Sales & Conversion
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, I was reviewing Facebook Ads data for an e-commerce client when I noticed something strange. Facebook was claiming a 8-9 ROAS on campaigns that were clearly not performing that well. Meanwhile, Google Analytics was showing completely different attribution data for the same customers.
Sound familiar? If you've ever tried to figure out which marketing channel actually drives your revenue, you've probably fallen into the first touch vs last touch attribution rabbit hole. Everyone's obsessing over which model is "correct" - but here's the uncomfortable truth I learned after working with dozens of clients: the attribution model debate is missing the real problem.
After implementing SEO strategies that boosted organic traffic while watching Facebook take credit for those conversions, I realized we're asking the wrong questions. The issue isn't whether first touch or last touch is better - it's that customer journeys are messy, platforms lie, and most businesses are optimizing for metrics that don't reflect reality.
Here's what you'll learn from my experience with attribution across multiple client projects:
Why both first touch and last touch attribution models are fundamentally flawed
The "dark funnel" reality that makes traditional attribution impossible
How I shifted from tracking attribution to focusing on distribution coverage
A practical framework for making marketing decisions without perfect attribution data
When to trust (and when to ignore) platform-reported metrics
Let's dive into why the attribution wars are distracting us from what actually drives growth.
Attribution Reality
The marketing world's obsession with perfect tracking
Walk into any marketing conference or scroll through any growth-focused LinkedIn post, and you'll hear the same debates raging: "First touch attribution gives credit to awareness channels!" "Last touch attribution shows what actually converts!" "Multi-touch attribution is the holy grail!"
The conventional wisdom breaks down like this:
First Touch Attribution Advocates argue:
Credit should go to the channel that started the customer journey
Awareness and top-of-funnel efforts get proper recognition
Prevents over-investing in bottom-funnel tactics
Shows the true impact of brand building and content marketing
Last Touch Attribution Supporters counter:
The final touchpoint deserves credit for closing the deal
It's easier to optimize what directly drives conversions
Sales teams can focus on channels that actually close deals
Budget allocation becomes more straightforward
Both camps have built entire strategies around these models. Marketing teams restructure budgets, hire specialists, and make major platform decisions based on which attribution model their organization adopts.
The problem? This entire debate assumes we can actually track customer journeys accurately in 2025. We can't. iOS 14.5, cookie deprecation, privacy regulations, and cross-device behavior have made traditional attribution models about as reliable as weather forecasts for next month.
But here's what nobody wants to admit: even before privacy changes broke our tracking, these models were already fundamentally flawed. Real customer journeys don't fit into neat first-touch or last-touch buckets.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The reality of attribution problems hit me hard while working with an e-commerce client who was heavily dependent on Facebook Ads. They had built their entire growth strategy around Facebook's reported ROAS of 2.5, which seemed decent enough for their margins.
But I noticed something was missing in their distribution strategy - they had zero organic presence. Their beautiful products, solid brand, and great customer service were completely invisible unless someone clicked on a Facebook ad. This felt like a massive missed opportunity.
So we launched a comprehensive SEO overhaul. I restructured their website architecture, optimized their product pages, and created content targeting long-tail keywords their customers were actually searching for. Within a month, we started seeing significant organic traffic growth.
Here's where it gets interesting: as our SEO efforts drove more organic traffic and conversions, Facebook's reported ROAS jumped from 2.5 to 8-9. Wait, what?
I knew better than to celebrate Facebook's "improved performance." What was actually happening became clear when I dug into the customer journey data:
A potential customer would Google search for the specific product they needed
They'd land on our optimized product page through organic search
They'd browse around, maybe add items to cart, but not purchase immediately
Later, they'd see a Facebook retargeting ad and click through
They'd complete their purchase, and Facebook would claim 100% credit
This wasn't just happening occasionally - it was the dominant pattern. Facebook was claiming credit for conversions that SEO actually initiated. The customer had already decided to buy based on their organic search experience; Facebook just happened to be the last touchpoint.
Under last touch attribution, Facebook looked like a hero. Under first touch attribution, Google would get all the credit. But neither model captured the reality: both channels were essential parts of a complex customer journey.
This experience taught me that the attribution model debate misses the fundamental point: customer journeys are messy, multi-touch, and often impossible to track accurately.
Here's my playbook
What I ended up doing and the results.
After realizing that traditional attribution was fundamentally broken, I developed a different approach with my clients. Instead of trying to perfect our tracking, we focused on understanding and embracing what I call "distribution coverage" - ensuring we had touchpoints everywhere our customers might discover us.
Step 1: Accept the Dark Funnel Reality
First, I had to help my client understand that most of the customer journey happens in what's called the "dark funnel" - interactions we can't track. This includes:
Word-of-mouth recommendations
Social media browsing without clicking
Podcast listening during commutes
Competitor research and comparison shopping
Cross-device behavior (research on mobile, buy on desktop)
Instead of fighting this reality, we embraced it. We stopped trying to attribute every conversion to a specific touchpoint and started focusing on coverage.
Step 2: Map All Possible Touchpoints
Rather than arguing about first touch vs last touch, we mapped out every possible place our ideal customers might encounter the brand:
Search engines (both branded and non-branded queries)
Social media platforms (even if they don't click through)
Email marketing and newsletters
Retargeting campaigns
Content platforms and industry publications
Partner and affiliate networks
The goal wasn't to track which touchpoint "won" - it was to ensure we had quality presence across all relevant touchpoints.
Step 3: Focus on Quality Over Tracking
Instead of obsessing over attribution data, we focused on making each touchpoint as valuable as possible:
For SEO, this meant creating genuinely helpful content that solved customer problems, not just ranking for keywords. For paid ads, it meant creating engaging creative that built brand awareness even if people didn't click. For email, it meant providing real value in every message, not just pushing products.
Step 4: Use Leading Indicators Instead of Attribution
Rather than relying on platform attribution data, we tracked leading indicators of healthy distribution:
Branded search volume growth
Direct traffic increases
Email list growth and engagement rates
Social media mention and engagement trends
Customer survey responses about discovery methods
These metrics told a much more accurate story about what was actually driving growth than any attribution model.
Step 5: Budget Based on Incremental Testing
For budget allocation decisions, we relied on incremental testing rather than attribution data:
Pause channels temporarily to measure impact on overall revenue
Test increased spending on specific channels and track total business growth
Use geographic or audience holdout groups to measure channel effectiveness
Focus on overall business metrics rather than channel-specific ROAS
This approach gave us much more reliable data about what was actually moving the needle for the business.
Stop Tracking
Start covering all possible customer touchpoints
Platform Reality
Attribution models create false competition between complementary channels
Coverage Strategy
Focus on distribution breadth rather than attribution precision
Testing Framework
Use incremental tests and holdout groups for budget decisions
The results of shifting away from attribution obsession were immediate and dramatic. Within three months of implementing this distribution coverage approach:
Business metrics improved across the board: Overall revenue increased by 40% as we stopped cannibalizing our own channels based on flawed attribution data. Customer acquisition costs actually decreased because we weren't competing against ourselves on different platforms.
Channel performance became more stable: By focusing on quality touchpoints rather than "winning" attribution, each channel performed better. Our SEO traffic had higher intent, our paid ads built better brand awareness, and our email marketing saw improved engagement rates.
Decision-making became clearer: Without the confusion of conflicting attribution data, marketing decisions became more straightforward. We focused on overall business growth rather than optimizing for metrics that platforms wanted us to optimize for.
The client gained strategic clarity: Instead of constantly second-guessing which channels to invest in based on attribution reports, they could focus on creating value across all customer touchpoints. This led to better creative, more helpful content, and ultimately stronger customer relationships.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After working through attribution challenges with multiple clients, here are the key lessons that changed how I approach marketing measurement:
Attribution models are marketing fiction. Every platform has an incentive to overstate its impact. Facebook, Google, email platforms - they all want to claim maximum credit for conversions. Treating their attribution data as objective truth is like asking each player in a basketball game who scored the most points.
Customer journeys are much longer and messier than we assume. The average customer touches 5-7 different points before buying, often across multiple devices and time periods. Trying to assign credit to a single touchpoint ignores this reality.
Coverage beats optimization. It's better to have solid presence across multiple relevant channels than to perfect your attribution tracking. Customers find you through many different paths - the goal is to be discoverable wherever they're looking.
Leading indicators are more reliable than lagging indicators. Branded search growth, direct traffic increases, and customer survey data tell you more about marketing effectiveness than platform ROAS reports.
Privacy changes accelerated an inevitable shift. iOS 14.5 and cookie deprecation didn't break attribution - they just exposed how broken it already was. Businesses that adapted to "imperfect" measurement early had a huge advantage.
Incremental testing trumps attribution modeling. Pause and restart tests, geographic holdouts, and controlled experiments give you much more reliable data about what's actually driving growth.
Platform data should inform, not dictate strategy. Use attribution data as one input among many, but don't base entire strategies on it. The platform that reports the highest ROAS isn't necessarily your most valuable channel.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies, focus on multi-touch coverage throughout long sales cycles. Track leading indicators like trial-to-paid conversion rates, feature usage patterns, and customer success metrics rather than obsessing over which channel gets attribution credit for enterprise deals.
For your Ecommerce store
E-commerce stores should prioritize distribution breadth over attribution precision. Map customer discovery paths from awareness to purchase, ensure quality presence across all relevant touchpoints, and use incremental testing to guide budget allocation decisions.