What is Checked in a Google Analytics Audit?

Making business decisions with bad data is like trying to navigate with a broken compass. If your Google Analytics setup is collecting flawed information, every report you run is leading you further off track. That's where an audit comes in. This guide provides a complete checklist to help you validate your Google Analytics configuration, ensuring the data you rely on is accurate, trustworthy, and ready for action.

What is a Google Analytics Audit (and Why Do You Need One)?

A Google Analytics audit is a systematic "health check" of your entire analytics setup. You're not looking for insights into user behavior just yet, instead, you're focusing on the technical foundation. The goal is to find and fix any issues in your account structure, tracking code implementation, data filtering, and conversion tracking that could be compromising your data's integrity.

The saying "garbage in, garbage out" is especially true for analytics. A poorly configured account can lead to wildly inaccurate conclusions. For example, if you're not filtering out your own company's traffic, you might think you have high user engagement when it's really just your internal team browsing the site. This could cause you to invest in marketing campaigns that appear successful on the surface but are ultimately failing to reach real customers. An audit protects you from these costly mistakes, giving you confidence that your strategic decisions are based on reality.

The Complete Google Analytics Audit Checklist

We’ve broken down the audit process into seven key areas. Work your way through this list to ensure every part of your GA account is functioning properly. Whether you're using the newer Google Analytics 4 or the older Universal Analytics (UA), these principles will guide you to a cleaner setup.

1. Account, Property, and View Structure

The first step is confirming that your account is organized logically. A messy structure can make maintenance difficult and lead to reporting errors down the line.

Checking Your Hierarchy

Google Analytics is organized in a hierarchy: Account > Property > View (for UA) or Account > Property > Data Stream (for GA4). An "Account" is the highest level, typically representing your company. A "Property" is a specific website or app. Ensure this hierarchy makes sense for your business. You should have one Account for your organization, and at least one Property for your main website. If you have separate staging sites or mobile apps, they should generally have their own Properties as well, unless you're intentionally tracking them together.

Best Practices for Views (Universal Analytics)

If you're still on Universal Analytics, having a structured approach to Views is critical, as there's no way to undo filters once they’ve been applied. The standard best practice is a three-view setup:

• Raw Data View: This view should have zero filters. It's your raw, untouched backup. If you ever make a mistake in your other views, you can always refer back to this one.
• Test View: Before applying a filter to your main reporting view, you should always test it here first. This is your sandbox for experimenting with new filters, goals, and settings without risking your primary historical data.
• Main/Master View: This is the cleaned-up view you use for all your daily analysis and reporting. It should have filters applied to exclude internal IP addresses, bots, and spam traffic.

Verifying Your Data Streams (Google Analytics 4)

GA4 replaces Views with "Data Streams." Instead of filtering data into separate buckets, GA4 consolidates data from multiple sources (like a website, an iOS app, and an Android app) into a single Property. In your GA4 Property, navigate to Admin > Data Streams. You should see a data stream set up for your website. Verify that the URL is correct and that it shows it's receiving data.

2. Data Collection and Tracking Code

This is where things can often go wrong. An incorrectly installed tracking code is one of the most common causes of data discrepancies.

Is the Tracking Code Installed Correctly?

The easiest way to check this is with the free Tag Assistant Legacy by Google extension for Chrome. Install it, visit your website, and enable the extension. It will show you all the Google tags firing on that page. You should see your Google Analytics tag (your "G-" ID for GA4 or "UA-" ID for Universal Analytics) appear with a green or blue icon. A red icon means there's an error in the implementation that needs to be fixed immediately.

Is the Code on Every Page?

It's easy to assume the tracking code is everywhere, but it can be missed on certain sections of a site—like a blog hosted on a subdomain, special landing pages, or the checkout confirmation page. A missing code on even one page breaks user journey tracking. Spot-check key pages across your site using Tag Assistant to be sure the code is present everywhere a user can go.

Are You Collecting Duplicate Data?

Sometimes, the same tracking code gets installed twice, which will significantly inflate your metrics. This can happen if the code is added directly to your website's header and also deployed via Google Tag Manager. Tag Assistant will reveal this by showing the same GA tag firing twice. A clear sign of this issue in your reports is an unusually low bounce rate (often under 2-3%), as each page visit is being counted twice.

3. Filters and Data Exclusions

Clean data means filtering out noise that doesn't represent real customer activity. Proper filters make your reports more accurate and useful.

Filtering Internal and Developer Traffic

Every visit from your own team, freelance partners, and agencies skews your data. Set up a filter to exclude traffic from your office and remote employees' IP addresses.

• In GA4: Go to Admin > Data Streams > [Your Stream] > Configure tag settings > Show more > Define internal traffic. Here, you can define IP addresses to exclude.
• In UA: Navigate to Admin > View > Filters and create a new predefined filter to "Exclude" traffic from specific IP addresses.

Excluding Bot and Spam Traffic

Spam bots visiting your website can wreak havoc on your data. Thankfully, Google makes this relatively easy to manage.

• In UA: Go to Admin > View > View Settings and ensure the "Bot Filtering" checkbox is checked.
• In GA4: Bot filtering is enabled automatically, so there isn't a manual setting you need to configure for this.

Campaign and URL Tagging (UTM) Consistency

UTM parameters (utm_source, utm_medium, etc.) are what tell Google Analytics where your traffic is coming from. Inconsistencies will fragment your reporting. For example, Facebook, facebook, FB, and facebook.com will all show up as separate traffic sources. Check your Acquisition reports for inconsistent casing or naming conventions and establish a standardized UTM creation process for your entire team.

4. Goals, Conversions, and Events

Ultimately, analytics should measure if you're achieving your business objectives. This step verifies that your conversions are tracking accurately.

Reviewing Conversions in GA4

In GA4, nearly every interaction is an "event" (like a page_view, click, or form_submit). You simply toggle which of these events you want to count as a "Conversion." Navigate to Configure > Conversions. Do the listed conversions still align with your primary business goals? Remove any old or irrelevant ones. Also, check for redundancy—if you have two events tracking the same form submission, only one should be marked as a conversion to avoid double-counting.

Checking Goals in Universal Analytics

For UA, go to Admin > View > Goals. Review your list of goals. Disable any that are outdated. For your most important goals ("Contact Us" form fills, "Request a Demo" submissions, etc.), go through the process yourself. Open an incognito window, complete the action on your website, and then check the Real-Time > Conversions report a moment later to see if your goal completion was recorded correctly.

5. E-commerce Tracking Setup

For online stores, this section is non-negotiable. Broken e-commerce tracking means you can't properly measure ROI from your marketing efforts.

Is E-commerce Tracking Enabled?

This is an easy miss.

• In UA: Head to Admin > View > Ecommerce Settings. The "Enable Ecommerce" and "Enable Enhanced Ecommerce Reporting" toggles should both be on.
• In GA4: Enhanced measurement will capture some activity automatically, but proper setup for events like add_to_cart and purchase requires configuration through Google Tag Manager or your e-commerce platform's integration (like Shopify's native integration).

Cross-domain Tracking for Payment Gateways

When a customer leaves yourstore.com to complete a payment on paypal.com and then returns, Google Analytics might incorrectly see this as two separate visits—one from your site and another from PayPal. You lose attribution for the sale. To fix this, add the domains of third-party payment processors to your Referral Exclusion List (yourstore.com -> admin -> Data Streams -> a web data stream -> Configure tag settings -> configure -> Domains in GA4) so GA treats the entire session as one journey.

Testing Actual Transactions

The best way to audit your e-commerce tracking is to conduct a test transaction yourself. Place a small order and then compare the revenue data that appears in Google Analytics with the actual order value in your store's backend (e.g., Shopify, Magento). Minor discrepancies are always possible due to things like ad blockers, but if the numbers are wildly different, it points to a significant issue in your tracking setup.

6. Reviewing Key Integrations

Google Analytics becomes vastly more powerful when connected to other Google marketing tools.

Checking Google Ads and Search Console Links

This connection is essential for a complete picture of your search performance. In either GA4 or UA, navigate to Admin > Product Links or Product Linking. Verify that both Google Ads and Google Search Console are connected to the correct property. Without these links, you can't view paid search cost data in GA or see detailed organic keyword performance reports from Search Console.

Enabling Auto-tagging in Google Ads

Inside your Google Ads account, go to Account Settings > Auto-tagging and make sure it's enabled. When on, every click on your ads will have a unique GCLID (Google Click Identifier) parameter added to the URL. This gives you far richer campaign data in Google Analytics than you would get from manual UTM tagging alone.

7. Data Quality and Audience Segmentation

Finally, a quick look at your reports and audiences can reveal subtle underlying issues.

Reviewing Audiences and Segments

Look through the Audiences or Segments you've created previously. Are they still relevant for retargeting campaigns or analysis? Do the definitions make sense? Sometimes a broken audience definition, such as one based on a page that no longer exists, can result in wasted ad spend.

Checking for "(not set)" and "(other)" Values

As you browse through your most-viewed reports, watch for a high percentage of (not set) values. In a Landing Page report, for instance, (not set) can indicate issues with session tracking. A large proportion of sessions having a (not set) Landing Page can signal poorly executed Event Tracking can 'hijack' sessions from the original Landing Page visit and obscure data about how your traffic actually lands on and behaves on your website.

Final Thoughts

Completing a Google Analytics audit gives you something invaluable: trust in your data. By methodically checking your structure, tracking code, filters, and conversions, you transform GA from a potential source of misinformation into a reliable compass for guiding your business growth. Set a reminder to perform a mini-audit every quarter to maintain data hygiene as your website evolves.

While an audit ensures your data is clean, the true goal is getting answers without friction. That’s why, after connecting our platforms correctly, we use Graphed to do the heavy lifting. We connect all our marketing and sales sources—like Google Analytics, Google Ads, a CRM—and instantly build real-time dashboards just by asking in simple terms. It lets everyone on our team answer their own questions, turning all that pristine data you just audited into actionable insights in seconds.