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Advertiser Analytics Data Studio Connector

1. What is a DataStudio Connector?

Google Data Studio is a powerful platform for creating interactive dashboards and reports, sourcing data from various platforms and data sources. A acts as the bridge, allowing users to import data from their desired platforms directly into Data Studio. By using connectors, users can seamlessly integrate data, customize their reports, and visualize insights without the need for manual data exports or imports.

2. Introduction to ad:personam Advertiser Analytics

The ad:personam Analytics is a robust tool designed for advertisers using the ad:personam Self Serve DSP platform. It empowers users by allowing them to fetch campaign data from one or more advertisers associated with their account. This means you can gain insights, analyze performance, and optimize your advertising strategies, all from one place.

3. Fetching Campaign Data for DataStudio

To utilize the ad:personam Self Serve DSP DataStudio Connector and pull your campaign data into Data Studio, follow these steps:

a. Navigate to the reporting section in your ad:personam .

b. Look for and select the “Build your own dashboard” option. Please note that accessing this feature requires an active .

c. You will be prompted to enter your ad:personam username. Additionally, list the Advertiser IDs you wish to fetch data from. If you're fetching from multiple advertisers, ensure IDs are separated by a comma.

d. For those looking for a starting point or a recommended layout, click on “Use report template for new reports“. This provides a basic report structure which can be customized further as per your needs.

e. Once you've input the necessary details, the connection will be established, and you'll be presented with the data source from ad:personam.

f. To generate a report based on this data, simply click on “Create Report”.

4. Additional Data Source Recommendations

Calculated Data Fields: We highly suggest adding calculated data fields to your data source. These fields can be instrumental in deriving specific insights from your data, allowing for a more nuanced understanding of your advertising .

Here's a list of calculated fields that we suggest to create:

To effectively make use of this list and integrate these calculated fields, it's important to understand the structure:

  • Field Name: The first line signifies the name of the calculated data field. This name represents how the metric will appear in your report.
  • Formula: The second line is the formula for the calculation. This dictates how the metric is computed based on existing data fields.
  • Field Type: The third line denotes the format in which the calculated result will be presented in the report, such as a percentage, number, or text.


  • SUM(clicks) / SUM(imps) 
  • percent


  • SUM(video_completions) / SUM(video_starts)
  • percent


  • SUM(total_convs) / SUM(imps)
  • percent

  • SUM(total_cost_buying_currency) / SUM(total_convs)

  • SUM(total_cost_buying_currency) / SUM(clicks)

  • SUM(total_cost_buying_currency)/ SUM(video_completions)

  • SUM(total_cost_buying_currency) / SUM(imps) * 1000


  • SUM(imps_viewed) / SUM(view_measured_imps)
  • Percent

Applying Markup to your total_cost

example :
gross_cost SUM(total_cost_buying_currency)/0.85

The division by 0.85 in the formula is a strategic approach to apply a 15% markup. Here's how it works:

  1. Basic Markup Calculation: If you have an expense of $100 and wish to add a 15% markup, you'd end up with $115. This is achieved by multiplying $100 by 1.15, where 1.15 is the sum of the original value (1 or 100%) and the 15% markup.
  2. Formula's Division Approach: When the formula divides by 0.85, it's an inversion of the markup process. The 0.85 derives from subtracting the 15% markup from 100% (or 1 in decimal form). By dividing the total cost by 0.85, it equivalently multiplies the cost by 1.15, thus adding the 15% markup.

Additional Example with a 30% Markup:

  • For a 30% markup, you'd multiply the original cost by 1.30.
  • In the formula's logic, you'd divide by 0.70 (since 1 – 0.30 = 0.70). Therefore, dividing by 0.70 is analogous to multiplying by 1.30, effectively adding a 30% markup to the cost.

By grasping this inversion technique, you can easily adjust the formula to apply different markup percentages as needed.

Blending Data Sources: Google Data Studio offers a feature known as ‘data blending'. This lets you combine data from different sources, like , into a same chart or table. By blending data from the ad:personam connector with other sources, you can gain comprehensive insights. For example, by combining ad campaign data with website analytics, you can better understand the effectiveness of your ad campaigns in driving website activity.

5. Conclusion

The ad:personam Self Serve DSP DataStudio Connector provides an efficient way to visualize and analyze your advertising data within Google Data Studio. By leveraging this connector and following the steps outlined above, advertisers can derive actionable insights, optimize campaigns, and enhance their overall advertising strategy.

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