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What Data is Google Analytics Unable to Track?

Cody Schneider•

Google Analytics is the cornerstone of digital marketing for a reason - it’s a powerful tool that tells you what happens on your website. But even a tool this essential has blind spots. Understanding what Google Analytics can't track is just as important as knowing what it can, as it helps you see the full picture of your marketing performance. This guide covers the key types of data you won’t find in Google Analytics and why it matters.

Why You Can’t Track Personally Identifiable Information (PII) in GA

This is the most critical and non-negotiable limitation. Google’s Terms of Service explicitly forbid you from sending any Personally Identifiable Information (PII) to their servers. This is done to protect user privacy and comply with a growing number of data protection laws like GDPR in Europe and CCPA in California.

So, what exactly counts as PII? It's any data that can be used to directly identify an individual. This includes information like:

  • Names

  • Email addresses

  • Phone numbers

  • Mailing addresses

  • Social security numbers or other national ID numbers

You have to be careful, as you can sometimes send PII to Google Analytics by accident. A classic example is seeing an email address appear in a URL after a form submission, like www.yoursite.com/thank-you?email=jane.doe@example.com. If that full URL is captured in GA, you've violated the terms of service.

Now, you might think, "How can I track a specific user's journey without their personal info?" For this, Google provides the User-ID feature. This allows you to assign a unique, non-identifiable, and anonymous ID (like user_12345) to users who log into your site. This lets you connect a user’s sessions across a single device, but it never involves sending actual personal information. It’s an anonymous key, not a user profile.

The Offline and Cross-Device Blind Spots

A customer's path to purchase rarely happens in one neat, linear session on a single device. It's often a messy journey spread across phones, laptops, and even offline interactions. This is where Google Analytics starts to struggle.

Connecting the Dots Across Devices

Out of the box, Google Analytics is largely cookie-based. This means if someone visits your website on their iPhone in the morning and then visits again on their work laptop in the afternoon, GA recognizes them as two different users. That’s because the browsing cookie is unique to each device and browser.

Google has solutions to mitigate this, but they aren't perfect:

  • Google Signals: This feature leverages data from users who are signed into their Google accounts and have Ads Personalization turned on. It helps de-duplicate users across devices, but it only works on a subset of your traffic — the users who meet those specific criteria.

  • User-ID: As mentioned earlier, this is the most accurate way to stitch together a single user's journey. However, it only works for users who log in to your platform, leaving unsubscribed or anonymous visitors out of the picture.

Essentially, unless you have a robust login system and have enabled User-ID, you’re likely seeing an inflated "user" count and a fragmented view of the customer journey.

What Happens Offline, Stays Offline

Google Analytics is a digital analytics tool. It has no native way of knowing what happens in the real world. Think about all the ways a customer can convert offline:

  • They see a marketing campaign, browse your site, then call your sales team to place an order.

  • They research a product on your website, then visit your physical store to purchase it.

  • They get an email promotion and speak to a sales rep at a trade show.

From GA's perspective, these users simply look like they abandoned the website. It has no visibility into the final conversion, completely missing the impact of your digital efforts on offline sales. While advanced setups using something called the Measurement Protocol allow developers to send offline data to GA, this is a highly technical solution that’s far from a simple, out-of-the-box feature.

Beyond Your Website: The Siloed Data Problem

Your business doesn't live inside a single platform, and neither does your data. Google Analytics is fantastic for analyzing website behavior, but it can’t access the rich, contextual data sitting inside your other essential business tools.

CRM and Sales Data

Your Customer Relationship Management (CRM) software (like HubSpot or Salesforce) is the source of truth for your sales pipeline. It holds crucial data GA knows nothing about, such as:

  • Lead status (e.g., New, Contacted, Qualified, Disqualified)

  • Deal size and value

  • Close rates by sales rep

  • Reasons for lost deals

A "conversion" in Google Analytics — like a "Contact Us" form submission — means something very different to a marketer than a "Sales Qualified Lead" (SQL) means to a sales rep. You might get 100 form-fill conversions in GA, but if your CRM shows that only 8 of those turned into real sales opportunities, the story of your campaign’s success changes dramatically. GA can’t tell the difference between a curious browser and a ready-to-buy lead.

Ad Platform Performance & Spend

Sure, you can use UTM parameters to see traffic and conversions in GA from Facebook Ads, LinkedIn Ads, or other platforms. But GA is still missing half the story. The data it can't track includes:

  • Ad Spend: The most critical metric for calculating Return on Ad Spend (ROAS) and Cost Per Acquisition (CPA). GA only shows you revenue, not cost.

  • Impressions: You can't see how many people saw your ad.

  • Cost Per Click (CPC) or Cost Per Mille (CPM): The efficiency metrics for your campaigns are invisible inside GA.

  • Platform-Specific Engagement: Metrics like Video Views on Facebook or Clicks to Profile on Instagram aren't captured.

This forces you into a manual reporting process: exporting conversion data from GA and ad performance from six different ad managers into a spreadsheet just to figure out what’s actually profitable.

Profit Margins, COGS, and Shipping Costs

For e-commerce businesses using platforms like Shopify, Google Analytics’s enhanced e-commerce tracking is great at reporting revenue. The problem is, revenue isn’t profit. GA doesn’t track business-critical financial data like:

  • Cost of Goods Sold (COGS)

  • Shipping and fulfillment costs

  • Transaction fees

  • Return and refund rates

A campaign that drives $5,000 in revenue in GA might look like a huge success. But if the ad spend was $3,000 and the COGS was $2,500, you’ve actually lost money. Without pulling in financial data from other sources, you’re making decisions based on top-line revenue instead of bottom-line profitability.

Understanding "Why": The Qualitative Gap

GA is a master of quantitative data. It excels at answering descriptive questions:

  • What pages did users visit?

  • How many users came from organic search?

  • Where are my visitors located?

  • When are they leaving the site?

Where it falls short is in answering the most important question of all: Why? GA can’t tell you why users abandoned their shopping carts, why they bounced from a landing page, or why they struggled with your checkout process. Answering these questions requires qualitative tools that analyze the user experience, such as heatmaps, session recording software (like Hotjar or FullStory), and user surveys. These tools provide the human context that raw numbers alone cannot.

Other Key Limitations to Be Aware Of

Finally, there are a few technical limitations that can impact the accuracy and completeness of your GA reports.

  • Ad Blocker Blind Spots: An increasing number of internet users use ad and script-blocking extensions. These tools often block the Google Analytics tracking script from running, meaning these visitors are completely invisible in your reports. Your actual traffic numbers are likely higher than what GA is reporting.

  • Data Sampling: If you have a high-traffic website, GA might show you sampled reports to speed up load times. Instead of analyzing every single session, it analyzes a representative subset and extrapolates the results. For most day-to-day reporting, this is acceptable, but for deep analysis, it means you're looking at a close estimate, not 100% precise data.

  • Bot Traffic: Google has robust filtering systems in place, but some bot or spam traffic inevitably slips through. This can artificially inflate metrics like sessions and users, giving you a slightly skewed view of real human activity. You can often spot it as traffic with a 100% bounce rate and a 1-second average session duration from an odd location.

Final Thoughts

Google Analytics is an indispensable tool, but it only shows you one piece of an increasingly complex business puzzle. Its inability to track PII, the full cross-platform journey, critical cost and sales data from other platforms, and the qualitative "why" behind user actions means you're never truly seeing the complete picture on its own.

We know how difficult it can be to pull data from Google Analytics, Facebook Ads, Shopify, and your CRM, then stitch it all together in spreadsheets. That endless cycle of downloading CSVs just to understand your basic performance metrics is why we built Graphed. It connects to all of your data sources in seconds, letting you create dashboards and get answers using simple, natural language. Instead of spending hours wrangling data, you can instantly see the whole story in one place and finally get back to growing your business.