When you compare the data in GA4 with Google Ads, it's not unusual to see some discrepancies in the reports. This is to be expected as Google Ads mostly measures user actions right before the click (or the impression) and GA4 starts measuring when the visitor reaches the landing page of a campaign. Understanding the reasons for this difference will give you more insights into your marketing data.
There are a few reasons for the Here are some of the most common reasons for these discrepancies:
Attribution models - GA4 and Google Ads use different attribution models and lookback windows, which can lead to differences in reported conversions. Google Ads has various attribution models, while GA4 uses a data-driven attribution model that credits all touchpoints.
Different types of metrics - Google Ads focuses on clicks and impressions, while GA4 prioritizes sessions, engaged sessions, and views. For example, if a user clicks on an ad three times and arrives at the website, Google Ads counts it as three clicks, while GA4 counts it as one session.
UTM tagging - If auto-tagging is not enabled, manual tagging or incorrect URL tagging can lead to data inaccuracies and the failure to attribute Google Ads campaigns. Make sure that you enable auto-tagging before investing any ad budgets in Google Ads.
Sampling - GA4 may sample data when analyzing large data sets over a prolonged period, leading to incomplete data for analysis.
Missing/faulty tracking - Inaccurate tracking or missing tracking on a separate landing page can lead to discrepancies in data. Cross-domain tracking issues can also lead to session disruptions, resulting in more data being visible in Google Ads and inaccurate data in GA4.
Time zones - When time zones don't align, data synchronization issues can arise. This can be easily resolved by setting the same time zone in both tools.
Ad blockers - Ad blockers can cause both tools to be blocked, resulting in incomplete data. Inconsistencies may also arise if ad-blockers are intermittently enabled or disabled, leading to challenges in data comparison.
While some discrepancies may be out of your control, there are ways to address others. For example, exporting data to a cloud tool like Google Cloud’s BigQuery and joining tables can help gain insights. Creating user-friendly dashboards using a visualization tool such as Google Looker Studio can also help communicate the impact of marketing efforts. The key is to understand the reasons for discrepancies and work towards minimizing them.
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