Overview
When comparing metrics in Polar to those shown directly in your ad platforms (like Meta, Google, TikTok, or others), it’s common to notice discrepancies. While this can be confusing or concerning at first glance, these differences are usually expected and explainable.
This article explains why data discrepancies happen, which checks to run first, and how to confidently validate your numbers in Polar. By the end, you’ll know how to identify the root cause of mismatches and ensure you’re making decisions based on accurate, aligned data.
In this article, we’ll cover:
The most common reasons Polar and ad platforms don’t match
How attribution logic impacts reported performance
Step-by-step troubleshooting checks you can apply immediately
Section 1: Understand Why Data Discrepancies Are Normal
Before diving into troubleshooting, it’s important to understand that Polar and ad platforms are built for different purposes, which directly affects how data is collected and reported.
Key reasons discrepancies occur
1. Different attribution models
Ad platforms typically use their own attribution windows (e.g. Facebook's 7-day click, 1-day view), while Polar uses a single, consistent attribution model across all channels. This means:
Ad platforms may over-attribute conversions to themselves
Polar prioritizes cross-channel consistency and deduplication
2. Different event definitions
Metrics like “purchases,” “conversions,” or “revenue” may not be defined the same way:
Ad platforms often include modeled or estimated conversions
Polar relies on actual tracked events from your ecommerce platform
3. Timing and timezone differences
Data can shift depending on:
The timezone set in your ad platform vs. Polar
When conversions are recorded (click time vs. purchase time)
💡 Important: A discrepancy does not automatically mean something is broken. In many cases, it means Polar is showing a more conservative, reality-based view of performance.
Section 2: Check Your Core Configuration First
Most unexpected data gaps can be traced back to configuration mismatches. Before investigating deeper, run through these foundational checks.
1. Confirm date ranges and timezones
Make sure you’re comparing:
The same date range
The same timezone across Polar and the ad platform
Even a small timezone offset can shift conversions into a different day.
2. Validate your integrations
Check that:
The ad platform integration is connected and active
Your ecommerce platform (Shopify, etc.) is fully synced
There are no recent disconnects or sync errors
If an integration was paused or reconnected recently, partial data for that period is expected.
3. Compare the right metrics
Ensure you’re aligning equivalent metrics:
Revenue vs. conversion value
Purchases vs. conversions
Click-through conversions vs. total conversions
Avoid comparing modeled metrics in ad platforms with raw metrics in Polar.
Section 3: Understand Attribution & Deduplication in Polar
One of Polar’s biggest strengths is its cross-channel attribution logic, which can make numbers look lower — but more accurate.
How Polar handles attribution
Each order is attributed once, even if multiple channels were involved
Polar removes double-counting that can happen when multiple ad platforms claim the same conversion
This often results in lower (but cleaner) numbers compared to ad platform dashboards
What this means for your analysis
Expect Polar totals to be lower than the sum of all ad platforms
Use Polar as your source of truth for blended performance
Use ad platforms primarily for in-platform optimization, not total revenue reporting
💡 If Polar exactly matched every ad platform, it would mean conversions were being double-counted.
Section 4: When to Investigate Further (and When Not To)
Normal and expected differences
You usually don’t need to worry if:
Discrepancies are within a reasonable range
Trends over time move in the same direction
Polar data aligns closely with your ecommerce backend
Red flags worth investigating
Dig deeper or reach out to support if:
Entire days or channels show zero data
Differences are extreme or sudden
Revenue in Polar doesn’t align with your ecommerce platform at all
When contacting support, sharing screenshots, date ranges, and the exact metrics you’re comparing will help resolve issues faster.
Conclusion
Data discrepancies between Polar and ad platforms are common, expected, and usually healthy. They’re a result of different attribution models, event definitions, and the deduplication logic that makes Polar a reliable source of truth.
Key takeaways:
Always align date ranges, timezones, and metrics first
Expect ad platforms to over-report compared to Polar
Trust Polar for cross-channel, decision-making insights
Use ad platforms for tactical, channel-specific optimization
By understanding why differences exist, you can move forward with confidence and focus on optimizing performance — not reconciling numbers.