If you’ve tried to dive into your store’s data chances are that you’ve noticed that your reports from Google Analytics, Meta Business Manager, and Shopify don’t quite match up, and you’re wondering why. Figuring out what’s causing these discrepancies can feel like a headache, but with the right approach, you can get to the root of the issue and trust your data again. Below is a checklist to help pinpoint and resolve the most common causes of mismatched reports.
The items in the checklist can be roughly grouped into four main areas:
1. Measurement and Attribution Differences
Google Ads and Google Analytics track and attribute interactions differently, which can lead to data discrepancies.
- Clicks vs. Sessions: Google Ads counts ad clicks, while Google Analytics records sessions, potentially causing mismatches if a user clicks multiple times.
- Attribution Models: Default attribution settings differ, and consistent alignment between platforms is key for accurate conversion tracking.
- Cross-Device Tracking: Google Ads often has more advanced cross-device capabilities, which may lead to higher conversion counts compared to Google Analytics.
2. Technical and Tracking Configurations
Differences in technical configurations, tracking settings, and URL tagging can lead to discrepancies.
- URL Tagging and GCLID Parameters: Auto-tagging is preferred for accuracy, but manual tagging should be consistent to avoid misattribution. Redirects and stripping of GCLID parameters can also impact tracking.
- Tracking Limitations: Factors like ad blockers, session timeouts, and incomplete tracking code implementation can prevent accurate data collection, especially in Google Analytics.
3. Reporting and Timing Variances
Both platforms handle reporting timeframes and processing in unique ways, affecting data consistency.
- Conversion Timing and Data Refresh Rates: Google Ads attributes conversions to the ad click date, while Google Analytics uses the conversion date itself. Data refresh rates differ as well, with Google Ads updating faster.
- Time Zone and Currency Settings: Misaligned time zones and currency settings can cause revenue and performance metrics to appear inconsistent.
4. Privacy and Filtering Considerations
User privacy, data sampling, and filtering differences can also lead to discrepancies.
- User Consent and Privacy: Regulations like GDPR mean users may decline tracking cookies, limiting data in Google Analytics.
- Filters and Data Sampling: Google Analytics may apply bot filters, data thresholds, or sample data to ensure accuracy and privacy, which Google Ads doesn’t always replicate.
The Checklist
1. Measurement Differences
Ensure you understand how each platform counts user interactions.
- Confirm that Google Ads measures clicks, while Google Analytics measures sessions.
- Remember that multiple ad clicks from the same user can register as multiple clicks in Google Ads but only one session in Google Analytics.
- Note that Google Analytics may filter out repeat clicks from the same user within a short period, while Google Ads counts each click.
2. Attribution Models
Review attribution models across both platforms.
- Check that you’re using consistent attribution models in both Google Ads and Google Analytics to prevent varying conversion counts.
- Align settings between platforms to ensure that attribution matches across campaigns.
3. Technical Issues Leading to Discrepancies
Consider any technical limitations in tracking setups.
- Check for ad blockers that may prevent Google Analytics from tracking some users.
- Ensure you’re aware that Google Ads uses server-side tracking, while Google Analytics relies on client-side tracking, which can result in data differences.
- If users exit before the Google Analytics code loads, those sessions might not be counted even if Google Ads logs the click.
4. URL Tagging and GCLID Parameters
Use accurate URL tagging for proper traffic attribution.
- Enable auto-tagging in Google Ads to automatically add a GCLID parameter to URLs, reducing errors.
- If using manual tagging, ensure consistent UTM parameters (utm_source, utm_medium, utm_campaign, etc.) for accuracy.
- If your site uses redirects, confirm that GCLID parameters are preserved to avoid lost attribution.
5. Timing and Reporting Differences
Review reporting and conversion timing in both platforms.
- Understand that Google Ads attributes conversions to the click date, while GA4 uses the actual conversion date.
- Keep in mind that Google Ads reports data within 3 hours, while Google Analytics can take up to 9 hours to reflect changes.
6. Cross-Device Tracking
Account for cross-device tracking capabilities.
- Recognize that Google Ads has more advanced cross-device tracking than Google Analytics, which can lead to higher conversion counts, especially with substantial mobile traffic.
7. Data Processing and Reporting Differences
Check data handling and reporting preferences.
- Ensure conversion window settings are consistent across platforms. Google Ads allows longer windows (up to 90 days) than Google Analytics, which attributes conversions to sessions.
- Be aware of data refresh rates, as Google Ads updates more frequently, which may cause temporary mismatches with Google Analytics.
8. Platform-Specific Features
Understand platform-specific settings that can impact conversions.
- If using Smart Goals in Google Analytics, remember these are predictive metrics and may not align exactly with Google Ads data.
- Smart Goals estimate high-value sessions, which can differ from the actual conversion data in Google Ads.
9. Time Zone Differences
Align time zones across platforms.
- Ensure both Google Ads and Google Analytics are set to the same time zone to prevent discrepancies due to different reporting periods.
10. Data Sampling in Google Analytics
Watch out for sampling in Google Analytics reports.
- For high-traffic sites, Google Analytics may use sampled data, leading to potential discrepancies with the unsampled data in Google Ads.
11. Filters and Views in Google Analytics
Check filters and views applied to Google Analytics data.
- Filters excluding specific traffic (like internal or bot traffic) may lead to lower session counts in Google Analytics.
- Ensure you’re comparing data from the correct view in Google Analytics to avoid unintended discrepancies.
12. Conversion Counting Methods
Make sure conversion counting settings are aligned.
- Google Ads may count multiple conversions per click, while Google Analytics counts one per session by default. Align these preferences to ensure accuracy.
13. User Privacy and Consent
Consider user privacy and consent settings affecting data collection.
- Consent regulations (like GDPR) allow users to decline tracking cookies, which prevents Google Analytics from recording their sessions, though Google Ads may still log clicks.
14. Data Thresholds in GA4
Account for data thresholds in Google Analytics 4.
- When user counts are low, GA4 may apply data thresholds to protect privacy, which can limit visibility of session or conversion data.
15. Bot and Spam Traffic
Enable bot filtering for consistent data.
- Google Analytics may automatically filter bot traffic, while Google Ads might still include bot clicks, causing mismatches. Enable bot filtering where possible.
16. Currency Settings
Ensure currency settings are consistent.
- Different currency settings across platforms can lead to revenue discrepancies. Align currency settings in both Google Ads and Google Analytics.
17. Importing Goals and Transactions
Verify imported goal setups.
- Make sure imported goals from Google Analytics to Google Ads are configured correctly, as misaligned setups may lead to mismatched data.
18. Auto-Tagging Issues
Test for auto-tagging issues that may prevent data syncing.
- Some websites strip GCLID parameters during redirects, which can prevent Google Analytics from attributing traffic correctly.
19. Different Date Ranges and Filters in Reports
Compare data using identical date ranges and filters.
- Ensure the date ranges are consistent across reports and check for any filters that might exclude relevant data in one platform but not the other.
20. Attribution of Direct Traffic
Check for misattribution in direct traffic.
- If Google Analytics cannot determine the source of traffic due to broken or missing parameters, it may categorize it as direct traffic, causing attribution issues.
21. Differences in User Identification
Understand how each platform identifies users.
- Google Analytics relies on cookies, which may be cleared by users, leading to identification issues. Google Ads may track users differently, resulting in data inconsistencies.
In Conclusion
With these steps, you can identify and resolve most data discrepancies between Google Analytics and Google Ads. This checklist should simplify the process of aligning your data, helping you get the insights you need for smarter decision-making. Run through these checks periodically, and you’ll find that most mismatches clear up, leaving you with clean, reliable data to work from. Happy tracking!
