What Can You Not Track on Google Analytics?

Google Analytics is an incredibly powerful tool for understanding website traffic, but it doesn't see everything. Knowing what GA can't track is just as important as knowing what it can, helping you avoid misinterpreting your data and find the right tools to fill the gaps. This article breaks down the key limitations of Google Analytics and explains what you can do about them.

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Personally Identifiable Information (PII)

This is the most critical and non-negotiable limitation of Google Analytics. You are strictly forbidden by Google's Terms of Service from collecting any Personally Identifiable Information (PII). Doing so can get your GA account suspended and could violate major privacy regulations like GDPR and CCPA.

PII is any data that could be used to directly identify a specific individual. This includes, but isn't limited to:

• Full Names: Sending "john_smith" as a custom dimension is a violation.
• Email Addresses: Capturing emails in URLs or events is a common but serious misstep.
• Phone Numbers: Never pass phone numbers into GA fields.
• Mailing Addresses: Physical addresses are considered sensitive PII.
• Social Security Numbers or Government IDs: This should go without saying, but never send this data anywhere near GA.

GA is designed for aggregated, anonymized analysis. It tells you how many people came from a certain city or what percentage of users are on mobile, not what a specific person named Jane Doe did on your site. The platform achieves this user-level tracking through anonymous identifiers like a Client ID (for browsers/devices) and a User-ID (for logged-in users you assign), but these IDs are intentionally kept separate from actual personal information.

What To Do About It

If you need to analyze customer behavior at an individual level, you need a different tool. A Customer Relationship Management (CRM) platform like Salesforce or HubSpot, or a dedicated Customer Data Platform (CDP), is built for this purpose. These systems are designed to securely store PII and connect it with customer interactions across different touchpoints, helping you see the full picture for identified leads and customers.

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Complete Cross-Device and Cross-Silo User Journeys

Picture this common scenario: a user sees your ad on Instagram while on their phone, clicks to your website, and browses. Later that day, they use their work laptop to search for your brand on Google, click through again, and sign up for your newsletter. The next day, they get an email, click a link from their tablet, and finally make a purchase.

In this person’s mind, that was one continuous journey. But to Google Analytics, that could look like three separate users on three different devices. GA has gotten much better at stitching these journeys together with features like Google Signals and User-ID measurement, but it’s still far from perfect.

The core challenge is that GA, by default, relies on browser cookies (Client ID) to identify a "user." Every new browser and device gets a new cookie, fragmenting the user journey. The only reliable way to connect these is through a consistent User-ID, which you can only assign once someone logs into your site or app. Anyone browsing anonymously remains fragmented.

This fragmentation also extends beyond devices into data silos. GA knows what happens on your website, but it has no visibility into what happens in your sales CRM, email marketing platform, or help desk software. Answering a simple question like, "Which of our marketing channels drive the most qualified leads that actually close?" is nearly impossible using GA alone.

Specific Individual User Sessions and Interactions

While GA helps you analyze user behavior in aggregate, it doesn't let you watch what an individual, anonymous user actually did during their visit. You can see that 500 users visited your product page and that 20% of them added an item to the cart, but you can't see the specific journey of one of those users: where they moved their mouse, what they clicked on, or if they seemed confused by the user interface.

GA reports on the "what" on a macro level (e.g., number of pageviews, bounce rate, conversion rate), but it misses the qualitative "why" on a micro, individual level. Why did so many users drop off at the second step of the checkout? Was a button broken? Was the shipping cost confusing? Was there a distracting pop-up?

You can use segments and create explorations in GA4 to get closer to individual user behavior paths, but it’s not the same as a video replay.

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Tools to Help Fill the Gap

This is where specialized user behavior analytics tools come in. Platforms like Hotjar, FullStory, and Microsoft Clarity offer features that GA does not:

• Heatmaps: These tools create visual overlays on your web pages, showing where users are clicking, moving their mouse, and how far they scroll down a page.
• Session Recordings: You can watch video-like replays of real, anonymous user sessions to see their exact journey through your site, including clicks, scrolls, and navigation.

Combining the quantitative data from Google Analytics with the qualitative insights from these tools gives you a much richer understanding of your user experience.

Most Offline Conversions and Interactions

Google Analytics lives online. It is fantastic at tracking digital behavior on websites and mobile apps. However, user journeys often transition from the online world to the offline world, and that's where GA’s visibility drops off.

Consider these examples:

• A user clicks a Google Ad, browses your e-commerce site, but decides to call your sales team to place their order over the phone.
• A potential customer researches your restaurant online, looking at the menu and location, and then visits in person that evening.
• A lead fills out a contact form on your website (an event GA can track), but the deal is actually closed weeks or months later by a sales rep in your CRM.

By default, GA has no knowledge of these offline events. This creates a huge hole in your attribution reporting, especially for businesses with a sales team, physical locations, or long sales cycles. Your marketing team might be driving incredibly valuable offline actions, but without connecting the dots, your GA reports will undervalue their efforts.

How to Bridge the Online-Offline Gap

While not an out-of-the-box feature, GA does provide methods for sending offline data into the platform. This requires some technical work but is incredibly powerful:

• Data Import: You can periodically upload CSV files containing offline data (like lead status updates from your CRM or refund data from your payment system) and associate it with an online user via their Client ID or User-ID.
• Measurement Protocol: For more advanced or real-time needs, you can use the Measurement Protocol to send data "hits" directly to GA's servers from any internet-connected device, like a point-of-sale system or a backend server.

Setting this up allows you to finally connect ad spend to closed deals, providing a true measure of your marketing return on investment.

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Cost Data from Non-Google Ad Platforms

One of the best features of Google Analytics is its seamless integration with Google Ads. Once you link the accounts, all of your Google Ads campaign data - including impressions, clicks, cost, and CPC - flows directly into GA. This allows you to build powerful reports comparing ad spend to website engagement and conversions.

The problem? This only works for Google Ads.

Most modern marketing teams advertise across a variety of platforms: Facebook Ads, LinkedIn Ads, TikTok Ads, Twitter (X) Ads, Bing Ads, and more. Google Analytics does not automatically connect to these third-party platforms to pull in cost data. You might be able to see that a campaign from Facebook drove traffic and conversions (by using UTM parameters), but you won't see how much that campaign cost you within GA's interface. Without the "cost" piece of the puzzle, you can't calculate crucial metrics like Return on Ad Spend (ROAS) or Cost Per Acquisition (CPA) directly in GA.

To get this data into Google Analytics, you have to use the Data Import feature to manually upload it. This is a notoriously frustrating process for many marketing teams. It often involves exporting a CSV from Facebook or another ad platform every day or week, making sure it’s formatted exactly right for GA’s requirements, and then uploading it. It’s manual, time-consuming, and easy to forget.

Final Thoughts

Google Analytics is an essential free tool for any business, but its limitations mean it can't tell you the whole story. By avoiding PII, understanding that the user journey is often fragmented, and acknowledging its blind spots around offline behavior and third-party ad spend, you can use GA for what it's best at: analyzing aggregated on-site behavior.

As you get deeper into your analysis, you’ll find that many of these challenges – like pulling in cost data from Facebook Ads or connecting web sessions to CRM data – boil down to a simple problem: your data lives in too many different places. Instead of spending hours manually exporting and importing reports, we built a way to automate it. With Graphed you can securely connect all your platforms - Google Analytics, your CRM, Shopify, Facebook Ads, Google Ads - and then ask questions in simple, natural language. We help you create the reports you actually need, without all the busywork.