KPI Tree

Intercom Metric

Customer Support

Agent Utilisation Rate = Active Handling Time / Total Available Time × 100

Agent Utilisation Rate measures the percentage of an agent's available time spent actively handling conversations versus idle or performing administrative tasks. It helps balance workload to prevent both burnout from over-utilisation and waste from under-utilisation.

Full guide: definition, formula, and benchmarks
IntercomCustomer Support

Agent Utilisation Rate

Agent Utilisation Rate measures the percentage of an agent's available time spent actively handling conversations versus idle or performing administrative tasks. It helps balance workload to prevent both burnout from over-utilisation and waste from under-utilisation.

How to calculate agent utilisation rate

Agent Utilisation Rate = Active Handling Time / Total Available Time × 100

Why agent utilisation rate matters for Intercom users

Over-utilised agents burn out quickly, leading to declining quality, increased absenteeism, and higher turnover. Under-utilised agents represent wasted staffing investment. Finding the sweet spot is critical for sustainable support operations.

For Intercom teams, utilisation data combined with conversation volume patterns enables smarter scheduling decisions. It reveals whether dips in CSAT correlate with periods of high utilisation, providing evidence for staffing requests.

Understand and act on agent utilisation rate with KPI Tree

Extract agent availability and conversation handling data from Intercom into your warehouse. Model utilisation rate in KPI Tree and link it to agent performance and team workload distribution.

Assign RACI ownership to the support operations manager and set threshold alerts when utilisation exceeds your target ceiling, signalling the need for additional staffing or automation.

Get started with your Intercom data

Query using MCP
MCP

Pull metrics from Intercom directly through the Model Context Protocol.

Data Warehouse
SnowflakeBigQueryDatabricksRedshift

Connect your existing warehouse where Intercom data already lands.

Professional Services
FivetranSnowflakedbt

Our professional services team can build you turn-key AI foundations in a matter of weeks. Data warehouse on Snowflake/BigQuery, ELT with Fivetran, all modelled in dbt with a semantic layer.

Related Intercom metrics

Agent Performance Analysis

Customer Support

Metric Definition

Agent Performance Analysis evaluates individual support agents across multiple dimensions including response time, resolution rate, customer satisfaction scores, and conversation volume handled. It provides a balanced scorecard that avoids over-indexing on speed at the expense of quality.

View metric

Team Workload Distribution

Customer Support

Metric Definition

Team Workload Distribution measures how conversations are distributed across teams and individual agents within Intercom. It highlights imbalances where some agents are overloaded while others are under-utilised, enabling fairer distribution and more sustainable working conditions.

View metric

Conversation Volume

Customer Support

Metric Definition

Conversation Volume = Count of New Conversations in Period

Conversation Volume measures the total number of new support conversations initiated within a given period. It is the foundational capacity metric for support operations, driving staffing decisions, budget planning, and automation investment. Sudden volume changes often correlate with product releases, incidents, or seasonal patterns.

View metric

Peak Support Hours Analysis

Customer Support

Metric Definition

Peak Support Hours Analysis identifies the times of day, days of the week, and seasonal periods when support conversation volume is highest. It provides the demand signal needed for optimal agent scheduling and helps set customer expectations about response times during different periods.

View metric

Explore agent utilisation rate across integrations

Empower your team to understand and act on Intercom data

Map what drives your metrics, measure progress at any grain, prove what works statistically, and deliver personalised action plans to every team member.

Experience That Matters

Built by a team that's been in your shoes

Our team brings deep experience from leading Data, Growth and People teams at some of the fastest growing scaleups in Europe through to IPO and beyond. We've faced the same challenges you're facing now.

Checkout.com
Planet
UK Government
Travelex
BT
Sainsbury's
Goldman Sachs
Dojo
Redpin
Farfetch
Just Eat for Business