Flex Filters

What if you could identify fans that bought more than 5 tickets in the last 90 days against a specific away team? Thanks to Flex Filters this is now possible! Read on to learn more about it.

Flex Filters gives you the flexibility to create robust fan audiences using signals across your users and transactions datasets. What normally would have required an analyst to manually identify the right tables and write a complex join query can now be done in just a few clicks in the app! Let me show you how step by step.

Before you begin

Connect your BigQuery instance to GrowthLoop Audience Platform. Learn how to here: Connect to BigQuery

Instructions

Navigate to the Datasets page.

Select Add Dataset.

Select Transactions to add your transactions dataset which contain fan purchase details.

Choose your Source. If your transactions table lives in the same data source as your users table, select the existing one. If not, select New Source at the bottom.

Configure your Transactions table. Specify the dataset, table, unique ID, and the key to join with your users table.

Click Save once your dataset is properly configured.

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Now let's create your fan audience using the users and transactions dataset. Go to the Audiences page and click on New Audience.

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Let's start off with the Users filter block and filter only to fans who have purchased a ticket in the last 90 days.

Click Add Filters.

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Search for the signal that captures when your fans last purchased. Select it and click Continue.

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Now let's apply the signal. Set the operator to in last count days and input 90.

Finally let's apply signals from the Transactions block and filter to fans who purchased more than 5 tickets against a specific away team.

Click Add Filters.

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Search for the signal that captures the away team for previous games. Select it and click Continue.

Apply the signal by selecting a specific away team such as the Chicago Cubs.

Specify the Transactions Count as 5.

Lastly, give your audience a name and click Save!