First Response Time (FRT)¶

Definition¶

First Response Time (FRT) is the average time it takes for a customer support team to provide an initial response to a customer inquiry. It reflects the speed and efficiency of a company’s ability to acknowledge and address customer concerns.

Description¶

First Response Time (FRT) is a key indicator of customer support responsiveness and operational readiness, reflecting how quickly your team replies to customer requests—whether via live chat, email, or ticketing systems.

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

In SaaS, it reflects support staffing and live chat SLAs
In eCommerce, it affects order issues, returns, or urgent inquiries
In B2B, it impacts high-value clients and account satisfaction

A short FRT reassures customers and builds trust, while a long FRT can lead to frustration, churn, or negative CSAT. By segmenting FRT by channel, region, or issue type, you uncover where resources, workflows, or automation can speed up replies.

First Response Time informs:

Strategic decisions, like support team scaling and tech investment
Tactical actions, such as auto-responders, triage improvements, or queue optimization
Operational improvements, including ticket routing, AI chat use, and rep prioritization
Cross-functional alignment, keeping support, CX, and ops teams aligned on speed-to-service goals

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 Staffing and Coverage Windows: More agents or extended hours reduce lag, especially across time zones.
Routing and Triage Efficiency: Delays often happen when issues aren’t routed to the right person fast.
Channel Responsiveness (Email vs. Chat vs. In-App): Real-time channels naturally drive lower response time — but need to be staffed accordingly.

Improvement Tactics & Quick Wins¶

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

If first response time is high, audit ticket routing rules — are low-complexity requests getting stuck behind enterprise cases?
Add automated “We got your message” confirmations + estimated response time to reduce perceived wait.
Run a test with a triage bot that tags and escalates urgent tickets faster.
Refine support shifts or SLAs based on high-volume hours (e.g., Monday mornings, launch weeks).
Partner with CX to track gaps by region, tier, or channel and rebalance resources.

Required Datapoints to calculate the metric
- Time of Customer Inquiry: Timestamp when a customer submits a request.
- Time of First Response: Timestamp when the first response is sent.
- Number of Tickets: Total tickets or inquiries handled within the measurement period.
Example to show how the metric is derived

An e-commerce company calculates FRT for its support team in Q3:
- Total First Response Time: 5,000 minutes
- Total Inquiries: 1,000
- FRT = 5,000 / 1,000 = 5 minutes

Formula¶

Formula

\[ \mathrm{First\ Response\ Time} = \frac{\sum \mathrm{First\ Response\ Times\ for\ All\ Tickets}}{\mathrm{Total\ Number\ of\ Tickets}} \]

Data Model Definition¶

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

CustomerSupport

cube('CustomerSupport', {
  sql: `SELECT * FROM customer_support`,

  measures: {
    firstResponseTime: {
      sql: `TIMESTAMPDIFF(SECOND, ${CUBE}.time_of_customer_inquiry, ${CUBE}.time_of_first_response)`,
      type: 'avg',
      title: 'First Response Time',
      description: 'Average time in seconds for the first response to a customer inquiry.'
    },
    numberOfTickets: {
      sql: `number_of_tickets`,
      type: 'sum',
      title: 'Number of Tickets',
      description: 'Total number of tickets handled within the measurement period.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each customer support interaction.'
    },
    timeOfCustomerInquiry: {
      sql: `time_of_customer_inquiry`,
      type: 'time',
      title: 'Time of Customer Inquiry',
      description: 'Timestamp when a customer submits a request.'
    },
    timeOfFirstResponse: {
      sql: `time_of_first_response`,
      type: 'time',
      title: 'Time of First Response',
      description: 'Timestamp when the first response is sent.'
    }
  }
});

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 Staffing and Coverage Windows: Insufficient staffing or limited coverage hours can lead to increased First Response Time as there are fewer agents available to handle incoming inquiries promptly.
- Routing and Triage Efficiency: Inefficient routing and triage processes can cause delays in directing inquiries to the appropriate support personnel, thereby increasing First Response Time.
- Channel Responsiveness (Email vs. Chat vs. In-App): Relying heavily on slower channels like email without adequate staffing for real-time channels can increase First Response Time.
- Agent Workload: High workload per agent can lead to slower response times as agents are unable to address inquiries promptly.
- System Downtime: Frequent system downtimes or technical issues can prevent timely responses, thus increasing First Response Time.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Support Team Staffing and Coverage Windows: Adequate staffing and extended coverage hours can significantly reduce First Response Time by ensuring more agents are available to respond to inquiries promptly.
- Routing and Triage Efficiency: Efficient routing and triage processes ensure inquiries are quickly directed to the right personnel, reducing First Response Time.
- Channel Responsiveness (Email vs. Chat vs. In-App): Utilizing real-time channels like chat or in-app messaging with proper staffing can lower First Response Time.
- Agent Training and Skill Level: Well-trained and skilled agents can handle inquiries more efficiently, reducing First Response Time.
- Automation and AI Tools: Implementing automation and AI tools for initial triage and response can decrease First Response Time by quickly addressing common inquiries.

Involved Roles & Activities¶

Involved Roles

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

Support Operations
Activities

Common initiatives or actions associated with this KPI:

Customer Support
SLA Management
Queue Prioritization

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.
- Ticket Volume: High ticket volume can predict longer First Response Time (FRT) by increasing support workload, providing an early signal for potential delays if staffing or automation is not adjusted.
- Lead Response Time: Lead Response Time reflects the overall responsiveness of teams to inbound requests (not just support), contextualizing FRT trends and providing cross-functional early warning if response times increase across the board.
- Customer Effort Score: Higher customer effort scores often correlate with slower or less efficient first responses, as increased effort suggests friction in support interactions. Monitoring this can help proactively address FRT issues.
- Onboarding Completion Rate: A higher onboarding completion rate suggests customers are better prepared and may submit fewer or simpler support inquiries, thus enabling faster FRT. Declines may foreshadow resource strain.
- Escalation Rate: Escalation Rate tracks the proportion of tickets that require higher-tier intervention, which can increase FRT when high. Early spikes signal the need for additional frontline training or resources.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Customer Satisfaction Score: Customer Satisfaction Score (CSAT) directly quantifies the impact of FRT on user happiness. Low CSAT following slow FRT episodes can recalibrate FRT targets and escalation procedures.
- Customer Churn Rate: Increases in churn, especially after periods of high FRT, highlight the long-term business risk associated with slow response. This relationship can inform FRT thresholds and support investment.
- Average Resolution Time: Trends in average resolution time often follow increases in FRT, confirming that slow first responses cascade into overall slower case resolutions. Analysis can help refine FRT as a predictive signal.
- Customer Downgrade Rate: Customers experiencing delayed first responses may downgrade due to perceived lack of support. Monitoring this lagging outcome informs strategy for proactive FRT improvements.
- Net Promoter Score: Drops in NPS after periods of high FRT reveal reputational and advocacy risks, providing feedback to calibrate FRT targets and support team processes.