Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I walked into a client meeting where the marketing team was celebrating their "success." Facebook Ads were reporting a 9 ROAS, Google Ads claimed 4.5 ROAS, and everyone was happy with their attribution reports.
The problem? Their actual revenue wasn't matching these metrics. Something was seriously broken.
After diving deep into their analytics setup, I discovered what most businesses miss: attribution is lying to you. Multiple channels were claiming credit for the same conversions, SEO was getting zero credit despite driving significant traffic, and their entire marketing strategy was built on fictional data.
This isn't uncommon. Most companies are optimizing based on attribution models that fundamentally misunderstand how customers actually discover and buy from businesses. The result? Wasted ad spend, killed profitable channels, and growth that hits a wall.
Here's what you'll learn from my framework for measuring true channel fit:
Why traditional attribution models create product-channel fit disasters
How to build a measurement system that reveals your actual customer journey
The "dark funnel" reality that's hiding 70% of your conversions
A step-by-step framework to identify which channels actually drive revenue
How to optimize for distribution coverage instead of attribution credit
Industry Reality
What every marketer believes about attribution
Walk into any marketing team meeting and you'll hear the same conversation. "Facebook is driving 60% of our conversions." "Google Search has the highest ROAS." "Our email campaigns are performing amazingly." Everyone's staring at their attribution dashboards like they're gospel truth.
Here's what the industry typically recommends for channel measurement:
Last-click attribution - Give credit to the final touchpoint before conversion
First-click attribution - Credit the initial discovery channel
Multi-touch attribution - Distribute credit across multiple touchpoints
UTM parameter tracking - Tag everything to track source performance
Platform-specific analytics - Trust Facebook Ads Manager, Google Analytics, etc.
This conventional wisdom exists because it's simple and gives teams clear numbers to optimize. Marketing managers love being able to say "Facebook drives 40% of revenue" because it feels scientific and actionable.
But here's where it falls apart: real customer journeys are messy. Someone might see your Facebook ad, Google your company name, visit via organic search, get retargeted on Instagram, receive an email, and finally convert after visiting your website directly. Traditional attribution models pick one or two touchpoints and ignore this complex reality.
The result? You're making budget decisions based on incomplete data, killing channels that are actually working, and doubling down on tactics that only appear successful because they're getting credit for other channels' work.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came during a project with a B2B SaaS client who was heavily dependent on Facebook Ads. Their reported metrics looked solid - 2.5 ROAS with consistent lead generation. But they had a hidden vulnerability: their entire growth engine depended on Meta's algorithm and rising ad costs.
The client was a typical SaaS startup, around $500K ARR, selling project management software to small teams. They'd been running Facebook Ads for 18 months and felt confident about their attribution setup. Everything was tracked with UTM parameters, pixels were firing correctly, and their dashboard showed clear channel performance.
But when I dug deeper, red flags appeared everywhere. Direct traffic was mysteriously high - about 40% of their conversions were labeled as "direct" in Google Analytics. Their customer surveys revealed people were Googling the company name after seeing ads, skewing the attribution. Worse, when I analyzed their customer journey data, the average buyer had 7+ touchpoints before converting.
Then I tried something that changed everything: I built a comprehensive SEO strategy running parallel to their paid ads. Within a month, something strange happened. Facebook's reported ROAS jumped from 2.5 to 8-9, even though I hadn't touched their ad campaigns.
The truth hit like a truck: SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins. Customers were seeing Facebook ads, searching for the company later, finding them through organic results, and converting. Facebook took full credit, while SEO got none.
This wasn't a Facebook problem - it was an attribution problem. The client's entire understanding of what drove their growth was fundamentally wrong.
Here's my playbook
What I ended up doing and the results.
After discovering how broken traditional attribution was, I developed a framework that focuses on measuring distribution coverage instead of attribution credit. Here's exactly how I built a system that reveals true channel performance:
Step 1: Map the Real Customer Journey
Instead of trusting platform data, I started with customer surveys. I added a simple question to their post-purchase flow: "How did you first hear about us?" and "What other places did you see us before signing up?" The responses revealed the true complexity - most customers had 5-7 touchpoints across different channels.
Step 2: Build a Coverage-Based Measurement System
Rather than fighting over attribution credit, I measured how well each channel covered their target audience. I tracked:
Reach metrics - How many target customers each channel exposed
Frequency metrics - How often customers encountered each channel
Awareness lift - Brand search volume increases after channel activity
Conversion assistance - Which channels appeared in winning customer journeys
Step 3: Implement "Dark Funnel" Tracking
I accepted that 70% of the customer journey happens in what I call the "dark funnel" - places you can't track. Instead of ignoring this, I built systems to illuminate it:
Brand mention monitoring across social platforms
Branded search volume tracking in Google Search Console
Direct traffic analysis by landing page and user behavior
Customer interview programs to understand offline conversations
Step 4: Test Channel Isolation
To understand true channel impact, I ran isolation tests. I paused Facebook Ads for two weeks while keeping everything else running. The result? Total conversions dropped by only 15%, not the 60% that attribution suggested. This proved Facebook was getting credit for conversions it wasn't actually driving.
Step 5: Optimize for Distribution, Not Attribution
The breakthrough came when I stopped trying to track individual customer paths and started optimizing for comprehensive market coverage. Instead of asking "which channel converted this customer?" I asked "are we visible everywhere our customers look for solutions?"
This meant expanding SEO efforts, building a content strategy, engaging in industry conversations, and yes, continuing paid ads - but with realistic expectations about their true impact.
Channel Isolation
Test what happens when you pause each channel for 1-2 weeks. The real impact is often much lower than attribution suggests.
Dark Funnel Mapping
Track brand searches, direct traffic patterns, and customer interviews to understand untrackable touchpoints.
Coverage Metrics
Measure reach and frequency across channels instead of fighting over conversion credit.
Customer Journey Reality
Survey customers post-purchase about all touchpoints. The truth is messier than any attribution model shows.
The results of implementing this framework were eye-opening. The client's understanding of their growth completely shifted, and more importantly, their actual growth accelerated.
Within three months of implementing the coverage-based measurement system:
Total organic traffic increased 300% as we built the SEO foundation
Cost per acquisition dropped 40% as we optimized the full funnel, not just paid channels
Customer lifetime value increased because customers coming through multiple touchpoints showed higher engagement
Marketing became recession-proof as the business wasn't dependent on any single paid channel
The most surprising outcome? Facebook Ads actually performed better when supported by strong SEO and content efforts. Instead of competing with organic channels, paid ads amplified them. Customers who saw Facebook ads and later found the company through organic search showed higher conversion rates and lower churn.
The business went from a single-channel dependency to a robust, multi-channel growth engine that could weather algorithm changes, increased ad costs, and market shifts.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This project taught me that most marketing teams are optimizing for the wrong metrics. Here are the key lessons that transformed how I approach channel measurement:
Attribution is always wrong - Accept that tracking individual customer journeys is impossible and optimize for market coverage instead
The dark funnel is real - 70% of customer research happens in untrackable places. Build systems to illuminate this, don't ignore it
Channels work together, not in isolation - Paid ads amplify SEO, content builds trust for paid traffic, email nurtures all channels
Brand search is your best metric - Increases in branded search volume indicate true channel effectiveness better than any attribution model
Customer interviews beat analytics - Regular post-purchase surveys reveal the real customer journey better than any tracking pixel
Test isolation, not optimization - Pause channels to understand true impact instead of just optimizing based on attribution data
Distribution beats perfect targeting - Being visible everywhere your customers look is more valuable than precise attribution tracking
I'd do one thing differently: start with customer interviews from day one. The biggest waste of time was trying to perfect tracking systems when simple customer surveys would have revealed the truth much faster.
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:
Start with post-signup surveys asking about discovery touchpoints
Track brand search volume in Google Search Console as your primary channel effectiveness metric
Build SEO alongside paid channels, don't choose one or the other
Focus on product-market-channel fit over perfect attribution
For your Ecommerce store
For ecommerce stores measuring channel fit:
Implement post-purchase "How did you hear about us?" surveys immediately
Test channel pause experiments during low-season periods
Track direct traffic behavior patterns to understand dark funnel activity
Optimize for omnichannel coverage instead of single-channel attribution