Sales & Conversion
Personas
Ecommerce
Time to ROI
Short-term (< 3 months)
Here's something that'll make you question everything: I had a client spending €2,000 monthly on Google Shopping ads. Google Ads dashboard showed a decent 2.5 ROAS. Everyone was happy.
Until I started digging deeper.
Turns out, their "amazing" Google Shopping performance was claiming credit for organic sales, direct traffic, and even email campaign conversions. The attribution was so broken that we were celebrating false wins while missing real optimization opportunities.
Most businesses make the same mistake - they trust platform attribution without understanding what's actually happening behind the scenes. Google wants to show you great numbers to keep you spending. Your analytics tell a different story. And the customer journey? It's messier than you think.
After fixing the tracking setup for this client and several others, I discovered that Google Shopping conversion tracking isn't just about installing a pixel and hoping for the best. It's about building a system that captures the real customer journey.
Here's what you'll learn:
Why platform attribution lies to you (and how to catch it)
The 3-layer tracking system I use to see real Shopping performance
How to set up first-party data collection that Google can't manipulate
My attribution model that reveals which Shopping campaigns actually drive revenue
The metrics that matter more than Google's inflated ROAS numbers
No more attribution theater. Let's track what actually matters.
Reality Check
Why Google's numbers don't tell the whole story
Walk into any ecommerce marketing meeting and you'll hear the same conversation: "Google Shopping is performing great - look at this 4x ROAS!" The dashboard looks impressive. The numbers are green. Everyone's patting themselves on the back.
The industry standard approach to Google Shopping tracking follows this playbook:
Install Google Analytics and Google Ads tracking - Set up conversion tracking through the Google ecosystem
Trust last-click attribution - Give full credit to the final touchpoint before purchase
Optimize based on platform metrics - Use Google's reported ROAS to make budget decisions
Focus on impression share and click-through rates - Chase vanity metrics that look good in reports
Set up basic enhanced ecommerce - Track product views and purchases through GA4
This conventional wisdom exists because it's simple. Google makes it easy to believe their attribution model. The setup is straightforward. The reports look professional. And honestly, most people don't know there's a better way.
But here's where this approach breaks down: Google Shopping rarely works in isolation. A customer might see your Shopping ad, research your brand, check reviews, visit your site directly, maybe even abandon their cart and come back through email before finally purchasing. Google's attribution model gives Shopping 100% credit for that sale, even though it was just one touchpoint in a longer journey.
The bigger problem? You're optimizing campaigns based on inflated performance data. You're scaling the wrong products, targeting the wrong audiences, and missing opportunities to improve the actual customer experience. It's like navigating with a broken compass - you might be moving fast, but you're probably heading in the wrong direction.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
I learned this lesson the hard way with a Shopify client selling over 1,000 products across multiple categories. They came to me frustrated because their "successful" Google Shopping campaigns weren't translating to actual business growth.
The numbers told a confusing story. Google Ads reported strong performance - 3.2 ROAS on Shopping campaigns. But when I looked at their actual revenue growth and profit margins, something wasn't adding up. They were spending more on ads but seeing flat revenue. Classic sign of attribution issues.
My first move was to audit their entire tracking setup. What I found was a mess of overlapping pixels, double-counting conversions, and zero first-party data collection. Google was claiming credit for sales that clearly came from other sources.
The breaking point came when I noticed their highest "performing" Shopping campaigns were for products with the lowest actual profit margins. Google's algorithm was optimizing for easy conversions on discounted items while ignoring their premium products that drove real business value.
This client's situation was perfect for testing a different approach because they had enough volume to see patterns quickly, multiple traffic sources to compare attribution across, and a willingness to prioritize accuracy over impressive-looking dashboards.
The traditional setup was failing them because they needed to understand the real impact of Shopping ads on their business, not just Google's version of success. They needed to see which products actually drove profit, which audiences were worth the higher click costs, and how Shopping campaigns influenced their overall customer acquisition strategy.
Most importantly, they needed a tracking system that couldn't be manipulated by platform algorithms trying to justify ad spend.
Here's my playbook
What I ended up doing and the results.
Instead of trusting Google's attribution black box, I built a three-layer tracking system that reveals what actually happens in the customer journey.
Layer 1: Platform-Agnostic Revenue Tracking
First, I set up server-side conversion tracking through their Shopify store that captures every purchase regardless of attribution source. This creates a single source of truth that no advertising platform can manipulate. I used UTM parameters and first-party cookies to track the real customer journey across sessions.
The key insight here: instead of letting Google define what counts as a Shopping conversion, we define it ourselves based on actual business metrics.
Layer 2: Multi-Touch Attribution Modeling
Next, I implemented a custom attribution model that weights touchpoints based on their actual influence on purchasing decisions. Shopping ads get credit for discovery and initial interest, but not full credit for conversions that happen days later through direct traffic.
I tracked customer behavior patterns and found that Shopping ads were most valuable for introducing new customers to specific products, while direct traffic and email typically drove the final conversion. This completely changed how we valued Shopping campaign performance.
Layer 3: Profit-Based Performance Metrics
Finally, I connected the tracking to their actual product costs and profit margins. Instead of optimizing for revenue ROAS, we optimized for profit ROAS. This revealed that many "high-performing" Shopping campaigns were actually losing money once you factored in product costs and lifetime value.
The implementation involved setting up custom events in their analytics, creating automated reports that combined Shopping data with profit margins, and building alerts for when attribution discrepancies exceeded certain thresholds.
The biggest change was moving from reactive optimization ("this campaign shows good ROAS") to predictive optimization ("this campaign drives profitable customers who make repeat purchases").
Within 6 weeks, we had completely restructured their Shopping campaigns based on real performance data instead of Google's version of success.
Server-Side Setup
Track conversions independent of platform attribution to see real performance data
Attribution Modeling
Weight touchpoints based on actual influence rather than last-click assumptions
Profit Integration
Connect Shopping performance to actual profit margins and customer lifetime value
Automated Monitoring
Set up alerts when attribution discrepancies signal tracking issues
The results were eye-opening and immediately actionable. Within 8 weeks of implementing the new tracking system, we discovered that Google's reported 3.2 ROAS was actually closer to 1.8 ROAS when measured against true incremental revenue.
More importantly, we identified that Shopping campaigns were driving 40% new customers but with a 30% lower lifetime value compared to organic and email acquisitions. This insight completely changed the budget allocation strategy.
The profit-based optimization revealed that premium product campaigns had been undervalued by Google's algorithm. When we shifted budget toward higher-margin items based on our real attribution data, overall profitability increased by 23% even with lower reported ROAS numbers.
Perhaps most valuable was discovering that Shopping ads had strong delayed conversion effects. Customers who clicked Shopping ads often purchased within 7-14 days through direct traffic or email campaigns. The original attribution model missed this entirely.
The automated monitoring system caught three major attribution discrepancies in the first month alone, including a week where Google was double-counting mobile and desktop conversions from the same customers.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The biggest lesson: platform attribution is designed to make platforms look good, not to help you make better business decisions. Google wants to show strong Shopping performance to justify your ad spend, even if it means taking credit for sales that would have happened anyway.
Second key insight: first-party data beats platform data every time. When you control the tracking, you control the truth. Server-side conversion tracking can't be blocked by privacy updates or manipulated by advertising algorithms.
Multi-touch attribution isn't just more accurate - it's more actionable. Understanding that Shopping drives discovery while email drives conversion lets you optimize each channel for what it actually does best.
Profit-based optimization changes everything. Revenue ROAS optimization leads to a race to the bottom on margins. Profit ROAS optimization builds sustainable growth.
The customer journey is longer and more complex than any single platform can measure. Shopping ads influence purchases that happen days or weeks later through completely different channels.
Automated monitoring is essential because attribution breaks constantly. Privacy updates, platform changes, and technical issues can corrupt your data without warning.
Finally: accurate tracking often reveals lower performance numbers initially, but it enables much better optimization over time. I'd rather have truthful data showing 2x ROAS than fake data showing 4x ROAS.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies using Google Shopping for software tools or services:
Connect Shopping tracking to trial signups and paid conversions, not just clicks
Track customer lifetime value from Shopping-acquired users vs other channels
Set up event tracking for key activation milestones beyond initial signup
For your Ecommerce store
For ecommerce stores implementing better Shopping conversion tracking:
Start with server-side tracking to create platform-independent conversion data
Implement profit-based optimization rather than revenue-only ROAS tracking
Use UTM parameters and first-party cookies to track true customer journeys
Set up automated alerts for attribution discrepancies and tracking issues