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Overview
Step 1. Create an input
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Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
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Step 9. Test your local app
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Step 1. Create an input
Step 2. Create an output
Step 3. Verify output
Introduction
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Create an Input
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Aliasing
A calculated attribute (CA) measure users’ aggregate behaviors, and store this information in new user attributes, such as customer lifetime value, total games played or content watched, or the last product viewed. You define a calculated attribute in mParticle and, once activated, they are computed automatically over time by using the raw data stream of events and user information. Once you’ve created calculated attributes, you can use them as segmentation criteria in Audiences, Profile API, or connect them downstream to any of your tools. This is incredibly powerful, and can be set up with a few button clicks–no SQL, no pipeline management.
You can define calculated attributes to track almost anything on an individual user, from counting the number of logins in the last 30 days or knowing the last product category viewed, to more complex calculations like the customer’s average order revenue or the most frequent purchase.
Calculated attributes provide value in many ways:
The following video explains how calculated attributes help you quickly generate customer insights without needing any developer resources:
A calculated attribute contains the following elements that you define:
Calculated attributes are defined and calculated per workspace; calculations use data available within the same workspace where they are defined. You can create calculated attributes with the same name and functionality in multiple workspaces.
After you activate a calculated attribute, it initializes using existing data in the mParticle CDP along with any seeded value that was sent using Seeding API. Depending on the date range selected in the calculated attribute definition, this can take several hours. After initialization, calculated attributes continue to recalculate with new data.
Calculations are either synchronous (sent with the batch of data being processed) or asynchronous (sent with the next batch):
We currently support 13 calculations organized into four categories:
For a list of calculations and details about each, see Calculated Attributes Reference.
For an overview of how to use calculation categories, view the following video:
Calculated attributes can apply to a date range that you choose:
Since: limit calculations to the period of a specified start date to now.
Note that above the date range selection drop-downs, the UI displays the date that data was first ingested into mParticle. You can choose a date earlier than the first date that data was ingested, however, mParticle only calculates as of the earliest ingestion date.
After selecting an event, you can add conditions to the attribute in order to more precisely define results. For example, a retailer creating a calculated attribute might use the Contains operator with a Count attribute to count only the purchases that contain “Sock” in the product name. For a complete list of operators for the four categories of attributes (Count, Aggregation, Occurrence, and List), see Conditions.
Seeding allows you to pass in historic values for calculated attributes that mParticle builds on as new data arrives, without passing in the raw events. Seeding allows you to transition from your own calculations to mParticle calculation. For example, you may have data from outside mParticle about the last four months’ bookings. You can create a calculated attribute, then send the seed data to mParticle using the Calculated Attributes Seeding API. mParticle then combines your seed data and live data so there’s no interruption.
You can seed calculated attributes in both draft (recommended) and active states; the calculated attribute must exist before you can seed it.
Seeding requires two pieces of information:
Note the following calculated attribute behavior:
If a partner supports user attribute forwarding, you can forward calculated attributes in an audience integration alongside user attributes. Different partners have implemented user attribute forwarding in different ways.
For example, Salesforce uses a separate data extension while Google BigQuery uses the configuration setting Send User Attributes.
To walk through several different scenarios for using calculated attributes, download the Calculated Attributes Use Case Guide.
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