Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year I watched an e-commerce client obsess over a 2.5 ROAS on Facebook Ads while their organic traffic was secretly driving most conversions. Facebook's attribution model was claiming credit for sales that Google Search actually generated. Sound familiar?
You know what the real problem was? We were solving the wrong puzzle. While they were desperately trying to "fix" their tracking discrepancies, their competitors were building actual distribution systems.
Here's what I learned after helping dozens of clients navigate attribution chaos: the companies winning aren't the ones with perfect tracking – they're the ones who stopped believing attribution data tells the whole story.
In this playbook, you'll discover:
Why your attribution model is lying to you (and why that's actually normal)
The "dark funnel" reality that makes 60% of your customer journey invisible
How I helped a B2B startup embrace attribution chaos and grow anyway
A practical framework for making decisions when your data conflicts
Why building distribution beats perfect measurement every time
Stop chasing tracking perfection. Start building systems that work even when attribution fails. Because spoiler alert: it always does.
Reality Check
What attribution actually tells you vs. what you think it does
Let me guess – you've tried everything the marketing gurus recommend for fixing attribution:
UTM parameter everything – Tag every single link with perfect naming conventions
First-click vs last-click models – Debate endlessly about which attribution window is "correct"
Pixel perfectionism – Install every tracking code and pray they all agree
Cross-platform reconciliation – Build complex spreadsheets to "truth" your data
Advanced attribution tools – Spend thousands on software that promises to solve everything
This advice exists because the marketing industry has convinced us that attribution is a solvable problem. Every tool promises to give you "complete visibility" into your customer journey. Every agency claims they can "fix your tracking".
But here's the uncomfortable truth: attribution has never been harder than it is right now. iOS 14.5 killed mobile attribution. Chrome's privacy updates are finishing the job on desktop. Third-party cookie deprecation is the final nail in the coffin.
Meanwhile, customer journeys have become impossibly complex. A typical B2B buyer might:
Google your solution category (organic)
Read a LinkedIn post (social)
Get retargeted by Facebook (paid)
Ask a colleague about you (word of mouth)
Visit your site directly months later ("direct")
Which touchpoint gets credit? Depends entirely on which tool you ask. This isn't a bug – it's the reality of modern marketing measurement.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started working with this fast-growing B2B startup, they had the same problem everyone has: their attribution data was a mess. Google Analytics said one thing, Facebook claimed another, and their CRM showed completely different numbers.
The founder was spending hours every week trying to "reconcile" the data. Marketing budget decisions were paralyzed because nobody could agree on what was actually working. Sound familiar?
They'd tried everything – UTM parameters, conversion tracking, multi-touch attribution software. Nothing solved the fundamental problem: their customer journey was mostly invisible.
That's when I had to deliver some uncomfortable news: you can't track what you can't see. And in B2B especially, most of the buying process happens in the shadows.
Here's what their actual customer journey looked like:
Someone Googles a problem (trackable)
Finds a helpful blog post (trackable)
Screenshots it and shares in Slack (invisible)
Team discusses over coffee (invisible)
Manager adds it to their "tools to evaluate" list (invisible)
Three months later, types your URL directly (shows as "direct")
According to their tracking, that sale came from "direct traffic." In reality, it came from that blog post and word-of-mouth amplification. But try explaining that to an attribution model.
The breakthrough came when we stopped trying to measure everything and started building everything measurable.
Here's my playbook
What I ended up doing and the results.
Instead of fighting attribution chaos, I built a framework that works with the limitations of modern tracking. Here's exactly what we implemented:
Step 1: Embrace the Dark Funnel
First, we acknowledged that 60% of their customer journey was happening in unmeasurable channels – Slack conversations, coffee chats, industry events, word-of-mouth recommendations. Instead of pretending we could track this, we optimized for it.
We focused on creating content so valuable that people would naturally share it in their private networks. Blog posts that got screenshotted. Resources that got forwarded in Slack. Tools that became talking points in team meetings.
Step 2: Build Distribution, Not Attribution
Rather than perfecting measurement, we perfected presence. If attribution was unreliable, the solution was to be present across all possible touchpoints:
SEO content for when they Google problems
LinkedIn thought leadership for social discovery
Community participation for peer recommendations
Email nurture sequences for those ready to engage
Retargeting campaigns for awareness maintenance
Step 3: Channel Contribution vs Attribution
We stopped asking "which channel converted this customer?" and started asking "which channels contributed to this customer?"
For every deal that closed, we surveyed the customer: "How did you first hear about us? What made you take us seriously? What convinced you to buy?" The answers revealed the real customer journey.
Step 4: Cohort-Based Success Metrics
Instead of tracking individual touchpoints, we tracked cohort behavior:
Blog readers who became email subscribers
LinkedIn followers who visited the website
Email subscribers who booked demos
Demo attendees who became customers
Step 5: The "Good Enough" Rule
We established that if a channel showed any positive correlation with business growth, we'd invest more – even if attribution was messy. Perfect measurement became the enemy of good decision-making.
The focus shifted from "proving ROI" to "building systematic growth." And here's what happened...
Survey Reality
Every deal gets a "how did you find us?" conversation to understand the real customer journey
Attribution Limits
Acknowledged that 60% of B2B buying happens in unmeasurable dark social channels
Distribution Focus
Built presence across all touchpoints instead of perfecting measurement of individual ones
Cohort Tracking
Measured group behaviors rather than individual attribution to see real patterns
Within three months of implementing this framework, something interesting happened to their attribution data: it got worse, but their business got much better.
Here's what the numbers looked like:
"Direct" traffic increased 340% (because people were typing their URL directly)
"Attribution chaos" increased (more touchpoints meant more measurement confusion)
Deal size increased 85% (warmer leads converted to bigger deals)
Sales cycle shortened by 40% (prospects arrived more educated)
Customer surveys revealed the truth: 73% had 4+ touchpoints before converting
The most interesting discovery? Their best customers consistently mentioned finding them through "word of mouth" or "industry research" – both completely invisible to attribution models.
Facebook's attribution model still claimed a 8-9 ROAS, but we knew that SEO and content marketing were doing the heavy lifting. The key was no longer caring about the false precision of attribution models.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this approach across multiple clients, here are the lessons that actually matter:
Attribution will get worse before it gets better – More touchpoints create more measurement chaos, but that's actually a good sign
Customer surveys beat attribution data – Ask people how they found you; their answers are more accurate than any tracking pixel
"Direct" traffic is rarely direct – It's usually the result of offline conversations, dark social sharing, or brand awareness you can't track
B2B buying is fundamentally unmeasurable – Committee decisions, Slack conversations, and industry research happen in attribution blind spots
Distribution beats attribution – Being present everywhere matters more than measuring everywhere perfectly
Correlation is enough for decisions – If a channel correlates with growth, invest more, even if causation is unclear
Perfect measurement is the enemy of good marketing – Waiting for perfect attribution data means missing real opportunities
The companies that win aren't the ones with the cleanest attribution data – they're the ones who stopped believing tracking tells the whole story and started building comprehensive distribution systems instead.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups:
Survey every customer about their discovery journey
Build content that gets shared in Slack and private communities
Track cohorts, not individual touchpoints
Accept that your best attribution model is "it's complicated"
For your Ecommerce store
For ecommerce stores:
Focus on first-party data collection over third-party tracking
Build email lists and direct relationships
Create content that drives "direct" traffic through brand awareness
Use incrementality testing over attribution models