Average Resolution Time

Definition

Average Resolution Time (ART) measures the average amount of time it takes to fully resolve a customer issue or support ticket from the moment it is raised to when it is marked as resolved.

Business Context

Average Resolution Time (ART) is a vital service metric that reflects how quickly your team can resolve customer support issues, directly impacting user satisfaction, brand trust, and operational efficiency.

The relevance and interpretation of this metric shift depending on the model or product:

In SaaS, it applies to ticket resolution across email, chat, or in-app support
In consumer products, it reflects how well teams handle returns, inquiries, or complaints
In B2B environments, it surfaces support process gaps or product UX pain points

A shorter ART typically leads to higher NPS, better retention, and happier customers. A rising ART may indicate resource bottlenecks, knowledge base issues, or product confusion.

Segment by ticket type, priority level, or customer segment to improve SLAs and self-service options.

Average Resolution Time (ART) informs:

Strategic decisions, like investing in automation, staffing, or CX enhancements
Tactical actions, such as improving triage workflows or updating knowledge bases
Operational improvements, including ticket routing or response templates
Cross-functional alignment, by giving support, product, and PMM teams visibility into systemic product or service friction

Key Drivers

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Support Staffing and Coverage: Long wait times or limited availability drag down resolution time. Quick triage = faster fixes.
Knowledge Base Quality and Self-Serve Tools: Empowered users solve issues faster on their own. Gaps in documentation = more tickets and slower answers.
Issue Routing and Prioritization Logic: If tickets aren’t properly categorized or routed, they bounce between teams. Smart routing accelerates resolution.

Improvement Tactics & Quick Wins

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If resolution time is long, audit routing rules and triage process to reduce unnecessary handoffs.
Add or update self-serve content based on the top 5 most common support requests.
Run a test offering live chat for high-tier accounts, and track the reduction in time to first and final response.
Refine in-product support prompts to guide users to self-help content before opening tickets.
Partner with support leadership to add priority tagging based on customer segment or issue urgency.

Activities commonly tied to improving or operationalizing this KPI.

Activity	Description
Support Performance Optimization	Support Performance Optimization focuses on the continuous analysis, improvement, and alignment of customer support and success processes to drive revenue growth and enhance customer satisfaction. It improves performance by removing friction, testing changes, and scaling what works. Relevant KPIs include Average Resolution Time.
In-App Help Improvements	In-App Help Improvements is essential for driving adoption, reducing friction, and accelerating value realization. It improves performance by removing friction, testing changes, and scaling what works. Relevant KPIs include Average Resolution Time.
SLA Monitoring	SLA Monitoring focuses on proactively tracking, analyzing, and optimizing commitments and performance metrics established between cross-functional teams such as sales, marketing, and customer success. It turns signals into decisions, interventions, and measurable follow-up. Relevant KPIs include Average Resolution Time.

Required Datapoints

Ticket Creation Time: The timestamp when the ticket or inquiry is created.
Ticket Resolution Time: The timestamp when the ticket or inquiry is resolved.
Total Resolution Time: Sum of the time spent on resolving all tickets.
Total Resolved Tickets: The number of tickets closed during the measurement period.

Example

A SaaS company resolves 1,000 tickets in a month, with a total resolution time of 50,000 minutes:

Average Resolution Time = 50,000 / 1,000 = 50 minutes per ticket

Formula

\mathrm{Average\ Resolution\ Time} = \frac{\mathrm{Total\ Resolution\ Time\ Across\ All\ Tickets}}{\mathrm{Total\ Number\ of\ Resolved\ Tickets}}

Positive & Negative Influences

Negative Influences

Support Staffing and Coverage: Insufficient staffing or inadequate coverage leads to longer wait times, which directly increases the Average Resolution Time as issues take longer to be addressed and resolved.
Knowledge Base Quality and Self-Serve Tools: Poor quality or incomplete knowledge base and self-serve tools result in more tickets being raised and slower resolution times, as users are unable to resolve issues independently.
Issue Routing and Prioritization Logic: Inefficient routing and prioritization cause tickets to be misdirected or delayed, increasing the Average Resolution Time due to unnecessary back-and-forth between teams.
Complexity of Issues: More complex issues naturally take longer to resolve, thus increasing the Average Resolution Time.
System Downtime or Technical Issues: Frequent system downtimes or technical issues can delay the resolution process, leading to an increase in the Average Resolution Time.

Positive Influences

Support Staffing and Coverage: Adequate staffing and effective coverage ensure that issues are addressed promptly, reducing the Average Resolution Time by minimizing wait times.
Knowledge Base Quality and Self-Serve Tools: High-quality knowledge base and effective self-serve tools empower users to resolve issues independently, reducing the number of tickets and the Average Resolution Time.
Issue Routing and Prioritization Logic: Efficient routing and prioritization ensure that tickets are directed to the right teams quickly, reducing the Average Resolution Time by streamlining the resolution process.
Agent Training and Expertise: Well-trained and knowledgeable support agents can resolve issues more efficiently, thereby reducing the Average Resolution Time.
Automation and AI Tools: The use of automation and AI tools can expedite the resolution process by handling routine queries and directing complex issues to the appropriate agents, thus reducing the Average Resolution Time.

Funnel Stage & Type

AAARRR Funnel Stage

This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

Retention

Type

This KPI is classified as a lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Ownership

Primary Owner

This role is directly accountable for the KPI and is expected to drive progress and decisions around it.

Support Operations

Secondary Owners

These roles contribute directly to performance and typically partner on execution, reporting, or optimization.

Supporting Leading & Lagging Metrics

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.

First Contact Resolution: A lower First Contact Resolution (FCR) rate typically predicts longer Average Resolution Time (ART), as unresolved issues require follow-up and additional handling, directly increasing ART.
Ticket Volume: Spikes in Ticket Volume often precede increases in Average Resolution Time, since higher support demand strains resources and extends the time needed to resolve each issue.
Escalation Rate: A higher Escalation Rate signals more complex or difficult tickets requiring specialized attention, which forecasts longer resolution times and thus increases ART.
Rate of Escalation to Higher Support Tiers: An increased rate of tickets being escalated to higher support tiers generally leads to longer ART, as escalated tickets are more complex and time-consuming.
Resolution Time: Resolution Time is a direct precursor to ART; when individual resolution times rise, it signals that ART will also increase, providing an early warning.

Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.

Cost Per Ticket: Higher ART often results in increased Cost Per Ticket, as prolonged resolution processes consume more resources and labor, amplifying operational cost impact.
Customer Churn Rate: Extended ART can contribute to higher Customer Churn Rate, as slow support resolution frustrates customers and leads to attrition.
Customer Satisfaction Score: ART strongly influences Customer Satisfaction Score (CSAT); longer resolution times typically result in lower CSAT, quantifying the impact of ART on customer sentiment.
Net Revenue Churn: Poor ART can drive customer dissatisfaction and churn, which directly increases Net Revenue Churn, showing the broader financial impact of inefficient support.
Customer Downgrade Rate: Lengthy ART may prompt customers to downgrade their subscription if they perceive support as too slow, thus a higher ART often results in a higher Customer Downgrade Rate.

Data Model Definition

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

cube('SupportTickets', {
  sql: `SELECT * FROM support_tickets`,

  measures: {
    totalResolutionTime: {
      sql: `total_resolution_time`,
      type: 'sum',
      title: 'Total Resolution Time',
      description: 'Sum of the time spent on resolving all tickets.'
    },
    totalResolvedTickets: {
      sql: `id`,
      type: 'count',
      title: 'Total Resolved Tickets',
      description: 'The number of tickets closed during the measurement period.'
    },
    averageResolutionTime: {
      sql: `${totalResolutionTime} / NULLIF(${totalResolvedTickets}, 0)` ,
      type: 'number',
      title: 'Average Resolution Time',
      description: 'Average amount of time it takes to fully resolve a customer issue or support ticket.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true,
      title: 'Ticket ID',
      description: 'Unique identifier for each support ticket.'
    },
    ticketCreationTime: {
      sql: `ticket_creation_time`,
      type: 'time',
      title: 'Ticket Creation Time',
      description: 'The timestamp when the ticket or inquiry is created.'
    },
    ticketResolutionTime: {
      sql: `ticket_resolution_time`,
      type: 'time',
      title: 'Ticket Resolution Time',
      description: 'The timestamp when the ticket or inquiry is resolved.'
    }
  }
});

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema