Traits

The Q2 SMART system categorizes users based on their financial behavior or shared attributes. These categories are called traits and assist in building target audiences for a campaign.

View the list of available traits in the Trait store.

Traits expected values

Data type	Expected value	Example
boolean	true or false	The user has a checking account or user does not have a checking account
date	date up to 365 days	The user completed a transaction for their checking account in the last 120 days
float	fraction-based numbers	The user has two checking accounts with the FI; the user's total checking account balance is $5k
enum	predefined value	The user's preferred language in UUX is Spanish
string	alphanumeric results	SMART identifies the user in the ZIP Code trait value 90210

Insight traits

Q2 SMART uses data model machine learning to create its insight traits like Recommended Audiences. This means SMART looks at how different elements of data relate to each other to make highly probable assumptions about users. Sometimes, additional logic is placed around these assumptions to advance accuracy. For example, traits based on an RFM score The RFM score is calculated using the average of the normalized rank of recency, frequency, and monetary values. are complex traits.

Detailed information on insight trait data makeup can be found within the Q2 SMART Product Documentation in the Q2 Customer Portal.