Sales & Conversion
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
Last month I had a client celebrating their "amazing" Facebook ads performance. ROAS jumped from 2.5 to 8-9 overnight. Their first instinct? Increase the Facebook ad budget immediately.
But here's the thing - I knew better. While Facebook was claiming credit for this sudden surge, the reality was that our newly implemented SEO strategy was driving significant organic traffic and conversions. Facebook's attribution model was just taking credit for organic wins.
This experience perfectly illustrates the biggest lie in ecommerce: that your shopping ads pixel tracking tells you the truth about what's actually driving sales. Most businesses are making budget decisions based on attribution data that's fundamentally broken.
After working with dozens of ecommerce clients and testing multiple attribution approaches, I've learned that the traditional "last-click" and "Facebook claims credit" models are not just inaccurate - they're actively harmful to your business decisions.
In this playbook, you'll discover:
Why your current pixel tracking is probably lying to you about your best performing channels
The hidden customer journey that makes attribution nearly impossible
My framework for making budget decisions when tracking data conflicts with reality
How to embrace the "dark funnel" and focus on what actually matters
The metrics that actually predict sustainable growth vs. vanity attribution numbers
This isn't about implementing better tracking - it's about fundamentally changing how you think about customer acquisition and budget allocation in an era where product-channel fit matters more than perfect attribution.
Industry Reality
What every ecommerce owner believes about tracking
Walk into any ecommerce conference and you'll hear the same tracking gospel repeated everywhere. The industry has convinced itself that perfect attribution is not just possible, but essential for growth.
The Standard Attribution Playbook includes:
Multi-touch attribution models - Track every touchpoint in the customer journey
Pixel perfect tracking - Facebook Pixel, Google Analytics, and UTM parameters on everything
ROAS optimization - Make budget decisions based on reported return on ad spend
Channel isolation - Test one channel at a time to get "clean" data
First-party data collection - Build comprehensive customer profiles for better targeting
This approach exists because it gives the illusion of control. Agencies love it because they can show clean reports with clear ROI. Tool vendors love it because it justifies expensive attribution software. Marketers love it because it makes them feel scientific and data-driven.
The problem is that this entire framework is built on a fundamental misunderstanding of how customers actually behave in 2025. With iOS 14.5 privacy changes, third-party cookie deprecation, and increasingly sophisticated ad blockers, the data these models depend on is incomplete at best.
But the bigger issue isn't technical - it's philosophical. The assumption that customer journeys are linear and trackable completely ignores the messy reality of how people actually discover and buy products. Yet most ecommerce teams continue optimizing based on this broken foundation, wondering why their "winning" channels suddenly stop working.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came during a three-month project with an ecommerce client who was completely dependent on Facebook Ads. They had what looked like a solid business - 2.5 ROAS with a €50 average order value. Their entire marketing strategy was built around Facebook's attribution data.
But I could see the underlying problem: they had over 1,000 SKUs, and their strength was variety, not viral products. Facebook Ads thrives on 1-3 flagship products that can get quick decisions. Their customers needed time to browse, compare, and discover the right product - the opposite of Facebook's quick-decision environment.
Instead of continuing to force this square peg into a round hole, I convinced them to let me run a parallel SEO strategy. Within the first month, something interesting happened - Facebook's reported ROAS jumped to 8-9, while we hadn't changed anything about the ads themselves.
Here's what was actually happening: SEO was driving significant organic traffic and conversions, but Facebook's attribution model was claiming credit for customers who had been touched by a retargeting ad at some point in their journey. The "dark funnel" was in full effect - customers were going through multiple touchpoints that no tracking system could properly attribute.
A typical customer journey looked like this: Google search → blog post discovery → email signup → Facebook retargeting ad exposure → direct visit to complete purchase. Facebook claimed credit because of the retargeting touchpoint, even though the real driver was the SEO content and email nurturing.
This experience taught me that attribution lies, but distribution doesn't. The channels that were actually working weren't the ones getting credit in our tracking systems.
Here's my playbook
What I ended up doing and the results.
After this revelation, I developed a completely different approach to shopping ads tracking and budget allocation. Instead of trying to perfect attribution, I built a framework that acknowledges its limitations and focuses on what actually matters.
Step 1: Embrace Attribution Uncertainty
I stopped trying to get "clean" attribution data and instead started tracking directional indicators. Rather than obsessing over which specific touchpoint deserved credit, I focused on understanding the broader patterns of customer acquisition.
For this client, I implemented what I call "coverage tracking" - monitoring how many potential touchpoints we had across different channels rather than trying to trace individual customer paths. The goal was expanding visibility across all possible discovery points.
Step 2: Focus on Channel Physics
Each marketing channel has its own "physics" - rules about how it naturally works. Facebook Ads demands instant decisions. SEO rewards patient discovery. LinkedIn favors B2B thought leadership. Instead of fighting these natural tendencies, I aligned our strategy with them.
For the 1,000+ SKU catalog, this meant accepting that Facebook Ads would never be the primary growth driver. Instead, I repositioned it as a supporting channel for retargeting and brand awareness, while investing heavily in SEO for product discovery.
Step 3: Build Distribution Coverage
Rather than optimizing individual channels for attribution, I focused on building comprehensive coverage across all relevant touchpoints. The goal was ensuring that no matter how a customer preferred to discover products, we had a presence there.
This included:
SEO-optimized product and category pages for organic discovery
Email sequences for nurturing and education
Facebook retargeting for staying top-of-mind
Google Shopping for high-intent searches
Content marketing for building trust and expertise
Step 4: Track Leading Indicators
Instead of relying on lagging attribution data, I identified leading indicators that predicted sustainable growth:
Organic traffic growth patterns
Email list growth and engagement rates
Direct traffic increases (indicating brand strength)
Customer lifetime value trends
Repeat purchase rates by acquisition channel
Step 5: Budget Based on Channel Strengths
Rather than allocating budget based on attributed ROAS, I allocated based on each channel's natural strengths and the business's specific needs. For this client, that meant shifting from 80% Facebook Ads to a more balanced approach with heavier investment in SEO and content.
Channel Physics
Each marketing channel has natural rules - work with them not against them
Attribution Lies
Your tracking data is incomplete at best and misleading at worst
Coverage Strategy
Focus on touchpoint visibility rather than perfect attribution tracking
Leading Indicators
Track metrics that predict growth rather than claim credit for it
The results spoke for themselves, though not in the way traditional attribution would have shown. Over three months, we achieved a 10x increase in organic traffic while maintaining the Facebook Ads presence as a supporting channel.
Most importantly, customer lifetime value increased significantly because customers acquired through SEO showed higher engagement and repeat purchase rates. The attribution data never would have revealed this insight because it focuses on first-purchase attribution rather than long-term value.
The real victory was philosophical: the client stopped obsessing over which channel "deserved credit" and started focusing on building a sustainable acquisition system. Their business became more resilient because it wasn't dependent on any single tracking system or attribution model.
Traffic quality improved across all channels because we were attracting customers at the right stage of their journey rather than trying to force quick decisions. The customer experience became more natural and less pushy.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience fundamentally changed how I approach ecommerce tracking and attribution. Here are the key lessons that now guide every project:
1. Attribution is useful for optimization, terrible for budget allocation - Use tracking data to improve individual channel performance, but don't base strategic decisions on which channel "gets credit."
2. Channel-market fit matters more than attribution accuracy - A channel that works naturally with your customer's buying process will outperform one that fights against it, regardless of what the tracking data says.
3. Customer lifetime value reveals true channel quality - The channel that drives the highest immediate attribution might deliver lower-quality customers. Track repeat purchase rates and long-term value by acquisition source.
4. Embrace the dark funnel instead of fighting it - Modern customer journeys are inherently complex and multi-touch. Build systems that acknowledge this reality rather than trying to force linear attribution.
5. Focus on distribution coverage, not attribution perfection - Ensure you have presence across all relevant touchpoints rather than optimizing individual channel attribution.
6. Leading indicators predict growth better than attribution data - Organic traffic growth, email engagement, and direct traffic increases are more reliable predictors of sustainable growth than reported ROAS.
7. Budget allocation should follow channel physics, not attribution claims - Allocate resources based on how each channel naturally works, not which one claims credit in your tracking system.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies dealing with longer sales cycles:
Focus on tracking demo requests and trial quality rather than immediate attribution
Monitor content engagement patterns to understand buyer journey progression
Track customer lifetime value by acquisition channel over 12+ month periods
For your Ecommerce store
For ecommerce stores with complex product catalogs:
Prioritize SEO and content for product discovery over paid acquisition
Use paid ads primarily for retargeting and brand awareness rather than cold acquisition
Track repeat purchase rates and customer lifetime value by channel