Regal

Overview

Regal is the customer engagement and communications platform used by Pair Team's Community Engagement Specialist (CES) team for patient outreach and enrollment. The platform manages the entire outreach lifecycle—from initial contact through enrollment conversion—and tracks all interactions with prospective patients.

Link to Regal application: https://app.regal.io/

Primary Use Cases

Regal data is primarily used for:

• Outreach tracking - Monitor calls, texts, and other contact attempts with prospective patients
• Enrollment channel analysis - Understand which marketing channels (e.g., Google Search Ads) drive patient acquisition
• Conversion analysis - Track the patient journey from initial contact to enrollment completion
• Agent activity monitoring - Analyze CES team performance and engagement patterns

Data Overview

Core Data Entities

The key data entities available from Regal include:

Entity	Description
Contacts	Contact profiles including demographics, phone numbers, and custom properties. Links to Arc charts via chart_id
Important Events	Key events in the patient journey (e.g., eligibility checks, attestation submissions)
Voice Events	Phone call records including duration, outcome, and timestamps
Agent Activity	CES team member actions and engagement patterns
Tasks	Follow-up tasks and workflow management for the CES team

Marketing Attribution

Regal captures marketing attribution data, including GCLID (Google Click ID) parameters from Google Ads campaigns. This enables analysis of which advertising channels and campaigns drive patient enrollment. Several dbt models (e.g., regal_eligibility_with_gclid, regal_submissions_with_gclid) join Regal contact data with Arc enrollment data to track conversion from ad click to enrollment.

Custom Properties

Regal stores flexible custom properties in JSONB fields, including:

• Contact ownership and assignment
• Marketing source and campaign information
• External identifiers and links to Arc
• Other engagement metadata

These properties are flattened into columns in the regal_contacts_properties mart model for easier analysis.

Data Models

The Regal data models in our dbt project (analytics-etl/models/regal/) follow our standard architecture:

• Staging models (stage_regal__*) - Raw data from Snowflake data share
• Intermediate models (int__regal_*) - Transformations and JSONB parsing
• Mart models (regal_*) - Final business-ready tables

Key mart models include:

• regal_contacts - Contact profiles and demographics
• regal_contacts_properties - Flattened custom properties
• regal_eligibility_with_gclid - Eligible patients with marketing attribution
• regal_submissions_with_gclid - Enrolled patients with marketing attribution
• regal_voice_events - Call history and outcomes
• regal_agent_activity - CES team engagement metrics
• regal_tasks - Follow-up task management

Data Access

To access Regal data in Snowflake, reach out to the Infrastructure team or Data team for access.

info

Regal data is primarily accessed through raw Snowflake tables or dbt-transformed mart models. Limited Regal data is currently available in Looker. For most analytical needs, you may need to query Snowflake directly or request that new models be added to Looker.