Sales & Conversion
Personas
Ecommerce
Time to ROI
Short-term (< 3 months)
Last month, I had a client celebrating their "best month ever" because Google Ads was reporting a 8.9 ROAS. Champagne was flowing, budgets were being increased, and everyone was patting themselves on the back.
Then I looked at their actual Shopify revenue for the same period.
The numbers didn't match. Not even close. Google Ads claimed they generated €50K in revenue, but Shopify showed only €28K from that traffic source. That's when I realized we had a classic case of attribution lies that most ecommerce stores never catch.
Here's the uncomfortable truth: your Google Ads dashboard is probably lying to you about conversions. Not intentionally, but the default tracking setup gives Google credit for sales it didn't actually drive. I've seen this pattern across dozens of Shopify stores - and once you understand what's really happening, you can fix it.
In this playbook, you'll learn:
Why Google Ads attribution is fundamentally broken for most Shopify stores
The three tracking gaps that inflate your ROAS by 40-70%
My exact setup for accurate conversion tracking that reveals true performance
How to audit your current tracking in 15 minutes
When to trust Google's numbers (and when to ignore them completely)
If you're making budget decisions based on Google Ads reports, you need to read this. The real conversion optimization starts with knowing which conversions actually happened.
Industry Reality
What every ecommerce manager believes about Google Ads tracking
Walk into any ecommerce company and ask about their Google Ads performance, and you'll hear the same story: "We're getting a 4.2 ROAS" or "Google Ads is our best performing channel." The entire industry has built a house of cards on Google's attribution model, and nobody questions it.
Here's what everyone "knows" about Google Ads tracking:
Google Ads tracks everything automatically - Just add the conversion pixel and you're done
Last-click attribution is accurate enough - The last ad clicked gets the credit
View-through conversions matter - People who saw your ad but didn't click still convert
ROAS is the ultimate metric - If Google says 5x ROAS, that's pure profit
Cross-device tracking works perfectly - Google connects mobile ad clicks to desktop purchases
This conventional wisdom exists because Google has made tracking "easy." Install one pixel, check a few boxes, and boom - you've got "complete" conversion tracking. Marketing agencies love this because it makes their campaigns look incredibly successful.
The problem is that easy rarely equals accurate. Google's attribution model is designed to make Google Ads look as good as possible, not to give you the truth about your business. They're not lying, but they're definitely not telling the whole story.
Most ecommerce teams never dig deeper because the numbers look good. Why question a 6x ROAS when that's exactly what you want to see? But when you're scaling ad spend based on inflated numbers, you're setting yourself up for a brutal wake-up call.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client was a mid-sized Shopify store selling outdoor gear. They'd been running Google Ads for eight months and were convinced it was their golden goose. Their dashboard showed consistent 7-9x ROAS, month after month. They wanted to triple their ad spend.
I was brought in to optimize their campaigns, but something felt off. The numbers were almost too good to be true. So I did what most people don't do - I cross-referenced their Google Ads conversion data with their actual Shopify analytics.
Here's what I found:
Google Ads claimed 847 conversions generating €73,400 in revenue over the past 60 days. But when I filtered Shopify's analytics to show only traffic from Google Ads (using UTM parameters), the real numbers were 623 conversions and €51,200 in revenue.
That's a €22,200 difference. Google was taking credit for 36% more revenue than it actually drove.
The client's first reaction was denial. "But Google says..." Then confusion. "How is this possible?" Then panic. "What does this mean for our budgets?"
I spent the next week digging into their tracking setup. What I discovered was a perfect storm of attribution issues that plague most Shopify stores:
View-through attribution was enabled - Google counted purchases from people who saw an ad but didn't click
Cross-network attribution was inflating numbers - YouTube and Display network "assists" were getting credit
The conversion window was set to 30 days - Any purchase within a month of clicking an ad was attributed to Google
Multiple touchpoints weren't being accounted for - Customers clicking ads, then finding the site through SEO, with Google getting full credit
The worst part? They'd been increasing budgets based on these inflated numbers for months. Their true ROAS was closer to 3.2x, not the 8.5x Google was reporting.
Here's my playbook
What I ended up doing and the results.
After discovering this attribution mess, I developed a systematic approach to audit and fix Google Ads conversion tracking for Shopify stores. This isn't about installing another pixel - it's about understanding what's really happening with your traffic.
Step 1: The 15-Minute Reality Check
Before changing anything, you need to know how bad the situation is. I run a quick comparison between Google Ads reported conversions and Shopify's source attribution:
Export Google Ads conversion data for the last 60 days
In Shopify Analytics, filter traffic by source "google / cpc"
Compare conversion counts and revenue between the two
Calculate the attribution gap percentage
If there's more than a 15% difference, you've got a problem worth fixing.
Step 2: Clean Up Google's Attribution Settings
Most of Google's "helpful" attribution features work against accurate tracking. Here's what I disable immediately:
View-through conversions - Unless you're running massive brand campaigns, these inflate numbers
Cross-network attribution - YouTube and Display assists rarely drive real ecommerce value
Store visits conversions - Physical store visits from online ads are usually correlation, not causation
I also adjust the conversion window from 30 days to 7 days for most ecommerce stores. People who take more than a week to buy after clicking an ad usually found you through other channels.
Step 3: Implement Server-Side Tracking
This is where most stores stop, but it's the most important part. I set up server-side tracking using Google Tag Manager Server Container or Shopify's new Customer Events API. This captures conversions that browser-based tracking misses due to:
iOS 14.5+ privacy restrictions
Ad blockers (used by 25-40% of users)
Cookie restrictions and deletions
JavaScript errors on checkout pages
Step 4: Create Multiple Attribution Models
Instead of relying on one "truth," I set up three different tracking approaches:
Google's Attribution - Keep the original setup for comparison
First-Click Attribution - Track what actually brought customers to your site first
Shopify Source Attribution - Use Shopify's built-in traffic source tracking
The truth usually lies somewhere between these three models.
Step 5: Weekly Attribution Audits
I run weekly reports comparing all three attribution models. This helps identify when Google Ads performance is genuinely improving versus when it's just attribution drift. The pattern recognition becomes invaluable for budget decisions.
For the outdoor gear client, this systematic approach revealed their true Google Ads performance and helped them reallocate €15K monthly budget to higher-performing channels.
Attribution Gaps
Google takes credit for organic and direct traffic conversions that happen after ad clicks
Cross-Device Issues
Mobile ad clicks credited for desktop purchases often involve multiple touchpoints
View-Through Inflation
People who saw your ad but found you through search get counted as Google Ads conversions
Window Settings
30-day attribution windows capture conversions that would have happened anyway
After implementing this tracking audit system across multiple Shopify stores, the results were consistently eye-opening. The outdoor gear client wasn't an outlier - they were typical.
Here's what the numbers looked like after three months of accurate tracking:
True ROAS dropped from 8.5x to 3.8x - Still profitable, but not the goldmine they thought
Attribution gap reduced from 36% to 8% - Much more reliable numbers for decision-making
Budget reallocation saved €18K monthly - Money moved from inflated Google Ads to proven SEO and email
Overall marketing ROI improved by 23% - Better allocation led to better results
The most interesting discovery was that their best-performing Google Ads campaigns actually had accurate tracking already. It was the mediocre campaigns that were being propped up by attribution inflation. Once we had clean data, we could optimize the good campaigns and pause the bad ones.
The client initially worried that "reducing" their Google Ads ROAS would hurt performance. The opposite happened. With accurate data, we could identify which keywords, audiences, and campaigns actually drove revenue. Their conversion rate improved by 31% within two months.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this tracking audit system across multiple Shopify stores, here are the patterns I've learned:
Every store has attribution inflation - I've never found a Shopify store where Google Ads wasn't taking some undeserved credit
The bigger the gap, the worse the campaigns - Stores with 50%+ attribution inflation usually have targeting problems
Brand campaigns inflate numbers the most - People searching your brand name were probably coming anyway
iOS 14.5 made attribution worse, not better - Privacy features broke tracking, but Google's models didn't adjust
Server-side tracking catches 15-25% more real conversions - The ones you're missing due to technical issues
First-click attribution often shows the real traffic source - What actually brought customers to your site initially
Clean tracking improves optimization - Google's algorithm works better with accurate conversion data
The biggest lesson? Don't question whether this is happening to you - question how bad it is. Every ecommerce store I've audited has had attribution issues. The only variable is severity.
I'd also do the attribution audit before any major campaign optimizations. There's no point optimizing campaigns based on inflated data. Clean up your tracking first, then optimize based on reality.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies using Google Ads to drive trial signups:
Focus on first-click attribution for top-of-funnel awareness campaigns
Track trial-to-paid conversion rates by traffic source separately
Use shorter attribution windows (3-7 days) for bottom-funnel keywords
For your Ecommerce store
For ecommerce stores serious about profitable Google Ads:
Run weekly attribution audits comparing Google Ads vs Shopify source data
Implement server-side tracking to capture iOS 14.5+ lost conversions
Disable view-through conversions unless running major brand awareness campaigns