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Common Causes of Digital Analytics Discrepancies

analytics data discrepancies google

A common frustration among marketers and digital analysts when using multiple analytics platforms is the existence of data discrepancies. Due to these inconsistencies, it can be difficult to know which (if either) source to trust.

This qualm is completely understandable, especially when important advertising spend and business decisions are being made with this data in mind. The industry average for discrepancies in data reporting falls generally somewhere between 10-15% (certainly a statistically-significant range). 

Accurate reporting is crucial when it comes to using data to analyze performance or develop strategy. So, getting to the root of the issue, what causes these variations in data reporting? Let’s take a look at some of the most common sources of analytics discrepancies.

1. Improper Configurations

The most common reason for reporting discrepancies is configuration error. Simply put, analytics platforms must be implemented correctly in order to provide accurate data.

Here are some of the most common set-up errors we see with regards to Google Analytics:

  • Multiple Google Ads accounts are linked to one Google Analytics account. This will add unnecessary complexity to the reporting and can cause discrepancies.
  • Multiple Google Analytics codes are firing on the same landing page. As more and more users implement Google Tag Manager, this has become more common. Often, developers might forget to remove the hardcoded tracking code from the site when switching over. This results in a single ad click registering two Google Analytics sessions and creating a large discrepancy in reporting.
  • The data layer contains special characters that may not register. Sometimes product names or attributes contain special characters that cause Google Analytics to reject a legitimate hit. These may also cause the data layer to be misconfigured.

2. Collections and Reporting Differences Between Tools

There are some basic differences in how various platforms and tools collect, analyze, and report data.

For example, one essential difference between Google Ads and Google Analytics is that Google Ads filters out invalid clicks, but Google Analytics is unable to filter out invalid sessions resulting from these clicks. Therefore, Google Analytics will track and report these invalid sessions. However, Google Ads will not. If your Google Ads account is getting a lot of invalid clicks, the data will show many more Google Analytics sessions than ad clicks would indicate.

3. App Data Dispatch Processes

Depending on what time of day you are checking data reports, a discrepancy may exist due to the dispatch process an app uses.

Data is stored locally on a device until the app “dispatches” the data to Google Analytics on a separate thread. According to Google, data must be dispatched and received by 4 a.m. (in the local timezone of each view) the following day. Data that is reported later than that will not appear in your reports.

For example, let’s say a user generates a hit locally at 11:59 p.m. That data must be dispatched by 3:59 a.m. the next morning (so within 4 hours) in order to appear on reports. On the other hand, a hit that comes at 12:00 a.m. must also be dispatched by 3:59 a.m. the following day, or within 28 hours.

These delays in reporting can cause large fluctuations in data reporting if mobile apps occupy a significant portion of user activity.

4. Third-Party Pixel Problems

With seemingly-limitless data-tracking tools available now, users often install multiple trackers on the same site. These may record and report data differently.

Problems may also arise if there are too many tags firing at the same time from the same event.  If these tags are not set up properly, the data layers may not be sequenced in a way that is conducive to accurate reporting.

Someone who is trained to properly set up your data collection and reporting can make changes to the sequencing of data layer events. This will reduce discrepancies in your reports.

Reach Out with Specific Questions

The entire purpose of collecting and interpreting data is to get an accurate representation of your customers’ behavior and your online performance. If you see major discrepancies between reporting tools, it is difficult to know what to believe.

While the industry standard discrepancy in reporting is between 10-15%, at InfoTrust, we’ve historically been able to get our clients between 2-8%. A lower discrepancy rate means data you can trust to inform and empower your business’ most important decisions. 

Contact our team today if you have specific questions about analytics discrepancies currently causing your team to mistrust data that is necessary for decision-making.

Discrepancies got you down?

Contact the experienced InfoTrust analytics consulting team for help.
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