{"componentChunkName":"component---src-templates-markdown-template-jsx","path":"/guides/data-warehouse/composable-audiences/data-models/event-data-models/","result":{"data":{"pageMetadata":{"title":"Create an Event Data Model","path":"/guides/data-warehouse/composable-audiences/data-models/event-data-models/","sourceCode":null,"metadataParent":{"title":"Data Models","path":"/guides/data-warehouse/composable-audiences/data-models/","sourceCode":null,"hasVersionPicker":null,"metadataChildren":[{"title":"Data Models Overview","path":"/guides/data-warehouse/composable-audiences/data-models/overview/","order":1,"label":null},{"title":"Create a User Data Model","path":"/guides/data-warehouse/composable-audiences/data-models/user-data-models/","order":2,"label":null},{"title":"Create an Event Data Model","path":"/guides/data-warehouse/composable-audiences/data-models/event-data-models/","order":3,"label":null},{"title":"Create a Generic Data Model","path":"/guides/data-warehouse/composable-audiences/data-models/generic-data-models/","order":4,"label":null}],"metadataParent":{"title":"Composable Audiences","path":"/guides/data-warehouse/composable-audiences/","metadataChildren":[{"title":"Warehouse Setup Overview","path":"/guides/data-warehouse/composable-audiences/overview/","order":0.5,"label":null},{"title":"Connections","path":"/guides/data-warehouse/composable-audiences/connections/","order":1,"label":null},{"title":"Data Models","path":"/guides/data-warehouse/composable-audiences/data-models/","order":2,"label":null}],"metadataParent":{"title":"Data Warehouse","path":"/guides/data-warehouse/","metadataChildren":[{"title":"Composable Audiences","path":"/guides/data-warehouse/composable-audiences/","order":1,"label":null},{"title":"Warehouse Sync","path":"/guides/data-warehouse/warehouse-sync/","order":4,"label":null}],"metadataParent":{"title":"Guides","path":"/guides/","metadataChildren":[{"title":"Platform Overview","path":"/guides/platform-overview/","order":0.5,"label":null},{"title":"Getting Started","path":"/guides/getting-started/","order":1,"label":null},{"title":"mParticle Agent","path":"/guides/mparticle-agent/","order":1.5,"label":null},{"title":"Oversight","path":"/guides/oversight/","order":2,"label":null},{"title":"Data Platform","path":"/guides/data-platform/","order":3,"label":null},{"title":"Data Warehouse","path":"/guides/data-warehouse/","order":4,"label":null},{"title":"Customer 360","path":"/guides/customer-360/","order":5,"label":null},{"title":"Analytics","path":"/guides/analytics/","order":6,"label":null},{"title":"Audiences","path":"/guides/audiences/","order":7,"label":null},{"title":"Settings","path":"/guides/settings/","order":8,"label":null},{"title":"Troubleshooting mParticle","path":"/guides/troubleshooting/","order":10,"label":null},{"title":"Default Service Limits","path":"/guides/default-service-limits/","order":11,"label":null},{"title":"Glossary","path":"/guides/glossary/","order":12,"label":null}],"metadataParent":null}}}}},"markdownRemark":{"html":"

Event Data Models capture timestamped user actions (such as logins, purchases, or conversions) that can be used to refine or enrich your audience definitions. These models pull event-level data from your cloud data warehouse and link back to a User Data Model using a shared identifier (for example, user_id).

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This makes it possible to build audiences not just on who your users are, but also on what they do. For example, you might want to create an audience of users who made more than three purchases in the past 90 days, or subscribers who logged in fewer than two times last month.

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\n \n \n \n \n \n \n \n Example

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Here is a simple SQL query you might use to define an event data model for purchases:

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SELECT\n  user_id,\n  order_id AS event_id,\n  purchase_date AS event_time,\n  total_value\nFROM ecommerce.orders
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With this model in place, you could build an audience such as “Gold loyalty tier users who made more than 3 purchases in the last 90 days.”

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This example focuses on what the user does. When combined with a User Data Model (who the user is) and a Generic Data Model (extra context), you can build more precise and meaningful segments.

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\n \n \n \n \n \n \n \n To create an event data model

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\n \n \n \n \n \n \n \n 1. Enter your query

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    \n
  1. Navigate to Data Warehouse > Data Models.
    2. \n
  2. Click + Add Data Model.
    3. \n
  3. Select Event Data Model.
    4. \n
  4. Select a warehouse connection using the Data Connection dropdown menu.
    5. \n
  5. Under SQL Query, enter the SQL query you want to execute against your warehouse. Your query should return timestamped actions associated with user identifiers.
    6. \n
  6. Click Run Query.
    7. \n
  7. Click Next Step to continue.
    8. \n
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\n \n \n \n \n \n \n \n 2. Enter your configuration details

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    \n
  1. Enter a descriptive name for your data model.
    2. \n
  2. Select a primary ID column (such as event_id) retrieved from your SQL query to uniquely identify each event.
    3. \n
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    \n
  1. Select a timestamp column that represents when the event occurred.
    2. \n
  2. Include a user identifier (such as user_id) to denote who performed the event.
    3. \n
  3. Click Save Data Model to continue.
    4. \n
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\n \n \n \n \n \n \n \n 3. Enter your column settings (optional)

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    \n
  1. Use the Enabled toggle to disable or re-enable a column.
    2. \n
  2. Review the data type mParticle detects for each column. Update if needed.
    3. \n
  3. Enable Autocomplete to see cached values for up to 7 days when building audiences.
    4. \n
  4. Use the Is Case Sensitive (Yes/No) setting to control casing for autocomplete.
    5. \n
\n

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Click Save Data Model to continue.

\n","frontmatter":{"title":"Create an Event Data Model","order":3,"seoTitle":null,"seoDescription":null},"parent":{"__typename":"File","modifiedTime":"October 12, 2026"}}},"pageContext":{"slug":"/guides/data-warehouse/composable-audiences/data-models/event-data-models/"}},"staticQueryHashes":["2950460867"]}