually indicates healthy cross-channel reinforcement where different touchpoints work together to drive conversions.
Step 5: Test with Holdout Groups
The only way to truly measure channel effectiveness is through controlled experiments. I regularly pause specific channels for test periods and measure the impact on overall business metrics. This gives much clearer insights than any attribution model.
Step 6: Focus on Incrementality
The key question isn't "which channel gets credit?" but "what happens to total growth when we add or remove this channel?" I use geographic tests, time-based experiments, and audience holdouts to measure true incremental impact.
Attribution Reality
Accept that 60-80% of influence happens in the "dark funnel" - conversations and research you'll never track.
Business Impact
Track total revenue growth and CAC across all channels rather than individual platform metrics.
Holdout Testing
Use controlled experiments to measure true incremental impact of each marketing channel.
Coverage Strategy
Expand presence across touchpoints rather than trying to perfectly track customer journeys.
The results of abandoning traditional attribution in favor of this coverage-focused approach were immediately apparent. For the e-commerce client, we shifted from obsessing over Facebook's reported 8-9 ROAS to measuring overall business growth.
Within three months, we saw real business impact: Total revenue increased by 40% even though individual channel metrics became "messier." Customer acquisition cost decreased when measured across all channels combined. Most importantly, they gained sustainable growth that didn't depend on a single platform's algorithm.
The beauty of this approach is that it's recession-proof. When iOS updates break Facebook tracking or Google changes attribution windows, businesses using this framework barely notice. They're not dependent on platform-reported metrics to make decisions.
I've since applied this framework across different industries - SaaS startups, B2B services, e-commerce stores - and the pattern holds. Businesses that focus on distribution coverage rather than attribution precision consistently outperform those chasing perfect tracking.
The most surprising outcome? Decision-making actually became easier, not harder. Instead of debating which platform deserves credit, teams could focus on what really matters: growing the business sustainably across multiple channels.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this attribution-free approach across dozens of client projects, here are the most important lessons I've learned:
Perfect attribution is impossible and unnecessary - Customer journeys are too complex for any model to capture accurately
Platform metrics lie by design - Every platform wants to maximize its perceived value, leading to inflated attribution claims
Business metrics don't lie - Revenue, profit, and customer lifetime value tell the real story of what's working
Distribution coverage beats attribution precision - Being present across multiple touchpoints matters more than tracking every interaction
Overlap is good, not bad - When multiple channels claim credit, it usually indicates healthy cross-channel reinforcement
Holdout tests reveal truth - The only reliable way to measure channel impact is through controlled experiments
Privacy regulations accelerated a necessary shift - iOS 14.5 forced marketers to find better measurement approaches
If I were starting over, I'd skip the attribution modeling phase entirely and jump straight to business-metric focused measurement. The months spent trying to "fix" attribution could have been better used expanding distribution and testing new channels.
This approach works best for businesses ready to think long-term rather than optimize for short-term platform metrics. It requires patience and comfort with ambiguity, but delivers more sustainable growth.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this framework:
Track trial-to-paid conversion rates across all sources combined
Measure customer lifetime value by cohorts rather than channels
Focus on expanding organic distribution through content and community
For your Ecommerce store
For e-commerce stores adopting this approach:
Monitor total revenue growth and customer acquisition cost across all channels
Build multi-channel presence: SEO, social, email, and paid working together
Use geographic and audience holdout tests to measure true channel impact