Constituents, segments, & properties

This article explains how constituents, properties, and segments work within the CollegeVine platform. Together, these concepts shape how your AI agents understand your audiences, determine who they can engage with, and ensure interactions remain accurate and consistent across all responsibilities.
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You’ll learn what each term means, how they relate to one another, and how to use them to organize outreach, campaigns, and operational workflows.

Constituents are the individuals your agent communicates with or supports through the CollegeVine platform. This group might include prospective students, applicants, current students, alumni, and any other groups your team supports. Constituents serve as the foundation of your data model, and each record provides a single source of truth used across all your agents.​
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Properties are the individual data points your institution imports from systems like your CRM or SIS. These fields give agents the context they need to personalize communication, trigger the right responsibilities, and ensure outreach aligns with your institution’s rules and objectives.
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For example, you may choose to import a funnel stage property to indicate current admissions funnel status for a prospective student.
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A segment is a defined group of constituents who share common attributes or meet selected criteria. Responsibilities operate within a single segment at a time. This structure keeps agent behavior focused and ensures that responsibilities only engage with the audiences they are designed to support.
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Segments are highly flexible and can represent any audience you serve, such as:

Property-based segmentation offers precise control. You can define rules using fields such as:

As you add filters, the interface displays the number of constituents who match the criteria. This helps you confirm that the segment includes the right population before saving it.
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Each responsibility operates within a single segment so that its actions, communications, and decisions apply only to the intended population. This structure improves clarity, reduces cross-talk between workflows, and ensures that behavior is both measurable and consistent.