Ticket Volume and Trend Analysis: Understanding the Rhythm of Support Demand

In IT Service Management (ITSM), ticket volume and trend analysis is the heartbeat of your support operations. It’s about more than counting tickets — it’s about understanding patterns, predicting demand, and making informed decisions about staffing, processes, and priorities.

By regularly analysing how ticket volumes rise and fall over time, you gain the insights needed to match capacity with demand, identify unusual spikes or drops, and uncover hidden opportunities for automation, knowledge sharing, or service improvement.

In this post, we’ll explore what ticket volume and trend analysis involves, how to use it in practice, and how it supports better service management — with a practical example to bring it to life.

What Is Ticket Volume and Trend Analysis?

At its core, this is the process of tracking and evaluating the number of support requests (incidents, service requests, tasks) over time, identifying:

• How much work is coming in

• When it’s coming in (day/week/month trends)

• What type of work it is

• Where it's coming from (channels, services, clients)

• Who it’s impacting (users, departments, customers)

You’re looking for patterns that tell you something actionable — whether that’s increased pressure on a team, recurring issues, or seasonal trends.

Why It Matters in ITSM

In a well-functioning ITSM environment, demand and capacity planning go hand in hand. If you’re unaware of ticket trends, you risk falling behind when volumes spike or underutilising staff during quiet periods.

Trend analysis supports multiple practices, including:

• Service Desk – Forecasting incoming workload and adjusting coverage

• Incident & Request Management – Identifying high-volume issue types

• Problem Management – Spotting recurring issues suitable for root cause analysis

• Continual Improvement – Using data to drive proactive enhancements

• Capacity & Performance Management – Matching resourcing with actual demand

In short, ticket volume data helps you move from reactive to proactive support.

How to Analyse Ticket Volumes

Here are some common ways to analyse trends:

• Volume over time (daily, weekly, monthly)

• By source – Email, portal, phone, automation, chat

• By ticket type – Incidents vs. service requests vs. tasks

• By service or application – Where is support being consumed?

• By user group or location – Are certain teams creating more demand?

• By priority/severity – Is the load business-critical or routine?

Visual tools like heatmaps, line graphs, and ticket funnel dashboards help reveal where pressure is building — or where things are improving.

Practical Example: Uncovering a Hidden Spike with Trend Analysis

A support team at a government agency noticed a dip in team morale and rising backlog — but ticket volumes appeared "steady" at first glance.

After a deeper analysis, they spotted that:

• Ticket volumes had spiked by 40% on Mondays, due to system refreshes that caused login and access iss

• ues.

• A recurring issue with one legacy application was generating over 100 tickets per month from just one department.

• Usage of the self-service portal had dropped by half, increasing call and email traffic.

Armed with this insight, they took several steps:

• Scheduled extra coverage on Monday mornings to absorb peak demand

• Worked with the problem management team to address the root cause of the legacy app issue

• Updated knowledge base content and promoted self-service options more actively

Within eight weeks, average ticket volume had stabilised, and the Monday backlog dropped by 60%.

How to Use Ticket Trends to Improve Service

• Forecast peaks and plan coverage: Adjust rosters or shift patterns to align with demand patterns.

• Target high-volume services for improvement: Automate common tasks or update knowledge base articles.

• Track ticket deflection: Monitor self-service adoption and how it affects volume.

• Inform budget and resource planning: Justify hiring, training, or tooling investments with real data.

• Trigger problem or change management: Repetitive incidents often reveal deeper issues worth resolving permanently.

Final Thought

Ticket volume and trend analysis isn’t just about dashboards and graphs — it’s about anticipating need, optimising resources, and improving the end-to-end service experience.

When combined with backlog health, SLA performance, and capacity data, it forms a powerful foundation for data-driven, proactive ITSM.