Percentage of Incidents due to Changes KPI

Percentage of Incidents due to Changes is a critical KPI that highlights the impact of change management on operational efficiency.

A high percentage indicates potential disruptions in service delivery, which can adversely affect customer satisfaction and overall business health.

Conversely, a low percentage suggests effective change implementation, leading to smoother operations and enhanced strategic alignment.

Organizations that track this metric can better forecast risks and improve their change processes, ultimately driving better business outcomes.

By focusing on this KPI, executives can make data-driven decisions that enhance performance and support long-term growth.

How Percentage of Incidents due to Changes Connects to Your Strategy

This KPI belongs to two KPI groups. Its home group is IT Service Management, where it ranks thirtieth of forty-five, a mid-table position. It also appears in the ISO 20000 KPI group, where it ranks thirty-third of fifty. In both it plays a supporting role behind the group leaders rather than a headline one.

In the IT Service Management group, the top-priority co-metrics are Incident Resolution Time, Mean Time to Restore Service, and Service Availability, with First Call Resolution Rate and Percentage of SLA Compliance close behind. In the ISO 20000 group the order shifts toward Incident Resolution Rate, First Contact Resolution Rate, Service Availability, and Mean Time to Repair. Across both groups the metric that pulls directly against this one is Change Failure Rate. Both measure how much trouble change introduces, but they point in opposite directions on velocity: a team chasing a lower share of incidents caused by change can simply slow down and ship fewer changes, which starves the delivery pipeline. Read this metric next to change throughput so caution does not masquerade as quality.

The metric sits on the internal-process perspective of the balanced scorecard, making it a leading signal of change-management discipline: it moves before availability and satisfaction do, warning that your release process is injecting the very incidents your restore-time metrics then have to clean up.

Measuring Percentage of Incidents due to Changes in Practice

The formula is the share of total incidents that were caused by changes: change-caused incidents over all incidents. Both halves are harder to pin down than they look. The numerator depends on linking each incident back to a change record, which means your incident data in the service desk has to carry a reliable reference to the change management database. If that link is filled in by hand after the fact, it will be sparse and biased toward the obvious cases, and your numerator will undercount. The denominator depends on what you count as an incident at all: auto-resolved alerts, duplicates, and reopened tickets each move the number.

The fork to settle first is attribution rigor. "Caused by a change" can mean a formal root-cause analysis confirmed the change, or it can mean someone noticed a change went out nearby and assumed a connection. Those produce very different shares from the same raw data, so decide the standard, apply it consistently, and record which one you used. The second fork is the denominator: all incidents versus major incidents only. Both are legitimate, but they answer different questions and are not comparable to each other.

Segment before you read the result. Change-caused share differs by change type (standard versus normal versus emergency), by service, and by team, and a blended figure hides where the risk actually concentrates. Watch two traps: emergency changes made during a live incident can get logged as the cause of that incident, inflating the ratio through a timing artifact, and a reporting window that lags the change calendar will miss incidents that surface days after a release, understating the true share.

Common Pitfalls

Many organizations overlook the importance of thorough impact assessments before implementing changes, leading to unforeseen incidents.

• Failing to involve key stakeholders can result in resistance and miscommunication. This often leads to poorly executed changes that disrupt workflows and create confusion among teams.
• Neglecting to document changes and their outcomes can obscure lessons learned. Without proper records, teams may repeat mistakes, increasing the likelihood of incidents in future changes.
• Overlooking training and support for staff during transitions can lead to errors. Employees may struggle to adapt to new processes, resulting in increased incidents and decreased morale.
• Ignoring feedback from frontline employees can prevent organizations from identifying potential issues. Employees often have valuable insights that can improve change implementation and reduce incident rates.

Improvement Levers

Enhancing the management of changes requires a proactive approach to minimize incidents and improve overall performance.

• Establish a robust change management framework that includes clear guidelines and processes. This ensures that all changes are assessed for potential impacts before implementation.
• Implement regular training sessions for employees on new processes and systems. Well-informed staff are less likely to make errors that lead to incidents.
• Utilize a centralized reporting dashboard to track incidents related to changes in real-time. This allows for immediate action and helps identify patterns that require attention.
• Encourage open communication channels for employees to report issues or concerns related to changes. This feedback loop can help organizations address problems before they escalate.

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Percentage of Incidents due to Changes Benchmarks

We have 2 relevant benchmarks in our benchmarks database.

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Reading the Benchmarks for Percentage of Incidents due to Changes

The two tracked sources describe related but non-identical measurements, so a customer cannot treat their figures as one benchmark. Uptime Institute reports on networking and connectivity outages and how many are attributed to change, drawn from data-center and IT operations, which is an outage-cause view. ServiceNow looks at major-incident causation across industries using process mining. Outage-cause attribution and change-caused major incidents are not the same thing: they differ in denominator (all outages of a type versus major incidents), in population, and in what event even qualifies. Before trusting either as a stand-in for your own number, verify two things. First, how "caused by a change" is attributed, since root-cause rigor varies widely and a loose attribution inflates the share while a strict one deflates it. Second, whether the denominator is all incidents or only major ones, because a share computed over major incidents alone will read very differently from one computed over every ticket. Match both to your own construct before any comparison.

OKRs That Use Percentage of Incidents due to Changes

In the IT Service Management KPI group, this metric supports the objective ensure uninterrupted IT services by minimizing downtime and disruptions. That objective already names Change Failure Rate as a key result tied to rigorous change controls, and the share of incidents caused by changes is the natural companion read: a team can commit to driving that share down over the period as evidence that tighter change controls are actually preventing disruption rather than just passing more reviews. Frame it directionally, a lower share of change-caused incidents, and keep any target as an illustrative goal the team sets.

In the ISO 20000 KPI group, it ladders to drive secure and effective change management to support continuous service improvement. That objective centers on Change Success Rate and a larger share of proactive changes, and this metric is the failure-side counterpart: as proactive, well-tested changes grow, the share of incidents traceable to change should fall. Set it as a directional key result under that objective so the team can show change management maturing from reactive to preventive, without copying any specific from-and-to figures as if they were benchmarks.

See OKR Examples for IT Service Management

KPI Categories

This KPI is associated with the following categories and industries in our KPI database: