Using Regal Events in Reporting

A blueprint for building your reporting data model off of Regal events

In order to build reports and analytics off of Regal's real-time reporting events, you can use the data model we've built to power our In-App Reporting Dashboards as a blue print.

This table describes which raw events roll up into which reporting tables and what types of metrics or reports you can power off of them.

Regal Events

Tables

Table Descriptions

Example Metrics / Analysis

agent.activity.updated

Agent Activity

  • Unique record for each Agent activity period with dimensional columns like agent_email, activity_name, and start and end timestamps.
  • Composite primary key of agent_name and start_timestamp
  • Total and avg. time spent in each activity by agent
  • Can join to Aggregated Tasks table for tasks completed per available agent hour

task.created
task.canceled
call.completed
call.recording.available
sms.conversation.completed
task.reservation.accepted

Aggregated Tasks

  • Unique record for each task with dimensional columns like task id, disposition, recording link, handling_agent, etc. as well as lifecycle timestamps - created_at, canceled_at, completed_at, etc.
  • Unique identifier = Task Id
  • Completed tasks per day
  • Inbound answer rates
  • Outbound answer rate and connect rate (based on agent disposition and/or call duration)
  • Completed Tasks by disposition
  • Avg talk and handle time

task.reservation.created
task.reservation.accepted

Reservations

  • Unique record for each reservation
  • Unique identifier = reservation_id
  • Reservation acceptance rate by Agent
  • Speed to accept reservations
  • Avg. number of reservations per task to get it accepted

contact.created
contact.subscribed
contact.unsubscribed

  • Aggregated Contacts
  • User Rollup
  • Aggregated Contacts - single record for each contact that includes dimensional data (“what we know about who the user is”)
  • User Rollup - single record for each contact that includes activity metrics and flags (“we know what the user has done”)
  • Unique identifier = contact_phone
  • Count of contacts created by day
  • Count of contacts by source
  • % of contacts subscribed
  • Cohort analyses
  • Can join to Aggregated Tasks to get “speed to lead” metrics

scheduled.callback.requested

Scheduled Callback Requests

  • Unique record for each scheduled callback requests joined to the scheduled callback task created from that request
  • Composite primary key of agent_email and created_at
  • Count of scheduled callback requests by agent by hour of day
  • % of scheduled callback requests completed by the scheduling agent

sms.received
sms.sent
sms.undelivered

Messages

  • Unique record for each SMS sent or received
  • Dimensional data such as the direction (inbound/outbound), automated vs. manual, the number the sms is sent from and to, the content of the message, and the associated campaign (if applicable)
  • Primary key = message_id
  • Count of sms sent by campaign by day
  • SMS delivery rate
  • SMS response rate by campaign

contact.experiment.assigned

Experiment Assigned Events

Record for each assignment

Conversion rate by experiment variant

Track events sent to Regal

Important Events

  • Subset of track events that represent lifecycle/funnel milestones e.g., Lead Created, Lead Converted, Lead Canceled Subscription (the events are tagged and unique to each brand)
  • Composite primary key of event_name and created_at
  • Can join to aggregated contacts and aggregated tasks to get % of conversations that lead to a conversion, % of leads who convert

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