Growth & Strategy
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
Last year, I watched an e-commerce client celebrate their "improved Facebook ad performance" while I knew the real story. Their reported ROAS had jumped from 2.5 to 8-9 overnight. The marketing team was ready to double down on Facebook spend.
But here's what Facebook's attribution model wasn't telling them: we'd just launched a comprehensive SEO strategy that was driving significant organic traffic and conversions. Facebook was claiming credit for wins that belonged to our content efforts.
This is the dark reality of attribution tracking in 2025. Your dashboards are lying to you. Your "winning" channels might be stealing credit from your real growth drivers. And the most dangerous part? You're making budget decisions based on this fiction.
After working with dozens of e-commerce stores and SaaS companies, I've seen this attribution problem destroy marketing strategies and waste hundreds of thousands in ad spend. The solution isn't better tracking—it's understanding why attribution fundamentally breaks down in today's complex customer journey.
Here's what you'll learn from my experience fixing attribution disasters:
Why multi-touch attribution models create more confusion than clarity
The hidden attribution tax that's inflating your paid channel costs
How to embrace the "dark funnel" instead of fighting it
A framework for making budget decisions when attribution fails
The one metric that actually predicts sustainable growth
Attribution Reality
What every marketer thinks they know about tracking
Walk into any marketing team meeting, and you'll hear the same conversation. "Our Facebook ROAS is 4.2, but Google Ads is only hitting 2.8. We need to shift budget." Everyone nods. The data seems clear. The decision feels obvious.
This is what the industry has taught us to believe: that attribution models can accurately assign credit to marketing touchpoints. That last-click, first-click, or multi-touch attribution will reveal the "truth" about channel performance. That with enough data and sophisticated modeling, we can finally answer the age-old question: "Which half of my advertising is wasted?"
The conventional wisdom follows a predictable pattern:
Install comprehensive tracking: Facebook Pixel, Google Analytics, UTM parameters on everything, conversion tracking across all channels
Choose an attribution model: Last-click for simplicity, first-click for brand awareness, or multi-touch for "sophisticated" analysis
Build attribution dashboards: Visualize the customer journey, map every touchpoint, create clean attribution reports
Optimize based on data: Increase spend on "winning" channels, decrease or eliminate "losing" ones
Scale what works: Double down on channels with the highest attributed ROAS or lowest attributed CAC
This approach exists because it promises control in an increasingly complex marketing landscape. When customers interact with 6-8 touchpoints before converting, marketers desperately want to understand which touchpoints "matter." Attribution models offer the illusion that we can quantify influence and make data-driven decisions.
The problem? This conventional wisdom fundamentally misunderstands how customers actually behave in 2025. It assumes linear, trackable journeys in a world where most interactions happen in the "dark funnel"—untrackable spaces where customers research, discuss, and decide.
More dangerously, it creates a feedback loop where marketers optimize for attribution models rather than actual business results. They chase metrics that feel scientific but often lead to worse outcomes: over-investment in last-click channels, under-investment in brand and awareness efforts, and a constant struggle to "prove" the value of channels that don't fit neat attribution boxes.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
I learned this lesson the hard way while working with an e-commerce client who had built their entire growth strategy around Facebook Ads. They were generating consistent revenue with a ROAS of 2.5, but their small margins meant they needed to find additional growth levers without just increasing ad spend.
The client came to me frustrated. "Facebook is our only profitable channel," they said. "We've tried Google Ads, influencer partnerships, email marketing—nothing else works. The attribution data is clear." Their dashboard showed Facebook driving 80% of attributed revenue, with every other channel showing negative or break-even ROAS.
But I noticed something interesting in their Google Analytics. Direct traffic was unusually high for a small e-commerce brand. Their organic search traffic was minimal—typical for a business that had focused entirely on paid social. Yet somehow, a significant portion of their revenue was coming through "direct" visits that couldn't be attributed to any marketing channel.
This is where most marketers make their first mistake: they trust their attribution data without questioning the story it tells. If Facebook is showing high ROAS and direct traffic can't be attributed, the obvious conclusion is that Facebook is working and everything else isn't. Case closed.
Except that's not how customers actually behave. I started digging deeper into their customer journey data. What I found challenged everything their attribution models were telling them:
Customers were seeing Facebook ads, but not clicking immediately
They were googling the brand name days or weeks later
They were visiting the website directly, browsing, leaving, then returning multiple times
Final purchases often happened through direct visits or organic search
Facebook was getting credit for conversions that happened through this complex, multi-session journey. But here's the crucial insight: without that initial Facebook touchpoint, many of these customers would never have discovered the brand. The attribution model was both right and wrong—Facebook was driving conversions, but not in the way the data suggested.
This revelation led to my biggest experiment yet: what would happen if we built a comprehensive distribution strategy while keeping Facebook spend constant? Could we increase overall revenue without increasing the cost of our "winning" channel?
Here's my playbook
What I ended up doing and the results.
Instead of trying to fix our attribution tracking, I decided to embrace what I call "coverage strategy"—expanding visibility across all possible touchpoints without obsessing over which one gets credit. This approach acknowledges that modern customer journeys are inherently messy and focuses on total business impact rather than channel-specific attribution.
Here's exactly what we implemented over three months:
Step 1: SEO Foundation Build
While keeping Facebook Ads running at the same spend level, we launched a comprehensive SEO strategy. This included complete website restructuring for search optimization, development of topic clusters around our core products, and creation of search-intent focused content rather than just brand messaging.
The key insight: we weren't trying to replace Facebook—we were trying to capture customers at different stages of their journey. Facebook was great for initial discovery, but we needed to be present when customers were actively researching solutions.
Step 2: Email List Growth Through Multiple Channels
We implemented lead magnets across every traffic source: downloadable guides from organic search, exclusive offers from social media, and retargeting campaigns for website visitors. Instead of focusing on which channel generated each email signup, we measured total list growth and engagement rates.
Step 3: Content Distribution Across Platforms
We repurposed our best content across multiple channels: blog posts became email newsletters, product demonstrations became YouTube videos, customer success stories became LinkedIn posts. Each piece of content served multiple distribution points without requiring separate attribution tracking.
Step 4: Measuring Holistic Business Impact
Instead of trying to track attribution across all these touchpoints, we measured what actually mattered: total revenue growth, customer lifetime value, and overall brand awareness. We used branded search volume as a proxy for brand lift and customer surveys to understand how people discovered us.
The results were immediate and eye-opening. Within the first month, our Facebook attribution models went completely haywire. Reported ROAS jumped from 2.5 to 8-9, which should have been impossible without changing our Facebook strategy. This confirmed what I suspected: Facebook was claiming credit for conversions that were actually driven by our expanded presence across multiple channels.
But here's what actually mattered: total business revenue increased by 40% while our total marketing spend increased by only 15%. Customer acquisition cost decreased when measured across all channels combined. Most importantly, the business became much more resilient—no longer dependent on a single platform's algorithm and pricing changes.
The lesson learned: attribution models don't just fail to capture reality—they actively distort it. By expanding our distribution footprint and measuring business impact rather than channel performance, we achieved growth that no amount of attribution optimization could deliver.
Attribution Lies
Attribution models don't capture how customers actually buy in 2025. They create a false sense of precision that leads to poor budget allocation decisions.
Dark Funnel
Most customer research happens in untrackable spaces. Instead of fighting this reality, successful businesses embrace it by focusing on total reach.
Coverage Strategy
Expand visibility across all touchpoints without obsessing over attribution. Measure total business impact, not individual channel performance.
Real Metrics
Focus on branded search volume, total revenue growth, and customer lifetime value rather than platform-reported attribution metrics.
The results from this omnichannel approach completely contradicted our attribution data, which proved my point about the fundamental flaws in tracking models.
Immediate Attribution Chaos (Month 1):
Facebook's reported ROAS jumped from 2.5 to 8-9 without any changes to the Facebook strategy. Google Analytics showed a 300% increase in "direct" traffic. Email attribution dropped to nearly zero despite email driving obvious conversions. The attribution models were completely broken.
Actual Business Results (Months 1-3):
Total revenue increased 40% while marketing spend increased only 15%. Customer acquisition cost decreased from $67 to $45 when measured across all channels. Branded search volume increased 180%, indicating real brand awareness lift. Customer lifetime value improved by 23% as we attracted higher-quality customers through multiple touchpoints.
Long-term Impact (6+ Months):
The business became platform-independent. When Facebook CPMs increased during Q4, total revenue remained stable. Organic search began driving 35% of new customer acquisitions. Email became a reliable retention channel with 28% of repeat purchases attributed to email touchpoints.
Most importantly, we stopped making budget decisions based on attribution reports and started making them based on business fundamentals: total growth, customer feedback, and market opportunities.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this strategy across multiple clients, here are the critical lessons that challenge everything marketers think they know about attribution:
Attribution models create false confidence: The more sophisticated your tracking, the more likely you are to make poor decisions. Simple business metrics often tell a clearer story than complex attribution dashboards.
Last-click attribution rewards bottom-funnel tactics: Channels that capture demand (like branded search) get over-credited, while channels that create demand (like content and brand campaigns) get under-credited.
Platform reporting is inherently biased: Facebook, Google, and other platforms have financial incentives to over-report their impact. Their attribution windows and models are designed to maximize their apparent value.
Customer journeys are getting longer and more complex: The average B2B buyer interacts with 11 pieces of content before making a decision. Attribution models can't handle this complexity without making arbitrary assumptions.
Dark funnel interactions are the majority: Word-of-mouth, private messages, screenshot sharing, and offline conversations drive most buying decisions but can't be tracked by any attribution model.
Coverage beats attribution: Being present across multiple touchpoints drives better results than optimizing individual channel attribution. Focus on expanding reach, not measuring it perfectly.
Business metrics matter more than channel metrics: Total revenue, customer lifetime value, and brand awareness are more reliable guides than ROAS, CPA, or attribution-based performance metrics.
The biggest learning: stop trying to solve attribution and start focusing on distribution. The businesses that win in 2025 are those that show up everywhere their customers are, not those that can perfectly track where credit belongs.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies dealing with attribution challenges:
Track trial-to-paid conversion rates across all sources, not just last-click attribution
Measure branded search volume as a proxy for demand generation effectiveness
Focus on expanding content distribution rather than optimizing individual channel tracking
For your Ecommerce store
For e-commerce stores struggling with attribution accuracy:
Implement post-purchase surveys to understand actual customer journey patterns
Use incrementality testing instead of attribution models for budget allocation
Prioritize customer lifetime value over first-purchase attribution metrics