Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
I was working with an e-commerce client who had built what they called "the perfect product catalog" - over 1,000 carefully curated items, beautiful photography, detailed descriptions. Their conversion rate was solid at 2.5%, and they felt confident about their setup.
But here's what shocked me: they were completely dependent on Facebook Ads for traffic. When I dug into their attribution data, I discovered something that changed everything - their "perfect" product-market fit was actually a product-channel mismatch that was bleeding money.
Most businesses obsess over perfecting their product while treating distribution as an afterthought. They'll spend months tweaking features or designs, but can't tell you which of their channels actually drives profitable growth. This backwards approach is why 90% of startups fail - not because their product sucks, but because nobody finds it.
In this playbook, you'll learn:
Why attribution lies and how to measure true channel effectiveness
The framework I use to test and optimize distribution channels
Real metrics from channel experiments that revealed hidden growth opportunities
How to identify when a channel is actually working vs. getting false credit
The counterintuitive approach that 10x'd organic traffic in 3 months
This isn't about vanity metrics or tracking every click. It's about building a measurement system that reveals which channels deserve your budget and which ones are secretly sabotaging your growth. Let's dig into what actually works.
Reality Check
What most businesses get wrong about channel measurement
Walk into any marketing meeting and you'll hear the same mantras repeated like gospel: "Track everything," "Attribution is king," and "Data-driven decisions." The industry has convinced everyone that more tracking equals better insights.
Here's what most "experts" recommend for measuring distribution effectiveness:
Last-click attribution models - Give all credit to the final touchpoint before conversion
Multi-touch attribution - Distribute credit across all touchpoints in the customer journey
UTM parameter tracking - Tag every link to trace traffic sources
Platform-specific analytics - Trust Facebook, Google, and other platforms' reporting
Conversion tracking pixels - Install tracking on every possible touchpoint
This advice exists because it sounds scientific and gives marketers something to present in spreadsheets. The tracking industrial complex has convinced everyone that if you can't measure it perfectly, you can't improve it.
But here's where this falls apart in practice: attribution is fundamentally broken. iOS updates killed pixel tracking. Customers use multiple devices. The customer journey is messier than any attribution model can capture. Most importantly, correlation doesn't equal causation - just because a channel gets "credit" doesn't mean it's actually driving growth.
The result? Businesses make decisions based on incomplete data, over-invest in channels that get false credit, and completely miss their actual growth drivers. They're optimizing for measurement rather than results.
What if I told you there's a better way - one that focuses on business outcomes rather than tracking perfection?
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started working with this e-commerce client, their dashboard told a clear story: Facebook Ads were driving 80% of their revenue with a 2.5 ROAS. Google Analytics showed most traffic coming from social media. Everything pointed to Facebook being their golden channel.
But something felt off. Despite "successful" campaigns, their overall growth had plateaued. They were spending more to get the same results. When I suggested testing other channels, they resisted - why would they invest elsewhere when Facebook was "clearly working"?
The client had over 1,000 SKUs in their catalog. Each product needed time for customers to browse, compare options, and make decisions. But Facebook Ads demand instant decision-making - scroll, see ad, click, buy. There was a fundamental mismatch between their product complexity and the channel's strengths.
Here's what I tried first: optimizing their Facebook campaigns. Better targeting, improved creative, streamlined landing pages. We saw marginal improvements, but nothing dramatic. The ROAS stayed stubbornly around 2.5, and customer acquisition costs kept climbing.
That's when I realized we were treating symptoms, not the disease. The real issue wasn't campaign optimization - it was that Facebook Ads might not be the right channel for a complex product catalog that requires discovery and comparison shopping.
I proposed something that made my client uncomfortable: let's test if organic channels could drive better results. They were skeptical because organic doesn't show up in Facebook's attribution reports. "How will we measure success?" they asked.
That question changed everything. Instead of measuring channel effectiveness through attribution, we decided to measure it through business impact. We would track overall revenue, customer quality, and true incrementality - not just clicks and conversions.
Here's my playbook
What I ended up doing and the results.
Forget attribution models. Here's the framework I developed after years of watching clients make decisions based on misleading data:
Layer 1: Business Impact Measurement
Instead of tracking clicks and conversions, I measure channels by their impact on business fundamentals. For my e-commerce client, this meant tracking total revenue, customer lifetime value, and organic growth rate - not just attributed sales.
We implemented a simple but powerful approach: monitor overall business metrics during channel experiments. When we launched the SEO strategy, Facebook's reported ROAS actually increased from 2.5 to 8-9. But I knew better than to trust this attribution lie.
What was really happening? SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins. The platform couldn't track the full customer journey: Google search → SEO article → multiple site visits → eventual purchase after seeing a retargeting ad.
Layer 2: Incrementality Testing
The gold standard for channel measurement isn't attribution - it's incrementality. I run holdout tests to see what happens when we pause or eliminate specific channels. Does overall revenue drop? By how much? How quickly does it recover?
For my e-commerce client, we gradually reduced Facebook ad spend while investing in SEO. If Facebook was truly driving that 80% of revenue, we should have seen proportional drops in sales. Instead, overall revenue stayed stable while our cost per acquisition plummeted.
This revealed the truth: Facebook was getting credit for sales that would have happened anyway through organic channels. The ads were providing the final touchpoint, not the initial discovery or consideration.
Layer 3: Dark Funnel Recognition
The most important insight from this experience: embrace the dark funnel instead of fighting it. Real customer journeys are messy and multi-touch. Instead of trying to track every interaction, focus on expanding your presence across all possible touchpoints.
I shifted the strategy from "control and attribute" to "cover and measure." More distribution channels meant more opportunities for customers to discover and trust the brand, regardless of which touchpoint got the "credit." The goal became omnipresence, not perfect tracking.
Within three months of implementing this approach, we saw remarkable results. But the most important change wasn't in the numbers - it was in how we made decisions. Instead of optimizing for attribution metrics, we optimized for business growth. Instead of chasing perfect tracking, we focused on expanding effective distribution.
Incrementality Tests
Run holdout experiments to measure true channel impact - pause channels and measure business impact, not attribution reports.
Quality Metrics
Track customer lifetime value and retention by channel - some channels bring better customers even with lower conversion rates.
Dark Funnel
Measure overall brand search volume and direct traffic increases - indicates channels working together beyond attribution.
Business KPIs
Focus on revenue growth and CAC trends rather than click-through rates and conversion attribution across platforms.
The results completely changed how my client thought about channel effectiveness. After implementing this measurement framework, we discovered Facebook's true contribution was much smaller than reported, while organic channels were driving sustainable growth.
Key Metrics After 3 Months:
Overall revenue maintained despite 40% reduction in Facebook ad spend
Customer acquisition cost decreased by 60% as organic channels scaled
Customer lifetime value increased 25% from organic vs. paid traffic
Brand search volume increased 200% indicating stronger brand awareness
But the most valuable outcome was clarity. Instead of making decisions based on conflicting attribution reports, my client could now see which channels actually moved the business forward. They stopped over-investing in expensive paid acquisition and started building sustainable organic growth engines.
The channel that "worked" according to attribution wasn't the channel that worked for the business. This framework revealed the difference and enabled much smarter resource allocation going forward.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven crucial lessons I learned from completely reimagining how we measure distribution effectiveness:
Attribution is correlation, not causation - Just because a channel gets credit doesn't mean it's actually driving growth
Incrementality beats attribution every time - Test what happens when you pause channels to see their true impact
Business metrics matter more than marketing metrics - Focus on revenue, LTV, and CAC trends rather than clicks and conversions
Customer quality varies dramatically by channel - Some channels bring lower conversion rates but higher lifetime value
The dark funnel is real and powerful - Customers interact with multiple touchpoints before converting
Channel-product fit matters more than optimization - The wrong channel won't work no matter how well you optimize it
Distribution coverage beats perfect tracking - Focus on being present where customers are rather than measuring every interaction
The biggest mistake I see businesses make is optimizing for measurement rather than results. Stop chasing perfect attribution and start measuring what actually moves your business forward. The channels that look best in reports aren't always the channels that build sustainable growth.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups applying this channel measurement framework:
Track MRR and expansion revenue by acquisition channel, not just signups
Measure trial-to-paid conversion quality across different traffic sources
Monitor organic brand search volume as indicator of content marketing effectiveness
Test incrementality by pausing paid channels and measuring impact on pipeline
For your Ecommerce store
For e-commerce stores implementing this measurement approach:
Focus on customer lifetime value and repeat purchase rates by channel
Track organic traffic growth as leading indicator of SEO and content success
Measure true incrementality by running holdout tests on paid advertising
Monitor direct traffic and brand search trends for multi-touch attribution insights