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Support and Service Use Case

A guide to implementing Auto Insights for a support and service use case, including data structure and sample questions.

Problem Statement

Support and service teams would typically want to manage their team workloads; which teams need more manpower, which teams are always having a long case duration, and which types of cases can be reduced with self-service knowledge base articles.

Typically these insights would be required to be monitored on a daily or weekly basis, with some insights raised at a monthly management meeting for decision-making.

An Example of Support and Service Data Structure

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Call Center:


Service Ticket Desk:



A segment is a qualitative value, like names or categories. Here are some of the typical segments we find in support and service data:

  • Case attributes: Case Resolution, Case Type, Outstanding Days Bracket, Time of Day, Priority, etc.

  • Team attributes: Team, Department, Agent, etc.

  • Other attributes: Channel, etc.


A measure is a quantitative, numeric value. Here are some of the typical measures we find in support & service data:

  • Case Metrics: Case Duration, Closed/Open/Reopen Count, Escalation Count, Outstanding Days, Interaction Duration

  • Team Metrics: Average Case per Team, Average Case Duration per Team, Number of Case

What Sort of Insights Can Auto Insights Help Me Uncover?

Identify Patterns

  • Number of Cases against products this month compared to last month

  • Outstanding Days by Case Type this week

  • Number of Cases filtered for Open Status by Case Type yesterday

Explore Team Performances

  • Total Outstanding Days by Teams this week

  • Number of Cases comparing Outstanding Days bracket

  • Number of Cases specific for Complaints by Teams this week

  • Reopen Count by Teams this month

  • Escalation Count by Teams this month

  • Average Case per Team this week