Resolution Time¶

Definition¶

Resolution Time measures the amount of time it takes to resolve a customer issue or ticket from the moment it is raised to when it is marked as resolved. It tracks the speed and efficiency of support teams in addressing customer concerns.

Description¶

Resolution Time is a key indicator of support efficiency, operational readiness, and customer satisfaction, reflecting how quickly your team resolves customer issues—from first contact to full closure.

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

In SaaS, it could mean resolving product bugs or usability issues via support tickets
In consumer apps, it often tracks chatbot or Tier 1 support interaction duration
In enterprise, it may include technical support escalations or SLA commitments

A shorter resolution time typically signals efficient workflows, good tooling, and empowered agents, while a longer time might point to process bottlenecks, resource gaps, or complex issue types. By segmenting by issue type, support tier, or customer segment, you can uncover where to streamline support operations and where to invest in better help content, training, or tools.

Resolution Time informs:

Strategic decisions, like tooling investment, SLA design, or self-service initiatives
Tactical actions, such as reallocating support resources during spikes
Operational improvements, including ticket triage, macros, and documentation gaps
Cross-functional alignment, across CS, product, and ops, to reduce friction and elevate customer experience

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 Team Coverage and Triage: The faster a ticket gets routed to the right person, the faster it gets solved.
Knowledge Base and Self-Service Availability: Great self-serve reduces ticket volume and clears agent bandwidth.
Product Complexity or Bug Frequency: More complex tools = longer fixes without proactive mitigation.

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 rising, implement ticket tagging by urgency and auto-route high-priority issues.
Add AI or chatbot support for FAQs and repetitive queries.
Run an audit on top ticket types and pre-build macros or help docs for common issues.
Refine SLA expectations and agent KPIs to include both speed and first-contact resolution.
Partner with product to reduce recurring support triggers (bugs, confusing flows).

Required Datapoints to calculate the metric
- Ticket Creation Time: The timestamp when the ticket or issue was raised.
- Resolution Time: The timestamp when the ticket or issue was marked as resolved.
- Total Tickets: The total number of resolved tickets within the measured period.
Example to show how the metric is derived

A SaaS company resolves 500 tickets in a month with a total resolution time of 25,000 minutes:
- Average Resolution Time = 25,000 / 500 = 50 minutes

Formula¶

Formula

\[ \mathrm{Average\ Resolution\ Time} = \frac{\mathrm{Sum\ of\ All\ Resolution\ Times}}{\mathrm{Total\ Number\ of\ Resolved\ Tickets}} \]

Data Model Definition¶

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

Tickets

cube(`Tickets`, {
  sql: `SELECT * FROM tickets`,

  measures: {
    resolutionTime: {
      sql: `TIMESTAMPDIFF(SECOND, ${CUBE}.ticket_creation_time, ${CUBE}.resolution_time)`,
      type: `number`,
      title: `Resolution Time`,
      description: `The amount of time in seconds it takes to resolve a ticket from creation to resolution.`
    },
    totalTickets: {
      sql: `id`,
      type: `count`,
      title: `Total Tickets`,
      description: `The total number of resolved tickets within the measured period.`
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: `string`,
      primaryKey: true,
      title: `ID`,
      description: `The unique identifier for each ticket.`
    },
    ticketCreationTime: {
      sql: `ticket_creation_time`,
      type: `time`,
      title: `Ticket Creation Time`,
      description: `The timestamp when the ticket or issue was raised.`
    },
    resolutionTime: {
      sql: `resolution_time`,
      type: `time`,
      title: `Resolution Time`,
      description: `The timestamp when the ticket or issue was marked as 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

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Support Team Coverage and Triage: Insufficient coverage or poor triage processes can delay the assignment of tickets to the appropriate team members, increasing Resolution Time.
- Product Complexity or Bug Frequency: High complexity or frequent bugs in the product can lead to longer resolution times as issues may require more in-depth investigation and problem-solving.
- Agent Workload: High workload on support agents can lead to slower response and resolution times as agents may be overwhelmed with the volume of tickets.
- Lack of Training: Inadequate training for support staff can result in longer resolution times due to inefficiencies in problem-solving and troubleshooting.
- Inefficient Communication Tools: Poor communication tools can slow down the process of gathering necessary information and collaborating with other team members, leading to increased Resolution Time.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Knowledge Base and Self-Service Availability: A comprehensive knowledge base and effective self-service options can reduce the number of tickets and free up agent time, leading to faster resolution of remaining issues.
- Automated Triage Systems: Implementing automated systems for ticket triage can ensure faster and more accurate assignment of tickets, reducing Resolution Time.
- Proactive Monitoring and Alerts: Proactive monitoring of systems and early alerts can help identify and resolve issues before they escalate, reducing Resolution Time.
- Regular Training and Skill Development: Ongoing training and skill development for support staff can enhance their efficiency and effectiveness, leading to quicker resolution of customer issues.
- Efficient Collaboration Tools: Effective collaboration tools can facilitate faster communication and information sharing among team members, reducing the time taken to resolve issues.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Product Management (PM)
Support Operations
Activities

Common initiatives or actions associated with this KPI:

Customer Support
Ticket Triage
SLA Management

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.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- First Response Time: Lagging_to_Leading - As a lagging KPI, First Response Time provides insights into how quickly support teams initially acknowledge customer issues. Analyzing trends in First Response Time helps recalibrate and predict patterns in Resolution Time, allowing support operations to refine staffing and process strategies for faster resolutions.
- Escalation Rate: Lagging_to_Leading - Escalation Rate tracks how often issues require higher-level intervention. An increase in escalations typically signals underlying issues with initial resolutions, prompting teams to revisit Resolution Time as an early indicator and recalibrate leading metrics to improve efficiency.
- Ticket Volume: Lagging_to_Leading - Ticket Volume is a lagging indicator that captures the total demand on the support team. Fluctuations in this metric inform the need to adjust Resolution Time expectations, as higher volume often correlates with increased Resolution Time, prompting resource planning and process optimization.
- First Contact Resolution: Lagging_to_Leading - First Contact Resolution measures the percentage of tickets resolved at the first touchpoint. Poor performance here often leads to longer Resolution Times, so analyzing this lagging KPI helps refine Resolution Time as a leading signal and improves forecasting of support efficiency.
- Customer Health Score: Lagging_to_Leading - Customer Health Score aggregates multiple user experience factors, including support interactions. Declines in customer health driven by slow Resolution Times can prompt recalibration of leading KPIs, helping align support processes for proactive improvement.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Time to Resolution: Leading_to_Leading - Time to Resolution is a closely related KPI that measures the average time to resolve support tickets. It contextualizes Resolution Time, helping to form a more robust early warning system for support efficiency by tracking overall trends and surfacing potential bottlenecks.
- Error Rate: Leading_to_Leading - Error Rate tracks the frequency of errors during service delivery, often leading to increased Resolution Time as more complex issues arise. Monitoring Error Rate alongside Resolution Time provides a broader early detection system for potential support slowdowns.
- Onboarding Drop-off Rate: Leading_to_Leading - High Onboarding Drop-off Rates can signal areas of product confusion or friction that later manifest as increased support tickets and longer Resolution Times. This metric contextualizes Resolution Time by highlighting upstream user experience issues.
- Usage Depth: Leading_to_Leading - Usage Depth measures how extensively users engage with product features. Deeper or more complex usage can lead to more challenging support needs, influencing future Resolution Times. Tracking this metric alongside Resolution Time helps anticipate support demand shifts.
- Customer Effort Score: Leading_to_Leading - Customer Effort Score quantifies the ease with which customers resolve issues. A high effort score can signal impending increases in Resolution Time, so using this alongside Resolution Time strengthens early detection of support process inefficiencies.