Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Three months into working with an e-commerce client, I discovered something that made me question everything I thought I knew about conversion tracking. Their Facebook Ads dashboard showed a 2.5 ROAS, which looked decent on paper. But when I dug deeper into their actual revenue attribution, the numbers told a completely different story.
Here's what really happened: SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins. The "dark funnel" was alive and well, and traditional tracking was failing miserably.
Most businesses are swimming in data but drowning in insights. They're making critical budget decisions based on attribution models that lie more than they reveal truth. After working across multiple channels and seeing the same patterns repeatedly, I realized the problem isn't just technical—it's philosophical.
In this playbook, you'll discover:
Why attribution models consistently mislead business owners
The real customer journey that analytics platforms miss
A practical framework for measuring true channel impact
How to build distribution coverage instead of chasing perfect tracking
When to trust (and when to ignore) your analytics data
This isn't about setting up another dashboard. It's about understanding what's actually driving your business forward. Let's dive into why the industry's obsession with perfect attribution is missing the bigger picture.
Reality Check
The attribution tracking myth everyone believes
Walk into any marketing meeting, and you'll hear the same refrain: "We need better attribution." "Which channel is really driving conversions?" "How do we measure the true ROI of each touchpoint?"
The industry has convinced us that perfect attribution is not only possible but essential. Here's what every marketing guru will tell you:
Multi-touch attribution models can track the complete customer journey
UTM parameters and tracking pixels will capture every interaction
Advanced analytics platforms can solve attribution challenges
First-party data collection will replace third-party tracking limitations
Marketing mix modeling can determine optimal budget allocation
This conventional wisdom exists because it makes everyone feel in control. Marketing teams can justify their budgets with clean reports. Agencies can show clear ROI to clients. Tool vendors can sell expensive attribution software.
But here's where it falls apart: the customer journey is messy, non-linear, and increasingly invisible. A typical buyer might Google your problem, see a retargeting ad, check reviews, visit your site directly, ask colleagues, attend a webinar, and finally convert weeks later after clicking an email link.
Which channel gets credit? Usually the last one—even though it might have been the least influential touchpoint. The industry's obsession with tracking perfection is causing businesses to optimize for the wrong metrics while missing the bigger strategic picture.
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, they were heavily reliant on Facebook Ads with what appeared to be solid performance metrics. The dashboard showed a 2.5 ROAS with a €50 average order value, and most marketers would call that acceptable. But something didn't add up when I looked at their overall business growth.
The client's challenge wasn't their products—they had over 1,000 SKUs, all quality items. The real issue was the mismatch between their catalog complexity and Facebook Ads' quick-decision environment. While most successful paid campaigns thrive on 1-3 flagship products, this client's strength was variety. Customers needed time to browse, compare, and discover the right product.
When I began implementing a complete SEO strategy alongside their existing paid efforts, something fascinating happened. Within a month of launching the organic approach, Facebook's reported ROAS jumped from 2.5 to 8-9. Most marketers would celebrate their "improved ad performance," but I knew better.
The reality? SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins. Customers were discovering the brand through search, researching products on the website, and then converting days later—often after seeing a retargeting ad that Facebook counted as the conversion driver.
This experience taught me that attribution models don't just provide incomplete data—they actively mislead business decisions. The client was ready to increase their Facebook ad spend based on the "improved" performance, which would have been a costly mistake. Instead, we needed to embrace what I call the "dark funnel" reality.
Here's my playbook
What I ended up doing and the results.
Instead of chasing perfect attribution, I developed a framework that focuses on distribution coverage and business reality. Here's the systematic approach I use with clients to understand true channel performance:
Step 1: Establish Business Reality Baselines
Before diving into attribution models, I establish clear business metrics that can't be gamed by tracking systems. This includes total revenue, customer acquisition cost across all channels combined, and overall growth rate. These become our north star metrics that attribution models must align with.
For my e-commerce client, I tracked their overall conversion rate, average order value, and customer lifetime value before and after each major channel addition. This gave us a clear picture of actual business impact regardless of what individual platform dashboards claimed.
Step 2: Implement Multi-Source Data Collection
Rather than relying on a single attribution model, I collect data from multiple sources and look for patterns. This includes platform analytics (Google Ads, Facebook Ads), website analytics (Google Analytics), customer surveys, and direct customer feedback.
I set up UTM tracking for all paid campaigns, but I also implemented customer surveys asking "How did you first hear about us?" and "What convinced you to purchase?" The survey data often revealed touchpoints that tracking pixels missed entirely.
Step 3: Create Channel Isolation Tests
The most reliable way to measure channel impact is through controlled tests. I pause individual channels for 2-4 week periods and measure the impact on overall business metrics. This approach reveals true channel incrementality rather than relying on correlation-based attribution.
When we paused Facebook Ads for this client, overall revenue dropped by roughly 15%—much less than the platform claimed to be driving. Meanwhile, when we reduced SEO content production, organic traffic and conversions declined more significantly than expected.
Step 4: Build the Distribution Coverage Framework
Instead of optimizing for perfect tracking, I focus on maximizing distribution coverage across all possible touchpoints. The goal is being present wherever customers might discover your brand, regardless of which touchpoint gets "credit" for the final conversion.
This means embracing what I call "distribution everywhere they already are" rather than "build it and they will come." For this client, we expanded from just Facebook Ads to include SEO, email marketing, retargeting, and content marketing—creating multiple pathways for customer discovery.
Step 5: Focus on Cohort-Based Analysis
Rather than analyzing individual conversions, I track customer cohorts based on their acquisition time period. This reveals the long-term impact of different marketing strategies without getting lost in attribution model complexity.
Customers acquired during periods of heavy SEO investment showed higher lifetime values and lower churn rates compared to those acquired primarily through paid ads, even though the immediate attribution data didn't reflect this difference.
Testing Framework
Use controlled channel pauses to measure true incrementality rather than relying on correlation-based attribution models.
Survey Integration
Customer surveys reveal touchpoints that tracking pixels miss entirely—ask "How did you first hear about us?" in your onboarding.
Business Metrics
Focus on overall revenue, customer acquisition cost, and growth rate as north star metrics that attribution models must align with.
Coverage Strategy
Build distribution presence across all possible touchpoints rather than optimizing for perfect tracking of individual conversions.
The results from this approach were eye-opening and validated my suspicion about attribution model limitations. When we measured actual business impact rather than platform-reported metrics, the picture looked completely different.
The channel isolation tests revealed that Facebook Ads were contributing about 15% of total revenue—significantly less than the 40-50% the platform claimed through its attribution model. Meanwhile, SEO was driving much more business impact than traditional "last-click" attribution suggested.
More importantly, the combination of channels created synergistic effects that individual attribution models couldn't capture. Customers who discovered the brand through SEO were more likely to convert from retargeting ads. Email subscribers who came from organic search had higher lifetime values than those acquired through paid campaigns.
The distribution coverage approach led to more sustainable growth. Instead of being dependent on a single channel's algorithm changes or cost increases, the business had multiple pathways for customer acquisition. When iOS 14.5 impacted Facebook ad performance industry-wide, this client was largely insulated because they had built a diversified acquisition strategy.
Most telling was the customer feedback data. When asked about their purchase decision, customers consistently mentioned multiple touchpoints: "I found you on Google, saw your ad on Facebook, checked reviews, and then bought after getting your email." Traditional attribution would have given 100% credit to the email, completely missing the crucial role of the other touchpoints.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience fundamentally changed how I approach conversion tracking and taught me several crucial lessons that challenge industry orthodoxy:
Attribution models lie by design - They're built to give clean answers to messy questions, leading to false confidence in channel performance.
The dark funnel is the real funnel - Most customer journeys happen outside trackable touchpoints, making perfect attribution impossible.
Business metrics trump platform metrics - Overall revenue and growth trends are more reliable than individual platform attribution reports.
Distribution coverage beats optimization - Being present across multiple channels matters more than perfectly optimizing individual touchpoints.
Customer surveys reveal hidden truth - Direct feedback often contradicts what tracking data suggests about the customer journey.
Synergy effects are unmeasurable but real - Channels work together in ways that attribution models can't capture.
Control tests beat correlation analysis - Pausing channels reveals true incrementality better than attribution modeling.
The biggest lesson? Stop believing in "build it and they will come." Start believing in "distribute everywhere they already are." Your job isn't to track every touchpoint perfectly—it's to be present wherever your customers might discover you and trust that business growth will reflect successful distribution strategy.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this tracking approach:
Track trial-to-paid conversion rates by acquisition channel over time
Survey new users about discovery touchpoints during onboarding
Focus on expanding distribution coverage rather than optimizing attribution
Use cohort analysis to measure long-term customer value by acquisition period
For your Ecommerce store
For e-commerce stores building better conversion tracking:
Implement post-purchase surveys asking about discovery and decision factors
Track customer lifetime value trends alongside immediate conversion metrics
Test channel pauses to measure true incrementality
Build presence across multiple discovery channels rather than optimizing single touchpoints