• Workflows – using the Union block

  Imagine you want to build an ad report, combining Google Ads and Facebook Ads. Here's where Conduit's Union block comes into play – a feature that helps to merge the data from the different data sources How It Works: Start by creating a Workflow, and pull data from Facebook and Google Ad accounts Add the Union block to your Workflow. Choose ‘Pull Data from Facebook Ads" as Dataset 1 and ‘Pull Data from Google Ads’ as Dataset 2. Add additional datasets, if needed (https://stFew readers

• Workflows – using the Transpose block

  Let's say we have metric sums for specific time periods, and each metric has its own column. Now, we want to transform each metric into a separate row. To achieve this, we can use the Transpose block, which switches columns to rows and vice versa. Here is how our Workflow layout setup would look like Let's ruFew readers

• Workflows – adding new columns

  Conduit's Workflows functionality enables users to integrate additional columns into their existing datasets, by several different ways Add Metric The first way is to add a new column with a new metric with a custom formula to the existing dataset. To do so, just drag the Add Metric block on your workflow's canvas Now, let's apply a custom formula to the block and add the 'Save to Google Sheet bFew readers

• Workflows – creating a weekly Ad Spend report

  Imagine you want to create a weekly report for tracking ad spend for different ad networks, listing them as rows Add a "Pull Data" block, selecting Facebook Ads as the data source and connect it with the Add Column block. Then, apply breakdown by Ad Network and the Spend (Cost) metric and the value as 'Facebook Ad Spend'. The result should look like this Then, repeat the same steps for Google AdsFew readers

• Workflows – using the Join by Key block

  The “Join by Key” block serves as the bridge between two datasets. Conduit processes each row in the primary dataset, seeking matches in the secondary dataset based on a specified key. Rows from the secondary dataset that don't find a match in the primary dataset are excluded from the final output. This feature could be useful, when you trying to merge related data from different sources, such as Ad networks Here's a practical example: Start a Workflow by pulling data from Facebook AdsFew readers