Overcome LinkedIn Ads limit for 18 metrics in a single report

LinkedIn Ads allows you to select up to 20 metrics in 1 report API request. You may see that Coupler.io allows you to select only 18 😅. Reason behind that "date" and "dimension" (pivotValue) are counted in same limit with metrics and Coupler.io requests them always.

For those who need to analyze more metrics simultaneously, there's a practical workaround that involves using multiple sources and joining them effectively. Here's a step-by-step guide on how to do it.

Step 1: Add Your First Source with Dimensions and the First 18 Metrics

Start by setting up your initial data source. This source should include the dimensions you want to analyze, along with the first 18 metrics that are most critical for your report.

Step 2: Add a Second Source with the Same Dimensions and the Next Set of Metrics

Next, create a second data source that includes the samesettings (Report period, Split data by, Dimension) but focuses on the next set of metrics. This could be up to 10 additional metrics, depending on your needs. The key is to ensure that other importer settings are identical between the first and second sources, as this will be crucial for the joining process.

Add more sources using same approach if you need more metrics.

Step 3: Define the Source with More Available Data

Choose the data source that has more comprehensive or critical information to be your primary source. This will be the source that will lead in the joining process. You can do this by import source data into your destination (e.g. Google Sheets).

Step 4: Edit the Importer and Open the Transformation Step

Once your sources are set up and you defined your primary source, access the settings of the importer and navigate to the Transformation step. This is where you'll define how the data from your multiple sources will be combined. Click "Add transformation":

Step 5: Add a Join to Combine the Sources

In the Transformation step, select to Create Join.

This will allow you to merge the two data sources into a single dataset. Select the primary source (the one with more data) as your LEFT source in the join operation. This ensures that all records from this source will be retained in the final dataset, even if they don't have corresponding matches in the second source.

Step 6: Select All Dimensions as Join Keys

To accurately merge the data, select all the dimensions that are common across both sources as the join keys. This ensures that the data aligns correctly across both sets of metrics:

• Add join by "Account Id" if you have several Ad accounts selected.
• Add "Date" to join metrics properly by date.
• Add dimension Id (e.g. Campaign Id) based on what dimension you selected in the settings

(Note: we suggest to use IDs if possible instead of Names for data accuracy, as names can be same, but IDs are unique).

Step 7: Run the Importer and Review the Results

After setting up the join, run the importer to combine the data. Once the process is complete, review the results to ensure that all the metrics have been correctly imported and aligned. You should now see a report that includes all metrics from sources you defined, providing a more comprehensive view of your data.

Potential fragility of the solution

While this solution effectively circumvents the LinkedIn Ads limit on metrics, it does come with certain risks.

Main concern is the potential for data discrepancies or inaccuracies during the joining process. It may be due to next things:

• If primary source selected has less data than second source - it may lead to missed data from second source.
• If there are differences in selected dimensions - it may lead to extra data available for one of the source and differences in calculations.
• If the dimensions used as join keys are not perfectly aligned- it may lead to some rows not joined.
• If you selected not all dimensions on JOIN - it may lead to invalid joining and wrong final result.