GA4 vs Shopify analytics: understanding data discrepancies

If you’re running an e-commerce business on Shopify and using Google Analytics to track your marketing performance, you’ve likely noticed frustrating discrepancies between the two platforms. One day, Google Analytics shows 50 orders while Shopify reports 65. The next day, your revenue figures don’t match either. These inconsistencies aren’t errors—they’re the result of differences in how each platform collects, processes, and reports data.

Understanding these discrepancies is crucial to make informed decisions. When you know why the numbers differ, you can interpret your data more accurately and choose the right platform for specific insights. In this article we will look into these differences. Let’s go!

Platform purposes and differences

Google Analytics and Shopify Analytics serve different purposes, which dictates how they collect data. Google Analytics is a web analytics platform designed to track user behavior across websites, measure marketing attribution, and provide insights into cross-domain customer journeys. It gives you an understanding of how users interact with your site, which marketing channels drive the most valuable traffic, and how different touchpoints contribute to conversions.

Shopify analytics, on the other hand, is built specifically for e-commerce operations. It focuses on sales performance, order management, and customer lifetime value because it’s integrated directly into your store’s backend. When someone completes a purchase, Shopify knows about it immediately and accurately because the transaction happens within its own ecosystem.

The tracking methods reflect these different purposes. GA4 relies on JavaScript-based tracking and cookies, which means it depends on client-side code executing in users’ browsers. Shopify uses server-side data combined with client-side events, giving it more reliable access to transaction data but less insight into broader user behavior. Let’s look at some more differences between the two.

Common discrepancies between GA4 and Shopify

The difference between sessions and visitors

The most obvious discrepancies appears in basic traffic metrics. Google Analytics usually reports less sessions and users than Shopify shows visitors. This happens because Google Analytics has bot filtering that removes non-human traffic from your reports. Additionally, ad blockers and privacy-focused browsers can prevent Google Analytics from loading entirely, creating blind spots in your data.

Shopify counts more page loads, often appearing inflated compared to Google Analytics. Since Shopify’s visitor tracking is partially server-side, it’s less affected by ad blockers but may include more bot traffic.

Order and transaction differences

The most critical discrepancies occur in order and revenue reporting. Google Analytics underreports transactions compared to Shopify’s actual sales data. This happens for several reasons: users may complete purchases but leave before the Google Analytics tracking code fires on the thank-you page, JavaScript errors can prevent tracking from working properly, and ad blockers can block the e-commerce tracking entirely.

Shopify’s transaction numbers represent actual sales recorded in the system. When someone completes a purchase, Shopify knows about it immediately because the transaction happens within its platform. This makes Shopify the source for actual sales data, while Google Analytics provides estimates based on successful tracking implementations.

Revenue calculation differences

Even when both platforms track the same transaction, they might report different revenue amounts. Shopify often shows higher revenue figures because it includes taxes and shipping costs by default, and it may count gross sales rather than net sales depending on your setup. GA4 tracks revenue through tracking tags, which you configure to send specific values. If these tags are misconfigured or missing key components like tax and shipping, your GA revenue will appear lower than Shopify’s reports.

Different attribution models

GA4 offers attribution modeling with customizable lookback windows. You can choose how credit gets distributed across multiple touchpoints in a customer’s journey, whether through last-click attribution, first-click attribution, or data-driven models that use machine learning to assign credit.

Shopify uses last-click attribution based on its own tracking capabilities. It doesn’t account for off-site touchpoints like Google Analytics. If a customer discovers your brand through a Google ad, researches on social media, and then returns directly to purchase, Shopify might attribute the sale to direct traffic while Google Analytics could give credit to the Google ad.

Technical limitations and external factors

The influence of ad blockers

Ad blockers and browser privacy features significantly impact GA4 data collection. Modern browsers increasingly block tracking scripts by default, and privacy-conscious users actively install ad blockers that prevent it from functioning. These same tools do not affect Shopify’s ability to track purchases because the transaction data is processed server-side.

Cookie consent and privacy regulations

Cookie consent banners add another layer of complexity. Users might reject GA4 tracking cookies but still complete purchases on your site. This creates a scenario where Shopify records the sale, but Google Analytics has no visibility into that customer’s journey or conversion. Shopify’s own customer privacy settings can impact the measurement in Shopify so don’t blindly trust on what Shopify is presenting to you.

Time zone mismatches

Different time zone settings between platforms can cause discrepancies in daily reports. If Google Analytics is set to Pacific Time but Shopify uses Central European Time, the same purchase might appear in different days’ reports.

Event-based vs transaction-based tracking

GA4’s event-based model requires the setup of e-commerce events like ‘purchase’, ‘add_to_cart’, and ‘begin_checkout’. Each of these events must be properly configured and tested to ensure accurate tracking. If any part of this implementation fails, you’ll see gaps in your GA4 e-commerce data.

Shopify tracks based on backend events that happen automatically when customers interact with your store. When someone adds a product to their cart or completes a purchase, Shopify knows about it because these actions happen within its system. This makes Shopify’s e-commerce tracking more reliable for basic transaction data.

Implementation errors that cause discrepancies

Many discrepancies stem from technical implementation issues. Misconfigured GA4 setups are quite common, including missing purchase events, incorrect currency settings, and improper dataLayer configurations. UTM parameters might be missing or incorrect, causing channel misattribution that makes your marketing performance appear different in Google Analytics compared to Shopify’s referrer data.

These technical issues can compound over time, for example when many different people work on the store, making discrepancies larger and more difficult to diagnose without careful analysis of your tracking implementation.

When to trust which platform

The key to working with both platforms effectively is understanding their strengths and using them for its intended purpose. Trust Shopify for actual revenue, order counts, refunds, and any financial reporting. Shopify is your source of truth for what actually happened in terms of sales and fulfillment.

Use GA4 for marketing attribution, channel performance analysis, and understanding user behavior on your site. Google Analytics excels at showing you how different marketing channels contribute to your success and how users interact with your website before making purchases.

How to get the most accurate insights?

Rather than trying to make the numbers to match exactly, develop a strategy for using both platforms together. Use Google Analytics for traffic insights, funnel analysis, and calculating marketing return on ad spend. Rely on Shopify for backend performance metrics and financial reporting that needs to be precise.

Consider implementing server-side tracking solutions like server-side Google Tag Manager to reduce the gap between platforms. This approach processes tracking data on your server rather than relying entirely on client-side JavaScript, making it more resistant to ad blockers and browser privacy features.

Moving forward with confidence

Data discrepancies between Google Analytics and Shopify analytics are normal given their different purposes. Instead of viewing these differences as problems to solve, treat them as complementary views on your performance.

Focus on trends and relative changes rather than absolute numbers when comparing platforms. If GA4 shows your conversion rate improving by 15% and Shopify shows similar positive trends in sales, that’s more meaningful than worrying about the exact numerical differences between platforms.

By understanding why these discrepancies occur and leveraging each platform’s strengths, you can make more informed decisions about your marketing strategies and business operations. The goal isn’t perfect data alignment—it’s using the right data from the right platform to answer your specific business questions.

Happy analysis!