Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Three months into working with a B2C Shopify client, I was staring at their Google Analytics dashboard feeling completely defeated. The numbers made no sense.
Their Facebook ads showed a 2.5 ROAS, which seemed decent enough. But when I dug deeper into the cross-device conversion data, something felt off. Users were apparently bouncing between mobile and desktop like ping-pong balls, and the attribution was claiming credit for conversions that happened days after someone clicked an ad.
That's when I realized the uncomfortable truth: most cross-device tracking is sophisticated guesswork. And after working with multiple e-commerce clients and testing various tracking setups, I've learned that chasing perfect attribution is often more harmful than helpful.
Here's what you'll learn from my experience:
Why cross-device tracking data is fundamentally flawed (and always will be)
The attribution dark funnel that nobody talks about
A practical framework for measuring what actually matters
How to make decisions with incomplete data (like every successful business does)
The tracking setup that actually improved our client's ROI
If you're tired of attribution reports that look like science fiction, this playbook will help you focus on distribution strategies that actually drive growth.
Reality Check
What the tracking industry won't tell you
Walk into any marketing conference or open any growth blog, and you'll hear the same advice about cross-device tracking:
Implement Google Analytics Enhanced E-commerce - "It tracks everything!"
Set up Facebook Pixel with Conversions API - "Cover all your bases!"
Use UTM parameters religiously - "Know exactly where traffic comes from!"
Deploy cross-device identity graphs - "See the complete customer journey!"
Trust your attribution models - "Data doesn't lie!"
This advice exists because tracking tools need to justify their complexity and cost. Marketing agencies need to show detailed reports to prove their value. And honestly, we all want to believe we can measure everything perfectly.
The problem? Cross-device tracking is fundamentally broken. iOS 14.5+ killed cookie-based tracking. Chrome is phasing out third-party cookies. Privacy regulations like GDPR create tracking gaps. And even when tracking "works," it's making educated guesses about user behavior across devices.
But here's what the industry won't tell you: most successful businesses make decisions with incomplete data. They focus on directional trends, not precise attribution. They optimize for outcomes, not tracking perfection.
Yet we keep chasing the holy grail of perfect attribution, wasting time and money on tracking setups that deliver false precision instead of useful insights.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this particular Shopify client came to me, they were running Facebook ads with what looked like decent performance - 2.5 ROAS according to their dashboard. But something didn't add up when I looked at their overall revenue growth.
Their challenge was typical for e-commerce: customers would discover products on mobile (usually through social media), research on desktop, then purchase on whatever device was convenient. The attribution was a mess.
I started by implementing what every "expert" recommends. Enhanced E-commerce tracking, Facebook Conversions API, UTM parameters on everything, and even experimented with customer ID tracking for logged-in users.
Three months later, our tracking setup was more sophisticated than NASA's mission control, but the insights were garbage. Facebook was claiming credit for purchases that happened a week after someone clicked an ad. Google Analytics was showing different conversion numbers than Shopify's actual sales data. The cross-device reports looked impressive but told us nothing actionable.
The breaking point came when the client asked a simple question: "Which marketing channel should we invest more in?" I had dozens of attribution reports but couldn't give a confident answer.
That's when I realized we were solving the wrong problem. Instead of trying to track every touch point perfectly, I needed to focus on what actually moved the business forward. The revelation came from looking at their distribution strategy holistically rather than obsessing over attribution precision.
This shift in thinking completely changed how I approach tracking for all my clients. Sometimes the best measurement strategy is admitting what you can't measure.
Here's my playbook
What I ended up doing and the results.
Instead of chasing perfect cross-device tracking, I developed what I call the "Attribution Reality Framework." It acknowledges tracking limitations while still providing actionable insights.
Step 1: Embrace the Dark Funnel
Most customer journeys happen in the "dark funnel" - places your tracking can't see. Someone sees your Facebook ad, searches for your brand on Google three days later, visits your site, then asks their friend about it on WhatsApp before purchasing a week later. Your attribution will claim the Google search "caused" the conversion, completely missing the Facebook ad that started everything.
I stopped trying to eliminate the dark funnel and started planning for it. Instead of obsessing over last-click attribution, I focused on first-touch awareness and overall brand lift.
Step 2: Directional Tracking Instead of Precise Attribution
Rather than trusting specific ROAS numbers, I started looking at trends. If Facebook's reported ROAS went from 2.5 to 3.2 while keeping spend constant, that's probably a real improvement - even if the absolute numbers are wrong.
The key insight: relative changes matter more than absolute accuracy. If Channel A's performance improves 40% relative to Channel B, that's actionable even if both channels' exact attribution is questionable.
Step 3: Business-Level Metrics Over Platform Metrics
I implemented what I call "holdout testing" - turning off different channels for controlled periods and measuring the impact on overall business metrics like total revenue, new customer acquisition, and organic search volume.
For the Shopify client, I paused their Facebook ads for two weeks while keeping everything else constant. Revenue dropped 15%, and organic search for their brand terms decreased 20%. This told us more about Facebook's true impact than months of attribution reports.
Step 4: Channel-Specific Success Metrics
Instead of forcing every channel into a ROAS model, I defined success differently for each:
Facebook Ads: Brand awareness lift + new customer acquisition
Google Ads: Direct response + retargeting efficiency
SEO: Long-term organic growth + reduced paid dependency
Email: Customer lifetime value + repeat purchase rate
This approach aligned measurement with each channel's actual strength rather than forcing everything into the same attribution box.
Attribution Honesty
Be transparent about tracking limitations instead of pretending your data is perfectly accurate. Focus on trends and directional insights rather than precise numbers.
Dark Funnel Planning
Plan your marketing strategy assuming most customer touchpoints will be invisible to your tracking. Design campaigns for brand awareness, not just measurable conversions.
Holdout Testing
Regularly pause individual channels for 1-2 weeks to measure their true business impact. This reveals attribution that cross-device tracking misses completely.
Business Metrics Focus
Track overall business health (revenue growth, customer acquisition, brand search volume) alongside platform-specific metrics for a complete picture.
The results weren't immediately obvious in the tracking dashboards - and that was exactly the point. By focusing on business outcomes instead of attribution precision, we made better decisions.
Within six months, the client's overall revenue increased 35% while their marketing efficiency improved significantly. We reallocated budget from channels that looked good in attribution reports but didn't drive real business growth.
More importantly, we stopped wasting time debugging attribution discrepancies and started focusing on evaluating channels based on their actual business impact.
The holdout testing revealed that their Facebook ads were actually driving 40% more value than attribution suggested, while their Google display campaigns were essentially worthless despite showing positive ROAS in reports.
The client finally had confidence in their marketing decisions because they were based on real business outcomes rather than sophisticated guesswork disguised as precision.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here's what I learned from rebuilding attribution measurement from the ground up:
Perfect tracking is impossible and unnecessary - Focus on directional accuracy instead
Relative performance matters more than absolute numbers - Track trends, not precision
Business metrics beat platform metrics - Revenue growth trumps ROAS reports
Holdout testing reveals true impact - Turn things off to understand their value
Each channel needs different success metrics - Stop forcing everything into ROAS
The dark funnel is your friend - Plan for unmeasurable touchpoints
Attribution theater wastes resources - Spend time optimizing, not measuring
The biggest lesson? Stop trying to track everything perfectly and start making decisions with confidence based on the data you can trust. Most successful businesses operate with incomplete information - the key is knowing which incomplete information actually matters.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies, focus on:
Trial-to-paid conversion rates by channel
Customer lifetime value trends
Organic brand search growth
Feature adoption patterns by acquisition source
For your Ecommerce store
For ecommerce stores, prioritize:
New vs returning customer ratios
Average order value by channel
Brand search volume increases
Repeat purchase rates within 90 days