Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
When I first encountered the "dark funnel" during my work with a B2C e-commerce client, I thought their Facebook Ads were performing miracles. The reported ROAS jumped from 2.5 to 8-9 in just one month after implementing our SEO strategy. My client was ready to throw all their budget at Facebook.
But here's the thing - I knew better. Facebook was taking credit for conversions that were actually happening through organic channels. The customer journey was messier than any attribution model could capture. Someone would Google the problem, browse social media, see a retargeting ad, research on review sites, go through email nurture sequences, and hit multiple touchpoints before converting.
This experience taught me that most businesses are making budget decisions based on lies. Not intentional lies, but the fundamental limitations of traditional attribution models that can't capture the full customer journey.
In this playbook, you'll discover:
Why single-touch attribution is killing your budget allocation
The real-world approach I developed for measuring marketing impact
How to embrace the dark funnel instead of fighting it
Practical frameworks for holistic measurement that actually work
When to trust (and when to ignore) your analytics data
This isn't about perfect tracking - it's about making better decisions with imperfect data. Let me show you how I learned to stop chasing attribution unicorns and start building sustainable growth systems instead.
Reality Check
What attribution models actually measure vs. what they claim
Every marketing guru preaches the same gospel: "Track everything, measure everything, optimize everything." The industry has built an entire ecosystem around the promise of perfect attribution. Google Analytics, Facebook Attribution, multi-touch attribution platforms - they all claim to solve the measurement puzzle.
Here's what the conventional wisdom tells you:
Last-click attribution - Give all credit to the final touchpoint before conversion
First-click attribution - Credit the channel that first brought the customer
Linear attribution - Distribute credit equally across all touchpoints
Time-decay attribution - Give more credit to touchpoints closer to conversion
Position-based attribution - Weight first and last touchpoints more heavily
The promise is seductive: implement the "right" attribution model, and you'll finally know which channels are truly driving results. Marketing mix modeling, incrementality testing, and advanced analytics platforms all claim to provide the holy grail of measurement.
This conventional approach exists because businesses desperately want control and certainty. CFOs want to know exactly which dollar of ad spend generated which dollar of revenue. Marketing teams need to justify budgets and prove ROI. Agencies want to demonstrate their value with clear before-and-after metrics.
But here's where this falls apart in practice: real customer journeys don't follow neat attribution models. Privacy regulations have killed third-party tracking. iOS updates have made mobile attribution nearly impossible. The customer journey now spans multiple devices, platforms, and months or even years of touchpoints.
Most attribution models are measuring shadows, not substance. They're giving you false confidence in data that's fundamentally incomplete.
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 an e-commerce client who was heavily dependent on Facebook Ads. They had a solid product catalog - over 1,000 SKUs - and were generating consistent revenue with a 2.5 ROAS. On paper, everything looked functional.
But I suspected there was a bigger problem hidden beneath the surface. Their entire growth engine depended on Meta's algorithm and ad costs. They had no diversification, no owned channels, and no real understanding of how customers were actually discovering their brand.
That's when I convinced them to invest in a complete SEO overhaul alongside their paid campaigns. We rebuilt their website architecture, optimized their massive product catalog, and created comprehensive content targeting their customer's search intent. Within a month of the SEO implementation going live, something fascinating happened.
Facebook's reported ROAS jumped from 2.5 to 8-9. My client was ecstatic - they thought I'd somehow made their Facebook campaigns incredibly more effective. But I knew exactly what was happening, and it wasn't what Facebook's attribution was telling us.
The reality was that SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins. Here's how the real customer journey worked:
Someone would search for their problem (organic discovery), browse the website (organic engagement), maybe leave without buying (lost customer), then see a Facebook retargeting ad a few days later (paid touchpoint), click through, and convert (Facebook gets the credit).
Facebook's tracking saw "ad click → conversion" and reported it as a paid acquisition. But the truth was that organic search had done the heavy lifting of initial discovery and trust-building. The Facebook ad was just the final nudge in a multi-touch journey that started with SEO.
This experience taught me that attribution models aren't just incomplete - they're actively misleading. They create false confidence in channels that might be taking credit for work done by other parts of your marketing ecosystem.
Here's my playbook
What I ended up doing and the results.
Instead of trying to solve attribution, I learned to embrace what I call "dark funnel acceptance." The goal isn't perfect tracking - it's building a measurement framework that acknowledges reality and helps you make better decisions anyway.
Here's the approach I developed:
Step 1: Shift from Control to Coverage
Rather than trying to track and control every interaction, I focus on expanding visibility across all possible touchpoints. If customers are discovering you through multiple channels, your job is to be present everywhere they're looking, not to track their every move.
For my e-commerce client, this meant building strong organic presence (SEO), maintaining paid visibility (Facebook/Google), nurturing email subscribers, and creating multiple pathways for discovery. More distribution channels mean more opportunities for customers to find and trust your brand, regardless of which touchpoint gets the "credit."
Step 2: Implement Cohort-Based Analysis
Instead of relying on attribution windows, I track business metrics by cohorts over longer time periods. I look at monthly recurring revenue, customer lifetime value, and retention rates rather than obsessing over which ad drove which sale.
For SaaS clients, this means tracking metrics like:
Monthly signups (regardless of source)
Trial-to-paid conversion rates
Customer lifetime value trends
Churn patterns over time
Step 3: Create Proxy Metrics for Channel Health
Since perfect attribution is impossible, I develop channel-specific metrics that indicate health without requiring cross-channel tracking:
SEO: Organic traffic growth, keyword rankings, content engagement
Paid: Cost per click trends, impression share, audience quality
Email: List growth, engagement rates, revenue per subscriber
Content: Share rates, backlinks earned, brand mention volume
Step 4: Use Directional Data, Not Precise Attribution
I look for patterns and trends rather than exact numbers. If I launch a major SEO initiative and overall conversions increase while maintaining healthy metrics across all channels, that's a positive signal - even if I can't prove exactly which organic visits converted.
Step 5: Test Channel Contribution Through Strategic Pausing
When attribution data is questionable, I test channel impact by temporarily pausing spend and measuring the overall business impact. This gives much clearer directional data than any attribution model.
For example, if Facebook claims 70% of conversions but pausing Facebook only drops total conversions by 20%, you know the attribution was inflated. The key is testing strategically and measuring holistic business impact, not just immediate channel metrics.
Channel Coverage
Focus on being present across all customer touchpoints rather than trying to control and track every interaction perfectly.
Cohort Analysis
Track business performance by time-based cohorts rather than relying on attribution windows that miss the full customer journey.
Proxy Metrics
Develop channel-specific health indicators that don't require cross-channel attribution to demonstrate value and performance.
Strategic Testing
Use controlled channel pauses and budget shifts to understand true channel contribution when attribution data is unreliable.
The results of this approach were immediately apparent with my e-commerce client. By focusing on overall business metrics rather than attribution data, we made much better budget allocation decisions.
Instead of dumping more money into Facebook because it "showed" high ROAS, we continued investing in both SEO and paid channels. Over the following six months, the business grew from 300 monthly visitors to over 5,000 - a 10x increase that wouldn't have been possible with a single-channel focus.
More importantly, the business became antifragile. When iOS 14 updates disrupted Facebook tracking for many e-commerce stores, my client's diversified approach meant they weren't dependent on any single channel's attribution accuracy.
The monthly revenue grew consistently because we were measuring what mattered: total customer acquisition, lifetime value trends, and business growth rather than precise attribution percentages.
Other clients who adopted this holistic approach reported similar benefits: better budget allocation decisions, reduced anxiety about tracking pixels and attribution changes, and more sustainable growth that didn't collapse when individual channels had issues.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key insights I learned from abandoning perfect attribution in favor of holistic measurement:
Attribution lies, distribution doesn't. You can't control how customers discover you, but you can control how many places they can discover you.
Dark funnel acceptance is liberating. Once you stop trying to track everything, you can focus on building systems that work regardless of tracking accuracy.
Business metrics trump channel metrics. Customer lifetime value and revenue growth matter more than click-through rates and ROAS calculations.
Diversification beats optimization. Multiple decent channels often outperform one "optimized" channel that could disappear overnight.
Strategic testing reveals truth. Temporarily pausing channels gives you more accurate data than any attribution model.
Proxy metrics provide direction. You don't need perfect data, just enough information to make directionally correct decisions.
Patience pays off. Holistic measurement works over months and quarters, not days and weeks.
The biggest lesson? Stop chasing measurement perfection and start building measurement systems that acknowledge reality. Your customers' journeys are messy, but your measurement framework doesn't have to be.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing holistic marketing measurement:
Track MRR growth and churn trends over attribution data
Focus on trial-to-paid conversion rates by cohort
Measure customer lifetime value patterns
Build multiple acquisition channels early
For your Ecommerce store
For e-commerce stores implementing holistic marketing measurement:
Track total revenue growth vs. individual channel ROAS
Monitor customer acquisition cost trends across all channels
Focus on repeat purchase rates and customer lifetime value
Use first-party data over third-party attribution