Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was working with an e-commerce client who was celebrating their "improved" Facebook ad performance. Their ROAS had jumped from 2.5 to 8-9 in just one month. The marketing team was popping champagne, ready to scale ad spend.
But something felt off. I'd just implemented a comprehensive SEO strategy, and organic traffic was growing significantly. Yet Facebook was claiming credit for nearly every conversion.
That's when I realized the uncomfortable truth: most distribution analytics tools lie to you. They're designed to make their own channels look good, not give you the real picture of how customers actually find your business.
Here's what you'll learn from my experience building a real distribution tracking system:
Why platform attribution is fundamentally broken in 2025
The 3-layer tracking system I use for accurate attribution
How to identify your actual top-performing channels
Tools that tell you the truth (vs. tools that sell you lies)
Why embracing the "dark funnel" changed everything
If you're tired of making marketing decisions based on fantasy metrics, this playbook will show you how to see what's really driving your growth. Check out our growth strategies for more distribution insights.
Industry Reality
What every marketer thinks they know about attribution
Walk into any marketing team meeting, and you'll hear the same confident statements about attribution. "Facebook drove 60% of our conversions this month." "Our Google Ads are crushing it with a 4x ROAS." "SEO only accounts for 15% of revenue."
The industry has built an entire ecosystem around this attribution mythology:
Platform-native analytics - Every ad platform claims credit for conversions that happened after someone saw their ad
Last-click attribution models - Give 100% credit to whoever gets the final touch, ignoring the entire customer journey
UTM parameter obsession - Track every link like it's the source of truth, missing the majority of untracked touchpoints
Single-touch attribution - Pretend customers make decisions after one interaction, when reality involves 7-13 touchpoints
Channel-specific dashboards - Each tool shows you why their channel is performing amazingly
This conventional wisdom exists because it's profitable. Ad platforms need to justify their existence. Marketing teams need clean numbers for reports. Agencies need to show clear ROI for their services.
But here's where it falls short: real customer journeys are messy. Someone might see your LinkedIn ad, Google your company name, read three blog posts, get retargeted on Facebook, click an email, and finally convert on a direct visit. Who gets credit? Usually whoever touched them last, even though the LinkedIn ad started the entire journey.
The result? You optimize for vanity metrics while starving the channels that actually drive discovery. You pour money into retargeting (which gets great attribution) while cutting SEO budgets (which rarely gets proper credit).
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came during a routine client check-in. My e-commerce client was ecstatic about their Facebook ad performance - ROAS had seemingly tripled overnight. But I'd just spent three months implementing a complete SEO overhaul for their 1000+ product catalog.
The timing was suspicious. We'd launched:
20,000+ SEO-optimized product pages across 8 languages
A comprehensive content strategy targeting long-tail keywords
Technical SEO improvements that dramatically improved site speed
Yet according to Facebook, they were suddenly converting customers at 3x the previous rate. The Google Analytics showed a massive spike in "direct" traffic, but Facebook was taking credit for most conversions through their attribution window.
That's when I started digging deeper. I pulled the raw data and found the smoking gun: the customer journey was completely different than what any single platform reported.
A typical customer would:
Discover the brand through Google search (SEO)
Browse multiple products organically
Get retargeted on Facebook
Return via direct visit to complete purchase
Facebook's attribution model gave them 100% credit because the customer had clicked a retargeting ad within 28 days of purchase. But SEO had done the heavy lifting of discovery and consideration.
This was my "attribution lies" moment. I realized that every client I'd worked with was probably making budget decisions based on fundamentally flawed data. The channels getting credit weren't necessarily the channels driving results.
Here's my playbook
What I ended up doing and the results.
After discovering how broken platform attribution was, I developed a 3-layer tracking system that reveals the truth about customer acquisition. This isn't about perfect attribution - that's impossible. It's about getting close enough to make smart decisions.
Layer 1: Holistic Traffic Analysis
I start by looking at traffic patterns across all channels simultaneously. When I implement a major SEO strategy, I track:
Total organic traffic growth (Google Analytics)
"Direct" traffic increases (often misattributed organic)
Brand search volume changes (Google Search Console)
Platform attribution spikes in other channels
The key insight: when one channel's real performance improves, you'll see "phantom" improvements in other channels' reported metrics.
Layer 2: Customer Survey Integration
I implement post-purchase surveys asking: "How did you first hear about us?" The answers rarely match platform attribution data. For my e-commerce client, 40% said "Google search" when platform data credited Facebook.
The survey includes:
First discovery method
Research process
Final decision trigger
Time from awareness to purchase
Layer 3: Channel Isolation Testing
I run experiments where we pause one channel completely and measure the impact on "other" channels. When we paused Facebook ads for my client, their Google Ads "performance" dropped 30% - proving Facebook was getting credit for Google's work.
For SEO specifically, I track:
New vs. returning visitor ratios
Time lag between content publication and conversion spikes
Long-tail keyword performance vs. brand keyword performance
Page-level attribution using heat mapping tools
The Tools I Actually Trust
After testing dozens of attribution tools, here's what works:
Google Analytics 4 - Use Data-Driven Attribution model, not last-click. Set up proper UTM tracking but don't rely on it exclusively.
Triple Whale - For e-commerce specifically, provides better cross-platform attribution than individual platform dashboards.
Hotjar + FullStory - Session recordings reveal the real customer journey that no attribution model captures.
Custom Spreadsheet Tracking - I maintain a manual log of major channel changes and their cross-channel impacts.
The goal isn't perfect attribution - it's making decisions based on directionally accurate data rather than platform fantasy metrics.
Attribution Reality
Facebook claimed 8x ROAS while SEO drove actual discovery
Survey Insights
40% of customers said Google when Facebook claimed credit
Channel Testing
Pausing ads revealed 30% attribution overlap
Tool Selection
Use GA4 Data-Driven + Triple Whale + customer surveys
The results of implementing this 3-layer tracking system were eye-opening. For my e-commerce client, the real attribution picture looked completely different from what any single platform reported.
The Real Channel Performance:
SEO drove 60% of new customer discovery (not the 15% that GA4 credited)
Facebook was excellent at retargeting but terrible at cold acquisition
"Direct" traffic was actually 70% misattributed organic search
Email drove more first-time purchases than any paid channel
This led to a complete budget reallocation. Instead of increasing Facebook spend based on inflated ROAS, we doubled down on SEO content production and email optimization.
Business Impact:
Within 6 months of accurate attribution tracking, overall customer acquisition costs dropped 40% while maintaining the same conversion volume. The client was making decisions based on reality instead of platform fiction.
Most importantly, we stopped starving the channels that actually drove discovery while overfunding the channels that just took credit for the final click.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Building this attribution system taught me seven critical lessons about distribution analytics:
Platform attribution is marketing, not measurement - Every tool wants to prove its value
The dark funnel is bigger than the tracked funnel - Most customer touchpoints go unmeasured
Customer surveys beat algorithmic attribution - Ask people how they found you
Channel isolation reveals attribution theft - Pause channels to see real dependencies
Directionally accurate > precisely wrong - Don't optimize for false precision
Brand searches indicate upper-funnel success - Track branded keyword volume growth
Multiple attribution models tell different stories - Use data-driven models when possible
The biggest mindset shift: stop trying to solve attribution perfectly. Instead, focus on understanding the broad patterns and customer journey flows.
What I'd do differently: implement customer survey tracking from day one, not after discovering attribution problems. The qualitative data is often more valuable than quantitative attribution models.
This approach works best for businesses with longer consideration cycles and multiple touchpoints. For simple, single-touch purchases, platform attribution is usually accurate enough.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies implementing better distribution analytics:
Track free trial sources through post-signup surveys
Monitor organic brand search growth as SEO indicator
Use cohort analysis to understand true channel performance
Implement multi-touch attribution for enterprise sales cycles
For your Ecommerce store
For e-commerce stores building accurate tracking:
Add post-purchase survey asking discovery method
Use GA4 Data-Driven Attribution vs last-click
Track new vs returning customer ratios by channel
Monitor cross-channel attribution overlap through testing