Data Discrepancies Between Google Ads and Analytics – How to Fix Them and Why They Matter

If you’re using Google Ads and Google Analytics to measure your online marketing performance, you might have noticed that the data from these two platforms don’t always match. This can be frustrating and confusing, especially when you’re trying to optimize your campaigns and make data-driven decisions.

Why do Google Ads and Google Analytics data discrepancies happen? How can you fix them? And more importantly, how can you use both platforms to get a more complete picture of your online marketing performance?

In this article, I’ll answer these questions and more. I’ll explain the common causes of data discrepancies between Google Ads and Google Analytics, how to check your configuration settings, and how to use both click and session metrics in your reports. I’ll also show you how to go beyond conversion and click tracking and use advanced features like call tracking, attribution modeling, and cross-device reporting.

By the end of this article, you’ll have a better understanding of how Google Ads and Google Analytics work together, how to troubleshoot data discrepancies, and how to leverage both platforms to improve your online marketing results.

Configuration Checklist

Before we dive into the reasons why Google Ads and Google Analytics data can be different, let’s make sure that your configuration settings are correct. Here are some basic steps you should take to ensure that your Google Ads and Google Analytics accounts are properly linked and configured:

• Link your Google Ads account to your Google Analytics account. This will allow you to import Google Ads data into Google Analytics and vice versa. You can link multiple Google Ads accounts to the same Google Analytics account, but make sure that each Google Ads account is linked to only one Google Analytics property.
• Enable auto-tagging in your Google Ads account. Auto-tagging is a feature that automatically adds a parameter called gclid (Google Click ID) to the destination URL of your ads. This parameter helps Google Analytics identify the source, medium, campaign, ad group, keyword, and other information of each click. Auto-tagging is enabled by default in new Google Ads accounts, but you should check that it is not disabled or overridden by manual tagging.
• Check your Google Analytics tracking code. Make sure that your Google Analytics tracking code is present on every page of your website and that it fires correctly. You can use tools like [Google Tag Assistant] or [Google Analytics Debugger] to verify that your tracking code is working properly.
• Check your time zone settings. Make sure that your time zone settings in both Google Ads and Google Analytics are consistent. If they are different, you might see discrepancies in the date and time of clicks and sessions.

If you’ve followed these steps and everything looks good, then you’re ready to move on to the next section. If not, you should fix any issues before proceeding.

Reasons Tracking in Google Ads and Analytics Can Be Different

Even if your configuration settings are correct, you might still see some differences between the data in Google Ads and Google Analytics. This is because these two platforms use different methods and metrics to track user behavior.

Here are some of the common reasons why tracking in Google Ads and Analytics can be different:

Discrepancies Between Clicks and Sessions

One of the most common sources of data discrepancies is the difference between clicks and sessions. Clicks are tracked by Google Ads, while sessions are tracked by Google Analytics.

A click is recorded when a user clicks on your ad on a Google network (such as Search or Display). A session is recorded when a user lands on your website after clicking on your ad (or any other source) and interacts with it within a certain time frame (usually 30 minutes).

In theory, every click should result in a session, but in practice, this is not always the case. There are several scenarios where clicks and sessions can differ:

• Users click on the same ad multiple times. If a user clicks on your ad more than once within a short period of time (such as 30 seconds), Google Ads will count each click as a separate click, but Google Analytics will count them as one session. This is because Google Analytics uses the gclid parameter to identify unique sessions from the same source. If the gclid parameter is the same for multiple clicks, they will be grouped into one session.
• Users don’t reach your website after clicking on your ad. If a user clicks on your ad but doesn’t land on your website for some reason (such as slow loading speed, browser issues, or network errors), Google Ads will count it as a click, but Google Analytics won’t count it as a session. This can also happen if the user closes the browser tab or window before the page loads completely.
• Users reach your website but don’t trigger a session. If a user lands on your website after clicking on your ad but doesn’t interact with it in any way (such as scrolling, clicking, or filling out a form), Google Ads will count it as a click, but Google Analytics won’t count it as a session. This can happen if the user bounces immediately or if your website doesn’t have any interactive elements.
• Google Analytics can’t read the gclid parameter. If the gclid parameter is missing, modified, or corrupted in the destination URL of your ad, Google Analytics won’t be able to attribute the session to Google Ads. This can happen if you have redirects, URL rewriting, or other modifications that remove or change the gclid parameter. It can also happen if the user has browser extensions or settings that block or alter the gclid parameter.

Discrepancies in the Conversion Rate

Another common source of data discrepancies is the difference between conversion tracking in Google Ads and Google Analytics. Conversions are actions that you want your users to take on your website, such as making a purchase, signing up for a newsletter, or downloading a file.

Google Ads and Google Analytics use different methods and metrics to track conversions. Here are some of the main differences:

• Google Ads and Google Analytics use different attribution models. An attribution model is a set of rules that determines how credit is assigned to different touchpoints along the user journey. Google Ads uses a last-click attribution model by default, which means that it gives all the credit to the last ad click before the conversion. Google Analytics uses a last-non-direct-click attribution model by default, which means that it gives all the credit to the last non-direct source before the conversion. A direct source is when a user types your website URL directly into the browser or uses a bookmark. For example, if a user clicks on your ad, then leaves your website, then returns later by typing your URL and converts, Google Ads will count it as a conversion from your ad, but Google Analytics will count it as a conversion from direct traffic.
• Google Ads and Google Analytics use different conversion windows. A conversion window is the period of time after a user clicks on your ad or visits your website that you want to track conversions for. Google Ads uses a 30-day conversion window by default, which means that it will count conversions that occur within 30 days of the last ad click. Google Analytics uses a 6-month conversion window by default, which means that it will count conversions that occur within 6 months of the last non-direct source. For example, if a user clicks on your ad, then leaves your website, then returns after 31 days by typing your URL and converts, Google Ads won’t count it as a conversion from your ad, but Google Analytics will count it as a conversion from direct traffic.
• Google Ads and Google Analytics use different conversion actions. A conversion action is a specific type of conversion that you want to track for your campaigns. Google Ads allows you to create and customize different conversion actions for different goals, such as purchases, leads, sign-ups, calls, downloads, etc. You can also import conversion actions from Google Analytics into Google Ads. Google Analytics allows you to set up goals for different types of conversions, such as destination, duration, pages/screens per session, and events. You can also import goals from Google Analytics into Google Ads. However, not all conversion actions and goals are compatible between Google Ads and Google Analytics. For example, Google Ads supports call conversions (when a user calls your business from your ad or website), but Google Analytics doesn’t support them by default. Conversely, Google Analytics supports event-based conversions (when a user performs an action on your website that triggers an event), but Google Ads doesn’t support them by default.
• Google Ads and Google Analytics use different counting methods. A counting method is how you want to count conversions for multiple interactions within the same conversion action or goal. Google Ads allows you to choose between two counting methods: one-per-click and many-per-click. One-per-click counts only one conversion per ad click for each conversion action. Many-per-click counts every conversion per ad click for each conversion action. For example, if a user clicks on your ad and makes two purchases on your website within 30 days, one-per-click will count one purchase conversion and many-per-click will count two purchase conversions. Google Analytics allows you to choose between two counting methods: unique and total. Unique counts only one conversion per session for each goal. Total counts every conversion per session for each goal. For example, if a user visits your website from an organic search and downloads two files within one session, unique will count one download goal and total will count two download goals.

How to Fix Data Discrepancies Between Google Ads and Google Analytics

Now that you know the common causes of data discrepancies between Google Ads and Google Analytics, you might be wondering how to fix them and make your data more accurate and consistent. Here are some tips and best practices to help you do that:

• Enable auto-tagging again and avoid manual tagging. If you have disabled auto-tagging in your Google Ads account or used manual tagging to override it, you should enable it again and remove any manual tags from your destination URLs. Auto-tagging is the easiest and most reliable way to ensure that Google Analytics can attribute your sessions and conversions to Google Ads. Manual tagging can cause errors, conflicts, or duplicates in your data, especially if you use the same parameters as auto-tagging (such as utm_source, utm_medium, utm_campaign, etc.).
• Use both click and session metrics in your reports. Instead of relying on only one type of metric (clicks or sessions), you should use both types of metrics in your reports to get a more complete picture of your user behavior. For example, you can use the [Google Ads reports] in Google Analytics to see both clicks and sessions for your Google Ads campaigns, ad groups, keywords, etc. You can also use the [Search Console reports] in Google Analytics to see both clicks and sessions for your organic search traffic. By using both types of metrics, you can identify and analyze the gaps between clicks and sessions, such as bounce rate, landing page quality, loading speed, etc.
• Invest in call tracking solutions to measure your call conversions. If you’re using call extensions or call-only ads in your Google Ads campaigns, you should also use a call tracking solution to measure your call conversions in Google Analytics. Call tracking solutions can help you track the source, duration, location, and outcome of each phone call from your ads or website. Some examples of call tracking solutions are [CallRail], [CallTrackingMetrics], [Invoca], etc. You can also use [Google forwarding numbers] to track calls from your Google Ads campaigns.
• Use consistent conversion actions and goals across platforms. If you’re using different conversion actions in Google Ads and different goals in Google Analytics, you should make sure that they are consistent and compatible across platforms. For example, if you’re tracking purchases as a conversion action in Google Ads, you should also set up a purchase goal in Google Analytics with the same parameters (such as value, currency, etc.). You can also import conversion actions from Google Analytics into Google Ads and vice versa to ensure consistency.
• Use the same attribution model across platforms. If you’re using different attribution models in Google Ads and Google Analytics, you should try to use the same attribution model across platforms to avoid discrepancies in conversion credit. For example, if you’re using a last-click attribution model in Google Ads, you should also use a last-click attribution model in Google Analytics. You can change your attribution model in both platforms by using the [Attribution] tool in Google Ads and the [Model Comparison Tool] in Google Analytics.

Go Beyond Conversion and Click Tracking in Google Ads and Analytics

While fixing data discrepancies between Google Ads and Google Analytics is important, it’s not enough to optimize your online marketing performance. You also need to go beyond conversion and click tracking and use advanced features that can help you gain more insights into your user behavior across devices, channels, and touchpoints.

Here are some of the advanced features that you can use to enhance your online marketing measurement:

• Use cross-device reports to understand how users interact with your website across different devices. Cross-device reports are a set of reports in Google Analytics that show how users move between devices (such as desktop, mobile, tablet) before converting on your website. These reports can help you understand how users switch devices during their journey, how device usage varies by user segment, how device combinations affect conversion rates, etc. To use cross-device reports, you need to enable [User ID] tracking in Google Analytics.
• Use multi-channel funnels to analyze how different marketing channels work together to drive conversions. Multi-channel funnels are a set of reports in Google Analytics that show how users interact with different marketing channels (such as organic search, paid search, social media, email, etc.) before converting on your website. These reports can help you understand how users discover your website through different sources, how long it takes them to convert after their first interaction, how different channels influence each other along the path to conversion, etc.
• Use attribution modeling to compare how different attribution models affect the value of your marketing channels. Attribution modeling is a feature that allows you to compare how different attribution models assign credit to different marketing channels for conversions. For example, you can compare how a last-click attribution model differs from a first-click attribution model or a linear attribution model. Attribution modeling can help you evaluate the performance of your marketing channels more holistically and allocate your budget more effectively.
• Use data-driven attribution to let Google Analytics use your data to create a custom attribution model. Data-driven attribution is a feature that uses machine learning to analyze your data and create a custom attribution model that best reflects the impact of your marketing channels on conversions. Data-driven attribution can help you optimize your marketing mix and improve your return on investment. To use data-driven attribution, you need to have enough data in your Google Analytics account and meet certain eligibility requirements.

Google Ads & Analytics Discrepancies FAQs

Here are some of the frequently asked questions about Google Ads and Google Analytics data discrepancies and how to fix them:

• Why is my bounce rate higher in Google Ads than in Google Analytics?
  • Your bounce rate is higher in Google Ads than in Google Analytics because Google Ads and Google Analytics use different definitions of bounce rate. Google Ads defines bounce rate as the percentage of clicks that result in a single-page session on your website. Google Analytics defines bounce rate as the percentage of sessions that result in a single-page session on your website. Therefore, if a user clicks on your ad multiple times within a short period of time, Google Ads will count each click as a separate bounce, but Google Analytics will count them as one session and one bounce.
• Why are my conversions lower in Google Ads than in Google Analytics?
  • Your conversions are lower in Google Ads than in Google Analytics because Google Ads and Google Analytics use different methods and metrics to track conversions. Some of the possible reasons are:
    • Google Ads and Google Analytics use different attribution models, conversion windows, conversion actions, and counting methods.
    • Google Ads tracks conversions from the last ad click, while Google Analytics tracks conversions from the last non-direct source.
    • Google Ads tracks conversions from any device, while Google Analytics tracks conversions from the same device by default.
    • Google Ads tracks call conversions, while Google Analytics doesn’t support them by default.
• How can I make my data more consistent between Google Ads and Google Analytics?
  • You can make your data more consistent between Google Ads and Google Analytics by following these steps:
    • Link your Google Ads account to your Google Analytics account and enable auto-tagging.
    • Check your Google Analytics tracking code and time zone settings.
    • Use both click and session metrics in your reports.
    • Invest in call tracking solutions to measure your call conversions.
    • Use consistent conversion actions and goals across platforms.
    • Use the same attribution model across platforms.

Conclusion

Data discrepancies between Google Ads and Google Analytics are inevitable, but they don’t have to be a headache. By understanding the common causes of data discrepancies, checking your configuration settings, and using both platforms to complement each other, you can fix most of the data discrepancies and make your data more accurate and reliable.

However, fixing data discrepancies is not enough to optimize your online marketing performance. You also need to go beyond conversion and click tracking and use advanced features that can help you gain more insights into your user behavior across devices, channels, and touchpoints.

By using cross-device reports, multi-channel funnels, attribution modeling, and data-driven attribution, you can enhance your online marketing measurement and improve your return on investment.

I hope this article has helped you understand how to fix data discrepancies between Google Ads and Google Analytics and how to use both platforms to get the most out of your online marketing efforts.

If you have any questions or feedback, please feel free to leave a comment below or contact me at istiquritconsultant@outlook.com Thank you for reading!