Guides

Flex Filters

What if you could identify fans that bought more than 5 tickets in the last 90 days? 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.

Prerequisites

Instructions

  1. From the homepage, Click Create Audience.
Create an audience. Create Audience: Open the audience creation flow.
  1. Select a Dataset that has already been set up.
Select the Dataset Group. Dataset Group: Select the appropriate configured group.
  1. Select an option for how you'd like to create an audience. (For more info on how to Create an Audience)
Choose how to build the audience. Start with the basics: Open the standard audience-building template.
  1. We've selected "Start with the basics" which will show you this template.
Find the related-data section. Other Data: Add the related dataset you want to filter.
  1. Under "Other Data", select an Event or Transaction dataset from the dropdown list.
Add the transaction dataset. Dataset picker: Select transactions or another related dataset.
  1. A new box is created in the canvas. This is the Flex Filter.
Use the Flex Filter. Aggregate condition: Set how many matching records a customer needs.; Row conditions: Limit which transactions contribute to the aggregate.
  1. The top section of the Flex Filter can be used to aggregate and filter the audience based on the number of events or transactions in the table.
Set the matching-record threshold. Comparison and count: Set the operator and the number of records.
  1. The aggregation can also be customized in this area by clicking "Count" and selecting a different option from the dropdown. Currently, options include "Count", "Sum", "Average" and "Count Distinct".
Choose the aggregation method. Aggregation menu: Select Count, Sum, Average, or Count Distinct.
  1. These are some examples of what those aggregations would look like.
Filter on a sum. Sum condition: This example requires transaction value to total more than 100.
Filter on an average. Average condition: This example requires average transaction value above 20.
Count distinct field values. Distinct-count condition: This example requires fewer than 10 unique customer IDs.
  1. Additional filters can be added to the Flex Filter box by typing at the bottom of the box.
Add a condition inside the Flex Filter. Add a filter: Select a field from the transaction dataset.
  1. So in the example of more than 5 transations in the last 90 days, the Flex Filter would look like this.
Combine a count with a time window. Record threshold: Require at least five matching transactions.; Time condition: Limit the records to the last 90 days.
  1. Lastly, give your audience a name and click Save!
Confirm the saved audience. Audience name: Use a name that explains the targeting rule.; Saved status: Confirm that your latest changes have been saved.

Updated 2 days ago

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