Data Import is an often overlooked feature of Google Analytics but it can certainly allow you to enrich your existing data, filling gaps and providing new perspectives that might have been unavailable before.
In this article, we explore a practical example of data import for event parameters and demonstrate how it unlocked simple yet valuable insights previously missing from our reports.
Nearly every business today is also a publisher. If a business is not delivering fresh, useful content to its users, chances are that it’ll fall off their radar. Moreover, just simply producing content isn’t enough—publishing without analysis can lead to wasted time and resources. Understanding what resonates with your audience (through data analysis) is crucial for optimizing your content strategy. This may sound like Marketing 101, but it underscores the foundation of any successful content strategy: data-driven decision-making.
Missing Information
The problem we encounter more often than not during analysis is “missing” data. This can not only happen due to an incomplete or poorly conceived measurement and tracking plan but sometimes even with a plan that seemed well thought-out at the time.
It’s often only during the analysis phase that you realize this missing piece that could really get your further with decision making. This is where Google Analytics’ Data Import feature becomes invaluable—allowing you to fill in those gaps, enrich your data with missing details, or append new information, ensuring your analysis is as comprehensive as possible.
Contrary to what you might think, importing data isn’t as complex as it seems. And if mistakes happen (though rare), you can always remove the import file with minimal risk (at least in many cases). There is a small risk that imported data that gets processed will stay in the account and therefore, its imperative to first understand how data gets imported before you do your first import.
How Data Import Works in GA4
When you import any data, it gets processed in one of the two ways depending on the type of data you imported. How this data gets processed determines whether you can safely remove the data after import without repercussions. Before you import, it is important to understand these two ways of processing:
- Data import file is used when new data gets collected i.e. at the time of Collection or Processing. This means that your data file will not affect any historical data but will only apply on data collected going forward. This is the risky one where you should NOT make a mistake with the import or else the data won’t get deleted even if you delete your import file.
- Data import file is used when you run reports i.e. at the time of Reporting or Querying. Which means that the data CSV file you import sits in your account. When you create a report, only then the connections or joins to this file are evaluated. This type of data import is not risky because it does not affect your processed data and I’d suggest that your first data import should be this one.
So how do you decide which type of processing should apply on your data? Well, you don’t. GA4 decided that.
There are 6 types of data types import possible in GA4 and each of them have a pre-defined processing type.
As mentioned above, the Reporting time data import will prove less risky and in case you screw up something, you can always delete the import and re-import with corrections. The Collection time imports are also not risky as long as you realize and correct your error before any data was collected and processed.
Limits with Data Import in Google Analytics 4
Did you notice the limits associated to each data type in the above table? Data imports come with a Storage limit and Data Source limit.
The Storage limit refers to the size of your data. It applies to only the data type that uses Reporting time processing because Analytics uses these files to join/merge the data when you run reports, while the other data types are all processed and collected like the rest of your data and the files aren’t used during reporting (thus less overhead on the system).
The Data source limit refers to the number of files you can add for each data type.
Despite its immense potential, the data import functionality remains under-appreciated (IMO) and seldom discussed within Google Analytics communities. Yet, this method offers significant opportunities to add value to your data, often with just a simple import process. So far we have covered the basics that are definitely worth knowing before we talk about our example on a simple Data Import. However, you must familiarize yourself with detailed information about it in the GA help documentation.
Here we’ll explore a case study demonstrating how we used Custom Event Data Import to enhance our historical data, giving us crucial insights into the most popular blog topics among our users. These insights guided us in refining the themes we should focus on moving forward.
Case Study:
Boosting our Content Strategy with Custom Event Data Import in GA4 by uncovering Top Blog Topics
When publishing articles in WordPress, Categories and Tags are commonly used to associate topics and themes with blog content. These attributes often drive WordPress recommendation engine, but they’re frequently left out of GA4’s measurement and tracking setup. It’s understandable—when planning a content calendar, analytics isn’t always top of mind.
The WordPress blog in our case study followed a similar pattern. While categories and tags were applied from the start, they weren’t integrated into Google Analytics, leading to the following missed opportunities:
- Without these key insights, it’s easy to overlook the broader themes driving user engagement and rely on indirect indicators for future content decisions.
- Additionally, the ability to create GA4 audiences based on specific topics was lost.
Although we were capturing page URLs in GA4, determining which topics resonated most with users was a tedious and inefficient process with URLs. Moreover we are using content grouping to categorize page types i.e. Blog pages, Site Pages, Landing pages and User Account Pages. Therefore, we set out to address the gap with Data Import plus fixing the tracking to capture this information upfront in the future.
Requirement: Assigning Topics to Each Post (Past and Future) to Identify Popular Themes
Each blog post needed to be assigned a specific Topic, both for existing content and all future posts. The goal was to generate reports in GA4 that would provide clear insights into which topics were most popular among our users, helping guide our content strategy moving forward.
Methodology:
- Create an event parameter called “topic” and pass it with every page view event (for blog posts) by adjusting the GA4 Event Variable in Google Tag Manager. This ensures future data collection includes the topic for each post.
- Add a custom dimension called “topic” in Google Analytics 4 that captures the event parameter “topic,” allowing it to be used in reports.
- Import existing blog articles with “topics” into the GA4 account, enabling the analysis of historical data.
The first two steps are straightforward and won’t be detailed here. However, if you need guidance, feel free to ask in the comments, and we’ll update the post accordingly.
Below we focus on #3 – how the Data Import process worked and the insights it provided.
Data Preparation:
The first step in our process was to prepare the data for import. In this case, the task was straightforward: we needed to associate one new event parameter—“topic”—which had been missing from our GA4 data, with an existing parameter—“page_path”—that had always been collected.
If you’re familiar with working with data, you’ll recognize this as a basic JOIN operation. For the import to succeed, our data file needed to include two types of data points:
- A JOIN key: This refers to an existing data point in our GA4 account that will act as the connector between the current data and the imported data.
- An Imported field: This is the new data being added to the existing dataset.
In our case, the JOIN key was the “page_path”, and the imported field was the “topic”.
To prepare for the import, we created a CSV file with two columns: one for the page_path of all blog articles, and another for the corresponding topic. With this file ready, we were set to move forward with the import.
Data Import:
To enrich our GA4 data, we’ll use the “Custom Event Data” type to import the CSV file, as the data we are adding—“topic”—is an event parameter.
The data import process consists of two key steps:
- Adding Data Source Details i.e. uploading the prepared CSV.
- Mapping the Data: This step requires more attention and explanation, as it defines how your imported data will be matched with existing GA4 data. Proper mapping is critical to ensure the enrichment works as expected.
Mapping the Data:
After uploading the CSV and clicking “Next,” it’s time to map the data. This step is crucial because it determines how effectively the imported data will integrate with your existing GA4 setup. You’ll make two key selections in this step:
- Key Fields: This is where you define the JOIN keys that connect your imported data with the data already in GA4. For Custom Event Data, you can only define two JOIN keys. Fortunately, in our case, we only need one—page_path.
GA4 offers a limited set of available keys for joining, which includes:
- All Custom dimensions you’ve in your account,
- Stream ID,
- Hostname,
- Page Path,
- Page Title,
- Page Title and screen class,
- Page Title and screen name.
Since our property only has one stream ID and hostname, we don’t need to use any other fields for this join. The Page Path is the only JOIN key we need.
Once you select Page Path, you’ll also choose the corresponding column header in the CSV under “import field”, which will display a preview of the sample data.
- Import Fields: After mapping the JOIN key, it’s time to define the fields you want to import. This is where the enrichment happens. In our case, we’re importing just one field—“topic”—but GA4 allows up to 10 fields to be imported and mapped to existing dimensions (except for Stream ID).
Since we already created “topic” as a custom dimension, we select it here and map it to the topic column in our CSV. You’ll see sample data populate alongside the mapped fields, ensuring everything is aligned correctly.
Once you’re satisfied with the mapping, it’s time to click the Import button—and that’s exactly what we did.
Depending on the size of your CSV, the upload process might take a few moments. Once completed, you’ll receive a confirmation with a timestamp and the status of your import, ensuring the data was successfully processed.
With the data imported, we can now move on to the Explore section in GA4 to create our report.
The results? A clean, insightful report that shows our users have engaged more with Shopify-related topics, providing us with clear guidance on the content themes to focus on moving forward.
GA4’s Data Import: A Step-by-Step Guide
The use case we outlined above is just one basic example, but we hope it sparks ideas for how you can enrich your own data in GA4. Start by identifying what’s missing or incomplete in your current tracking setup, but can still be captured and made available through external sources. From there, follow these key steps:
- Identify Missing Data: Look at your reports and think critically about what’s missing—whether it’s product details, user attributes, or campaign parameters—and how these gaps are impacting your ability to make data-driven decisions.
- Determine the Right Import Type: Based on your needs, select the most appropriate data import type—whether it’s custom event data, user data, or cost data. Ensure you understand whether it will process data historically or for future collection only.
- Check Available Key Fields and Import Fields: Before preparing your data, review the available Key Fields (the fields used to join the imported data to your GA4 dataset) and Import Fields (the new data you’ll be adding) to ensure they align with your objectives.
- Consider Storage and Data Source Limits: Remember that GA4 has storage and source limits for certain types of data, so plan your imports accordingly to avoid hitting these limits and potentially disrupting your data.
- Data Preparation and Validation: Once you’ve defined your fields, ensure your data is clean, accurate, and properly formatted before importing. Double-check for any inconsistencies in the join keys or the data points you’re enriching.
- Test and Monitor: After importing, closely monitor your reports to ensure that the data has been correctly mapped and integrated. This step is crucial for identifying any issues early and making corrections if necessary.
Take your time to assess what additional insights would help your analysis, and use the Data Import feature as a powerful tool to bring your data to life.
Data Import Enhances Your Content Strategy and Fills Tracking Gaps
A multitude of publishing channels offers marketers limitless opportunities to reach their ideal audience. However, at the heart of all these channels arguably lies a central hub—the blog, which serves as the foundation for content shared across social media, email, and other platforms. To keep producing fresh, valuable content, continuous analysis is essential. And meaningful analysis is only possible when your data collection is comprehensive and well-planned.
If you’ve missed key data points in your initial collection or tracking setup, don’t worry. GA4’s Data Import feature provides an easy way to fill those gaps and enrich your existing data. Take a moment to review your current setup—Data Import might just be the solution to unlocking deeper insights and optimizing your content strategy.
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