Overview
If you're noticing a difference between the data reported in Polar and Google Analytics (GA4), you're not alone. Discrepancies between the two systems can occur for several reasons. Below, we explain the most common causes and what they mean for your data.
Data Latency from Google Analytics
Google Analytics can take up to 12 hours to fully process and consolidate data. Until that processing is complete, the data is still considered in progress and may continue to fluctuate.
After Google has finalized the data:
Polar then pulls and processes it.
Depending on your data refresh frequency in Polar, it might take additional time before it's fully reflected in your dashboards.
You can read more about GA4 data freshness in this Google support thread.
A Common Example of Latency
Here's a realistic example that shows how delays can stack up:
A user starts a session on their phone before bed.
They turn off their device before the tracking event is sent.
The next morning (8 hours later), the device turns back on and sends the event to Google.
Google takes up to 12 hours to process it.
Polar then takes up to 4 hours to sync the data.
Total latency: 8 hours (device delay) + 12 hours (GA processing) + 4 hours (Polar refresh) = 24 hours
Because of this latency, even data from previous days might appear inconsistent or incomplete at first.
Tip: If you need faster access to finalized GA data, consider:
Subscribing to Google Analytics 360 (reduces processing time to 1 hour).
Asking your Customer Success Manager about enabling hourly refreshes.
Discrepancies Within Google Analytics Itself
GA4 uses different databases for different purposes:
The data you see in Reports
The data in Explore
The data made available to third-party tools, like Polar
As a result, even within GA4, you may see inconsistencies depending on where you're looking.
Discrepancy within Google Analytics
Google Analytics uses a different database, and thus different version of the data:
to calculate its Reports
to calculate its Explore
to share data with 3rd party apps - including Polar Analytics.
Moreover, Google Analytics 4 per design, cannot give 100% accurate results that add up depending on the breakdowns and metrics combination.
Examples of GA4 Inconsistencies
Total Sessions vs. Sessions by Gender
GA4 may show:
Total sessions: 14,560
Sessions broken down by gender:
Male: 11,389
Female: 2,187
Unknown: 1,196
Sum: 17,772
In this case, the sum of the breakdown exceeds the total—a known issue with GA4's data aggregation logic.
Engaged Sessions by Session Type
You might also see:
Total engaged sessions: 300,000
Breakdown by session type:
Direct: 242,769
Referral: 50,295
Paid: 28,537
Sum: 321,601
Again, the sum of the breakdown is greater than the total.
These inconsistencies are expected behavior in GA4. The Google team has confirmed this limitation, and at this time, no fix is available.
How GA4 Query Structure Affects Data in Polar
GA4 uses different datasets for different purposes, including what you see in the GA4 interface (Reports, Explore) and what third-party tools like Polar can access via the API.
Because of this setup, you may notice slight differences between what you see in GA4 and what appears in Polar.
Here’s why:
GA4 is designed in a way that doesn’t always produce consistent totals across different combinations of metrics and breakdowns.
In Polar, we query the GA4 API using a specific set of dimensions to ensure the most meaningful insights. These queries can't be exactly replicated in the GA4 UI.
As a result, small discrepancies are expected — especially in metrics like sessions when broken down by attributes such as channel, device, or location.
Example: The session totals you see in Polar might differ slightly from those shown in GA4 if you’re looking at the same metric but applying different breakdowns.
That said, Polar accurately processes and aggregates all data received from GA4’s API. We don’t modify or re-calculate the values — the numbers shown in Polar are exactly what GA4 provides through the API.
Optional workaround: Our development team can configure a custom Sessions metric using an alternate set of breakdowns (e.g., Session Default Channel Group) that more closely mirrors what GA4 displays in its standard reports. If this is something you’d like, feel free to reach out to our support team.
Attribution Limitations in GA4
GA4’s API lacks granular, event-level data. This makes it:
Difficult to accurately tie GA4 traffic to Shopify conversions.
Risky to blend GA4 and Shopify data—can produce misleading metrics unless simplified.
To ensure attribution accuracy, Polar recommends prioritizing Pixel-based tracking over GA4. You can learn more about the Polar Pixel here.