User event data
Auxia's typical engagement removes all the heavy lifting that comes with the data integration. Your teams are only required to share your relevant tables and our internal deployment teams will do all the transformation required to ingest this data into Auxia's system.
Background
Event data refers to records of actions or occurrences, often captured as individual events, with details like the type of action, the time it happened, the user or system involved, and other relevant metadata. These events can come from various sources, such as visitor interactions across your web and mobile applications.
Each time an event occurs on your website or mobile app, it's typically stored as a entry into your analytics database or data warehouse. This entry corresponds to a single row that captures all the specific details of the event, contributing to a complete set of clickstream data.
Here is an example of what that able might look like:
7f8a3c9d-2e4b-48ab
2024-09-01T10:23:45Z
page_viewed
john.doe@auxia.com
Website
9c4d21f7-1f2a-4a19-9b23
2024-09-01T11:45:32Z
added_to_cart
jane.smith@auxia.com
Mobile App
c7b43d2e-a1c5-4e6d-bd41
2024-09-01T12:15:20Z
purchased
tommy.bahama@auxia.com
Website
How Auxia uses event data
Event data provides a rich source of information that can be transformed to help Auxia's models understand patterns in user behavior, predict outcomes, and power your personalization initiatives. Once events are ingested into Auxia, they are used to create aggregations which are relevant for:
Events generated on the client are ingested in the Auxia's system to create aggregations (e.g. number of logins per week, products viewed last month, etc) on top of them, which are then used for:
Qualification Criteria: Define rules to determine which users should receive a treatment and which should not.
Content Personalization: Tailor treatment content by dynamically inserting specific attributes into the messaging.
Enhanced Treatment Selection: Use user attributes to guide the model in selecting the most appropriate treatment for the user.
Machine Learning Features: Features are measurable characteristics of data that are used by the model to make predictions. Think of these as the input variables that help the model understand and learn patterns in the data.
How to structure your events for ingestion
The event data that's ingested into Auxia has the following structure:
After the event data is shared with Auxia, it's transferred to a unified event schema, demonstrated below:
Last updated