Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I was analyzing the marketing data for a B2B SaaS client when something didn't add up. Their fancy multi-touch attribution model was claiming that their LinkedIn ads had a 8x ROAS, while their actual revenue was declining month over month.
The marketing team was celebrating their "improved performance," but I knew we were looking at a mirage. After digging deeper, I discovered that their SEO efforts were driving 60% of actual conversions, but Facebook's attribution model was claiming credit for organic wins. Classic attribution lie.
This experience taught me something uncomfortable: multi-touch attribution is fundamentally broken, and most businesses are making decisions based on fantasy numbers. The dark funnel is real, and our tracking tools are living in denial.
Here's what you'll learn from my journey through attribution hell:
Why multi-touch attribution fails in today's privacy-first world
The specific attribution lies I uncovered across multiple client projects
My alternative framework that actually predicts business outcomes
How to embrace the dark funnel instead of fighting it
Practical steps to build attribution that works for decision-making
If you're tired of chasing attribution ghosts and want to understand what's really driving your growth, this playbook will save you months of confusion. Let's dive into why the industry standard is broken and what actually works.
Industry Reality
What every marketer believes about attribution
Walk into any marketing conference and you'll hear the same gospel: "Multi-touch attribution is the holy grail of marketing analytics." The industry has built an entire mythology around perfect customer journey tracking.
Here's what the conventional wisdom preaches:
Track every touchpoint - Map the complete customer journey from first touch to conversion
Weight interactions properly - Use sophisticated models to distribute credit across channels
Optimize based on attribution - Shift budget to channels with the highest attributed value
Linear, time-decay, or position-based models - Choose the "right" attribution framework
First-party data solves everything - Build your own tracking to replace lost third-party cookies
This sounds logical, right? Map the journey, measure the impact, optimize accordingly. The promise is seductive: perfect visibility into what's driving growth.
Marketing platforms feed this illusion by offering increasingly sophisticated attribution reports. Google Analytics 4 promises "enhanced measurement." Facebook claims its "Conversions API" solves attribution gaps. HubSpot sells you on "full-funnel visibility."
The problem? This conventional wisdom ignores the fundamental reality of how people actually buy. Real customer journeys don't follow neat attribution models. They're messy, cross-device, cross-platform, and increasingly invisible to our tracking systems.
But the industry keeps selling the dream because admitting attribution is broken would mean admitting that most marketing analytics are built on sand.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came while working with that B2B SaaS client I mentioned. They had spent months implementing a sophisticated multi-touch attribution system using Google Analytics 4, Facebook's Conversions API, and HubSpot's attribution reporting.
The setup looked impressive: tracking pixels everywhere, UTM parameters on every link, cross-platform identity resolution, the works. Their attribution dashboard showed beautiful customer journey maps with weighted touchpoints and channel performance metrics.
But something felt wrong. The attribution data was telling one story, while the business reality was telling another.
Here's what the attribution model claimed:
LinkedIn ads: 8x ROAS (up from 2.5x the previous quarter)
SEO: declining attribution value
Direct traffic: minimal impact
Email: moderate attribution
But here's what was actually happening to the business:
Total qualified leads were flat despite "improved" LinkedIn performance
Organic search traffic was growing 40% month-over-month
Sales team reported most prospects mentioned finding them through Google
Customer interviews revealed complex, multi-month research processes
The disconnect was massive. I started digging deeper and discovered what I now call "attribution theater" - the performance of measurement without actual insight.
The client had fallen into the classic trap: optimizing for attribution metrics instead of business outcomes. They were about to double down on LinkedIn ads based on fraudulent data while cutting investment in the SEO that was actually driving growth.
This project forced me to confront an uncomfortable truth: our attribution systems aren't just inaccurate - they're actively misleading us.
Here's my playbook
What I ended up doing and the results.
After seeing attribution fail repeatedly across client projects, I developed what I call the "Coverage vs Control" framework. Instead of trying to track the untrackable, I focus on expanding visibility across all possible touchpoints while accepting that attribution will always be imperfect.
Here's exactly how I rebuilt their measurement approach:
Step 1: Admitted Attribution Defeat
First, I had to get the client comfortable with uncertainty. We stopped pretending we could track every conversion back to its "true" source. Instead, we acknowledged that most B2B buying happens in what I call the "dark funnel" - invisible to our tracking systems.
We documented every place attribution typically breaks:
Cross-device browsing (mobile research, desktop conversion)
Team buying decisions (multiple people, shared links)
Long sales cycles (months between touchpoints)
Privacy tools blocking tracking
Direct navigation after brand searches
Step 2: Built Business-Outcome Tracking
Instead of optimizing for last-click attribution, we focused on leading indicators that actually predicted revenue:
Total qualified leads by source (using UTM data where available)
Brand search volume trends
Organic content engagement metrics
Email list growth and engagement
Sales team feedback on lead sources
Step 3: Embraced Distribution Coverage
Rather than trying to control every interaction, we focused on being discoverable everywhere prospects might look:
Comprehensive SEO strategy for problem-aware searches
LinkedIn thought leadership content
Email nurture sequences
Retargeting for engagement, not immediate conversion
Customer success stories across multiple formats
Step 4: Implemented Reality-Based Reporting
We created a new dashboard that showed:
Directional trends rather than precise attribution
Confidence intervals around all metrics
Business outcomes alongside channel metrics
Qualitative insights from sales and customer success
The result? They stopped chasing attribution ghosts and started making decisions based on actual business impact.
Attribution Lies
Most platforms over-attribute their own contribution while under-reporting assists from other channels, creating false confidence in channel performance.
Dark Funnel Reality
B2B buyers research across multiple devices and platforms in ways that are invisible to tracking, making perfect attribution mathematically impossible.
Coverage Strategy
Focus on expanding touchpoint coverage rather than measurement precision - be discoverable everywhere prospects might look for solutions.
Business Metrics
Track leading indicators that predict revenue growth rather than optimizing for attribution metrics that may not reflect reality.
The results spoke for themselves. Within three months of implementing this reality-based approach:
The client stopped wasting budget on "high-attribution" channels that weren't actually driving business growth. Instead of doubling down on LinkedIn ads based on fraudulent 8x ROAS data, they redirected investment toward the SEO strategy that was genuinely moving the needle.
More importantly, they gained clarity about what was actually working. The sales team reported higher quality leads, and customer interviews revealed that prospects were finding them through multiple touchpoints before converting - exactly what our coverage strategy predicted.
The biggest win wasn't in the metrics - it was in the decision-making. Marketing discussions shifted from "Which channel deserves credit?" to "How do we expand our coverage across the buyer journey?" This mindset change led to more coherent campaigns and better cross-channel coordination.
The most surprising outcome? By accepting attribution uncertainty, they actually became more confident in their marketing investments. They stopped second-guessing campaigns based on attribution noise and started measuring success through business fundamentals.
This approach has now been validated across multiple client projects. Every time I see a company obsessing over attribution precision, I know they're optimizing for the wrong thing.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from multiple attribution reality checks:
Attribution is correlation, not causation - Just because a channel gets attribution credit doesn't mean it's driving incremental growth
The dark funnel is bigger than the visible funnel - Most B2B research happens outside your tracking systems
Platforms lie to protect their business model - Facebook, Google, and LinkedIn all over-attribute their contribution
Perfect measurement kills good marketing - Obsessing over attribution precision leads to channel tunnel vision
Coverage beats precision - Being discoverable everywhere is more valuable than tracking everything perfectly
Business outcomes trump attribution metrics - Revenue growth matters more than click attribution
Qualitative insights often beat quantitative data - Sales team feedback frequently reveals what attribution models miss
The hardest lesson? Admitting that marketing measurement is fundamentally limited doesn't make you less sophisticated - it makes you more realistic. The companies that win are those that accept attribution uncertainty and focus on business fundamentals instead of measurement theater.
If I had to start over, I'd spend less time building perfect attribution systems and more time ensuring comprehensive market coverage. The goal isn't to track every interaction - it's to be present at every stage of the buyer journey.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups dealing with attribution confusion:
Track qualified leads and pipeline metrics, not last-click attribution
Focus on building brand awareness measurable through search volume
Use customer interviews to understand actual discovery paths
Measure content engagement alongside conversion metrics
For your Ecommerce store
For ecommerce stores struggling with attribution accuracy:
Combine first-party data with platform attribution for directional insights
Track incrementality through hold-out tests rather than attribution models
Focus on customer lifetime value across all channels
Use post-purchase surveys to understand discovery journeys