Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
When I started working with a B2B SaaS client who was burning through their marketing budget, they had what looked like a sophisticated setup. Multiple attribution tools, detailed customer journey maps, and dashboards that would make any marketing manager proud.
The problem? They were still making terrible budget allocation decisions.
After three months of working together, we discovered that their attribution model was not just wrong—it was actively harmful to their growth. Facebook was claiming credit for organic conversions, their "data-driven" approach was missing 60% of their actual customer journey, and they were doubling down on channels that weren't actually working.
This experience taught me something uncomfortable: most attribution modeling tools create the illusion of clarity while obscuring the truth. The real customer journey is messier, more complex, and fundamentally untrackable in the way these tools promise.
Here's what you'll learn from my experiments with attribution across multiple client projects:
Why I stopped trusting single-touch attribution after it cost a client 40% of their marketing budget
The distribution strategy that works when attribution fails you
How to make smart budget decisions in the "dark funnel" era
The simple tracking method that outperformed expensive attribution software
When attribution tools actually help (and when they hurt)
Industry Reality
What every marketer believes about attribution
Walk into any marketing conference and you'll hear the same promises about attribution modeling. The story goes like this: install the right tools, track every touchpoint, and you'll finally understand your customer journey with scientific precision.
The industry has convinced us we need:
Multi-touch attribution models that assign credit across every interaction
Advanced analytics platforms that track users across devices and channels
Machine learning algorithms that optimize for the "true" conversion path
Real-time dashboards showing exactly which channels drive revenue
Data-driven budget allocation based on attribution insights
This conventional wisdom exists because it feels scientific. We're told that with enough data and the right tools, marketing becomes predictable. Attribution modeling promises to turn the messy art of customer acquisition into a clean science.
Marketing teams love this narrative because it gives them something concrete to show leadership. "Our attribution model shows Facebook drives 35% of revenue" sounds much more professional than "we think Facebook might be helping, but we're not sure how."
The software companies selling these tools have every incentive to perpetuate this myth. They've built billion-dollar businesses on the promise that perfect attribution is just one more integration away.
But here's where this falls apart in practice: the modern customer journey is fundamentally untrackable. Privacy regulations, cookie deprecation, cross-device behavior, and the "dark funnel" have made traditional attribution models not just incomplete—but misleading.
Most businesses are optimizing their marketing based on fiction disguised as data.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came during a project with an e-commerce client who was heavily reliant on Facebook Ads. They had a "sophisticated" attribution setup with multiple touchpoints tracked across their customer journey.
On paper, everything looked good. Facebook was reporting a solid 2.5 ROAS, and their attribution model was giving them confidence in their ad spend. But something felt off when I dug into their overall business metrics.
This client had over 1,000 SKUs in their catalog—a massive product range that required customers to browse, compare, and discover. Their strength was variety, not impulse purchases. Yet they were trying to force this into Facebook's quick-decision environment based on what their attribution tools were telling them.
The turning point came when I implemented a complete SEO overhaul alongside their existing Facebook campaigns. Within a month, something interesting happened: Facebook's reported ROAS jumped from 2.5 to 8-9.
Most marketers would have celebrated their "improved ad performance." But I knew better. The reality was that SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins.
We were witnessing the attribution lie in real-time. Customers were finding the business through search, researching products organically, but Facebook was taking credit because they'd seen a retargeting ad at some point in their journey.
This experience taught me that attribution doesn't just fail to capture the full picture—it actively distorts your understanding of what's working. The client was about to double down on Facebook spend based on false data, which would have been disastrous for their business model.
Here's my playbook
What I ended up doing and the results.
After this revelation, I completely changed how I approach attribution and channel optimization. Instead of chasing perfect attribution, I developed what I call the "coverage approach"—focusing on expanding touchpoints rather than tracking them.
Step 1: Embrace the Dark Funnel
I stopped trying to track every interaction and started designing for an unmeasurable customer journey. The typical path isn't linear: Google search → social media browsing → retargeting ad → email sequence → multiple touchpoints → conversion. Attribution tools want to assign percentages to each step, but that's impossible and unnecessary.
Instead, I focus on distribution coverage. If customers are going to interact with your brand across multiple channels anyway, your job is to be present and valuable at every possible touchpoint, not to measure which one "wins."
Step 2: Switch to Channel-Fit Analysis
Rather than asking "which channel drives the most conversions," I started asking "which channel fits this product and business model?" For my e-commerce client, Facebook Ads demanded instant decisions, but their 1,000+ SKU catalog required patient discovery. SEO rewarded this shopping behavior.
This led to a fundamental insight: you can't change the rules of a marketing channel, you can only control how your product plays within those rules.
Step 3: Implement Revenue-Based Tracking
I replaced complex attribution models with simple revenue correlation analysis. Instead of tracking individual customer journeys, I tracked channel investment against overall business metrics over longer time periods.
The method is surprisingly simple:
Track total monthly revenue alongside channel activity
Look for correlations over 3-6 month periods
Test channel pauses to measure true impact
Focus on incrementality rather than attribution
Step 4: Build for Omnichannel Presence
The goal shifted from optimizing individual channels to creating a comprehensive presence across all relevant touchpoints. This meant building SEO content, maintaining social presence, running targeted ads, and nurturing email relationships—not because I could measure each one's contribution, but because I knew customers needed multiple touchpoints to convert.
For B2B SaaS clients, this looked like combining founder-led content on LinkedIn (which I discovered was often the real growth driver, not paid ads) with educational content, product demos, and case studies across multiple channels.
The key insight: attribution lies, but distribution doesn't. More distribution channels mean more opportunities for customers to discover and trust your brand, regardless of which touchpoint gets the credit.
Channel-Fit Analysis
Instead of measuring attribution, analyze whether your product naturally fits each channel's behavior patterns and decision-making environment.
Revenue Correlation
Track total business metrics against channel activity over 3-6 month periods rather than individual customer journeys.
Dark Funnel Strategy
Design marketing for unmeasurable touchpoints by focusing on comprehensive presence rather than trackable conversions.
Incrementality Testing
Pause channels systematically to measure true impact rather than relying on attribution model credit assignment.
The results of this approach fundamentally changed how my clients allocated their marketing budgets and measured success.
For the e-commerce client, we shifted 60% of their budget from Facebook Ads to SEO and content creation. Within six months, their organic traffic increased by 400%, and more importantly, their actual profit margins improved significantly because organic traffic converted at higher values and didn't require ongoing ad spend.
The B2B SaaS client discovered that their founder's LinkedIn content was driving more qualified leads than their entire paid acquisition strategy. When we doubled down on personal branding and thought leadership, their cost per acquisition dropped by 70% while lead quality improved dramatically.
Most surprisingly, both clients reported feeling more confident about their marketing decisions despite having "less data." When you stop chasing false precision, you can focus on genuine business impact.
The revenue correlation method revealed patterns that attribution tools had completely missed. For example, SEO efforts showed up in revenue 3-4 months later, but attribution tools gave credit to whatever campaign was running at conversion time. This delayed correlation would have been invisible in traditional attribution reporting.
By the end of these experiments, both clients had achieved better ROI with simpler tracking methods than they ever had with sophisticated attribution models.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the most important lessons I learned from abandoning traditional attribution in favor of distribution-focused marketing:
Attribution tools create dangerous confidence - The precision they promise often leads to bad decisions based on incomplete data.
Product-channel fit matters more than attribution - Understanding how your product naturally aligns with channel behavior is more valuable than tracking every touchpoint.
The dark funnel is real - Most customer research and consideration happens in untrackable spaces, making attribution fundamentally limited.
Simple correlation beats complex attribution - Tracking revenue against channel investment over time reveals patterns that detailed attribution misses.
Incrementality testing works - Pausing channels reveals their true impact better than any attribution model.
Distribution coverage trumps optimization - Being present across multiple touchpoints matters more than perfectly optimizing individual channels.
Organic wins compound - Channels that don't require ongoing spend become more valuable over time, but attribution tools undervalue them.
What I'd do differently: I would have questioned attribution tools sooner and focused on business fundamentals from the start. The months spent optimizing based on false attribution data were essentially wasted.
This approach works best for businesses with longer sales cycles, complex product catalogs, or B2B models where relationship-building matters. It's less effective for simple, impulse-purchase products where single-touch attribution might actually be accurate.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups, focus on:
Track trial-to-paid conversion by channel monthly, not individual attribution
Test founder-led content before scaling paid acquisition
Measure time-to-value rather than first-touch attribution
Build educational content that works across multiple discovery channels
For your Ecommerce store
For e-commerce stores, prioritize:
Product-channel fit analysis over attribution modeling
SEO investment for complex catalogs that require browse behavior
Revenue correlation tracking over customer journey mapping
Organic channel development that compounds over time