Tired of conflicting reports between your paid ad platforms and Google Analytics 4? This kind of data discrepancy can make it nearly impossible to trust your numbers or optimize your campaigns effectively.
Google Analytics 4 (GA4) eliminates this confusion by allowing you to import clicks, impressions, and cost data directly into its platform. This not only simplifies your data integration process but also unlocks a major advantage: it enables you to calculate Return on Ad Spend (ROAS) using a single, consistent attribution model. Instead of relying on each advertising platform’s proprietary model, GA4 ensures that all your paid media sources are measured by the same yardstick, simplifying decision-making and improving the quality of your insights.
This article introduces “The GA4 UTM Strategy,” a method that leverages GA4’s native data import to unify your reporting environment, streamline UTM usage, and standardize how you attribute performance across multiple ad platforms.
Table of contents
Understanding the traditional challenges
Before GA4 offered data import features, marketers faced several problems:
- Manual processes: Traditional UTMs require you to manually create and maintain naming conventions for every campaign, risking errors and inconsistencies.
- Complex integrations: Aligning cost, click, and impression data with user behavior often meant building out complicated data pipelines or juggling endless spreadsheets.
- Inconsistent attribution models: Each ad platform typically applies its own attribution model, resulting in multiple, sometimes conflicting versions of ROAS. This made it tough to truly understand which campaigns were most effective.
How GA4 data import solves these challenges
With GA4’s data import, the game changes:
- Direct data imports: Bring in cost, click, and impression data directly into GA4, removing the need for separate ETL processes.
- Automated stitching: GA4 takes care of aligning external ad data with on-site engagement and conversion events, drastically reducing the risk of human error.
- Unified attribution for ROAS: This is the big win. GA4 applies a single, consistent attribution model to all your paid ads data. Instead of comparing apples to oranges, you finally have a level playing field, allowing you to calculate ROAS fairly and accurately across all campaigns and channels.
- Consolidated reporting environment: With all your marketing data centralized, you can produce more reliable, easy-to-interpret reports without relying on multiple tools.
Implementing The GA4 UTM Strategy
Create UTM templates with ID-based parameters
While GA4’s data import feature makes life easier, a few best practices still apply:
1. Leverage ad platforms dynamic ID insertion:
Utilize macros or dynamic placeholders in your ad platforms to automatically insert the relevant dynamic IDs into your UTM templates. This automation reduces the risk of human error and ensures that your UTM parameters are consistently populated with accurate IDs.
For example, here’s our Tracking Template recommendation for Google Ads:
{lpurl}?utm_source=google&utm_medium=cpc&utm_id={campaignid}&utm_campaign={_campaign}&ad_group_id={adgroupid}&ad_id={creative}
Click here for detailed instructions on leveraging macros and dynamic IDs for the most popular ad platforms.
2. Use URL-friendly campaign names inside ad platforms:
Keep your campaign names concise, lowercase, and free of special characters or spaces so they integrate seamlessly into UTMs. This approach makes your URLs cleaner, reduces the chance of encoding errors, and simplifies reporting.
Example:
Instead of using a campaign name like:
US – Women – Shoes – MOF – Conversions – June 1st 2023
Use a more URL-friendly version like:
us-women-shoes-mof-conv-june1-2023
For Google Ads and Microsoft Ads, if naming campaigns in a URL-friendly format isn’t possible directly within the ad platform, you can implement a script that dynamically converts campaign names into a URL-friendly version. To learn how, read our article here.
Import paid ads data using GA4 data import feature
Getting set up is more straightforward than you might think. You can import data into GA4 using two methods:
CSV File: This method involves uploading data from a CSV file manually at regular intervals. It’s suitable for small-scale data imports.
SFTP Server: The SFTP server method allows you to connect GA4 with your server and schedule periodic data uploads. This is more suitable for large-scale or automated data imports.
Manual CSV data import
1. Export data from ad platforms:
Obtain your click, impression, and cost data from your paid ad platforms. This can be done via manual exports or API-based connectors.
The following dimensions and metrics are required:
- Campaign ID (utm_id)
- Source (utm_source)
- Medium (utm_medium)
- Campaign name (utm_campaign)
- Date (YYYY-MM-DD)
- Clicks, impressions and cost as daily values
Click image to expand
2. Upload CSV via GA4 Data Import:
In GA4, go to Admin > Data collection and modification > data import. Then click on “Create data source”, name your data source, and select “Cost data” for the data type. Upload your CSV file and let GA4 handle the stitching process.
3. Complete data mapping:
After your file is uploaded, GA4 will guide you through a data mapping process. You’ll match your file’s columns—like campaign IDs, cost, clicks, and impressions—to GA4’s corresponding fields.
After importing data into Google Analytics 4 (GA4), it’s important to note that it may take up to 24 hours for the imported data to be reflected in your GA4 reports.
If you’re experiencing issues with the import, please consult our section “How to fix unknown data import status”.
SFTP data import (advanced)
Coming soon.
Reporting and analysis in GA4
There are two ways to visualize your new unified data in GA4.
Explorations report
- 1. In Google Analytics 4, on the left sidebar, go to Explore, then click the (+) symbol to create a new exploration.
- 2. Select the date range for which you imported paid ads data.
- 3. Add the dimension of your choice (e.g., Campaign, Source, Medium)
- 4. Add the following metrics: Non-Google impressions, Non-Google clicks, Sessions, Non-Google cost, Non-Google cost per click, Key events, Purchase revenue, Non-Google cost per key event interaction, and Return on non-Google ad spend.
Advertising report
- 1. In Google Analytics 4, on the left sidebar, go to Advertising > Planning > All channels.
- 2. Select the date range for which you imported paid ads data.
- 3. Select Campaign as your primary dimension, and source/medium as your secondary dimension.
It’s important to note that the imported data may take up to 24 hours to be reflected in your GA4 reports.
Here’s a comparison of how the data looks in GA4 once imported:
Click image to expand
Here’s the same data reported by Microsoft Ads:
Click image to expand
Since this account is a DTC brand with a very short sale cycle, the numbers are very close. You should also expect a much bigger difference between GA4 and Meta Ads. That’s the point. The idea is to finally see the result of your paid ad campaigns when they all use the same attribution model.
How to fix “unknown” data import status
If you’re encountering an “Unknown” status during your data import, try these solutions:
- Remove any formatting from your spreadsheet before converting it to CSV.
- Log out of all Google accounts except the one you’re using for the data import.
- Ensure that campaign names, sources, and mediums in your paid ads data match exactly how they are reported in Google Analytics 4.
If none of these steps resolve your issue, feel free to contact us for assistance. We’re here to help!
Useful links
- GA4 import cost data (Google Documentation)
- GA4 about data import (Google Documentation)
- Our recommended UTM structures
- Renaming campaigns script
*BONUS*
You don’t have to stop there. You can also import cost data for email or social campaigns. The process is very similar to the one outlined for paid ads. Here’s how:
- 1. Ensure every links from your email/social campaigns have the necessary UTMs for mapping (utm_source, utm_medium, utm_campaign, utm_id).
- 2. Export the data from your email/social platforms broken down by day into a spreadsheet.
- 3. Sum the costs related to your email/social campaigns (software, salaries, etc.) for the date range exported.
- 4. Divide the total by the number of rows exported
- 5. Use that value for cost.
- 6. Use the number of emails sent to be impressions.
- 7. Number of open as clicks.
- 8. Import to GA4 using the data import feature.
Conclusion
The GA4 UTM Strategy offers a long-awaited solution to the persistent data discrepancies that arise from relying on each paid ad platform’s unique attribution model. By centralizing your click, impression, and cost data in GA4—and applying a single, consistent attribution framework—this approach finally ensures that your metrics are directly comparable, accurate, and fair.
This, however, is just the start. GA4’s data import feature goes far beyond cost data. You can extend this strategy to incorporate product information, user data, client records, and even offline interactions. By integrating additional data sources, you’ll unlock a more nuanced understanding of user behavior and campaign performance. As the ecosystem matures, the possibilities for a 360-degree view of your marketing ROI are virtually limitless.
Let me know about your experiences using the GA4 data import. Let me know what’s working, what’s not, and what you’re excited to explore next.
As always, if you need help or have questions about this implementation, please contact us. We’d be happy to help you make the most of this game-changing feature.
