Time to Resolution

What Is Time to Resolution?

Time to resolution (TTR) is the total elapsed time between when a customer submits a support request and when that request is fully resolved. It is one of the most important operational metrics in customer service because it directly reflects how quickly your team delivers outcomes, not just responses.

TTR is sometimes called average resolution time or mean time to resolution (MTTR). Unlike first contact resolution, which measures whether an issue was resolved in a single interaction, TTR captures the full lifecycle of a ticket, including wait times, transfers, escalations, and back-and-forth communication. A low TTR means customers spend less time waiting and more time getting value from your product.

How to Calculate Time to Resolution

Time to Resolution = Total Resolution Time for All Resolved Tickets / Number of Tickets Resolved

For example, if your team resolved 500 tickets last month and the total resolution time across all 500 tickets was 4,000 hours, your average TTR is 8 hours per ticket.

Key considerations when measuring TTR:

• Business hours vs. clock hours: Decide whether TTR counts only during business hours or includes nights and weekends. Business hours give a more accurate picture of agent performance, while clock hours reflect the customer's actual experience.
• Median vs. mean: A few complex tickets can inflate the average. Reporting both mean and median TTR gives a more honest view of performance.
• Channel segmentation: Measure TTR separately for live chat, email, phone, and self-service since each channel has different resolution dynamics.

Industry Benchmarks

TTR benchmarks vary widely depending on your industry, support complexity, and channel mix. Based on 2025 industry data:

• E-commerce and retail: 1.5 to 4 hours median TTR. High-performing teams resolve most issues within a single session.
• B2B SaaS: 12 to 24 hours average. Technical complexity and multi-stakeholder issues push resolution times higher.
• IT service management: 22.6 hours average for teams using generative AI, compared to 32.5 hours for teams without AI, according to SolarWinds' 2025 State of ITSM report.
• General helpdesk: 8 to 24 hours typical. Top-performing teams target under 6 hours.

Research Insight: According to Forrester, reducing time to resolution is one of the highest-impact improvements a support team can make, because every hour saved directly improves CSAT, reduces cost per ticket, and lowers churn risk. Industry data from 2025, based on over 1.2 billion tickets, shows AI-driven teams reducing resolution times from 32 hours to 32 minutes in top-performing segments.

Why It Matters

TTR is one of the strongest predictors of customer satisfaction (CSAT) and loyalty. Every hour a customer waits for resolution is an hour they may spend evaluating competitors. Longer resolution times also increase cost per ticket because each additional touchpoint adds agent labor, system overhead, and management attention.

TTR also compounds. Unresolved tickets clog queues, increase ticket volume through follow-ups, and reduce agent productivity as teams context-switch between aging tickets and new requests.

How AI Improves This Metric

AI-powered customer service dramatically reduces TTR in three ways:

• Instant resolution of common issues: AI Agents can resolve routine questions in seconds, eliminating wait times for password resets, order status checks, billing inquiries, and similar high-volume requests.
• Intelligent routing and context: AI can classify, prioritize, and route complex tickets to the right specialist on the first try, reducing transfers and escalation delays.
• Agent augmentation: AI Copilot tools surface relevant knowledge base articles, past resolutions, and customer context so human agents resolve issues faster without manual research.

The Maven AGI Advantage

Maven AGI reduces time to resolution by resolving the majority of customer issues autonomously, before a human agent is ever needed. Instead of routing tickets through queues, Agent Maven resolves them in real time.

• Mastermind (EdTech): 93% of live chat conversations resolved autonomously. Deployed in 6 weeks.
• K1x (FinTech): 80% resolution rate with 10x improvement over their prior AI tool. Live in 1 week.
• Papaya Pay (FinTech): 90% autonomous resolution, meaning 9 out of 10 tickets never enter a queue.

Maven AGI Approach: With 100+ integrations and an AI-native architecture, Maven AGI connects to your existing systems and starts resolving tickets from day one. When 80-93% of issues are resolved by AI, the remaining tickets that reach human agents get faster attention and shorter resolution times across the board.

Frequently Asked Questions

What is a good time to resolution for customer support?

A good TTR depends on your channel and industry. For live chat, aim for under 10 minutes. For email-based support, 4 to 12 hours is strong. B2B SaaS teams typically target under 24 hours. The most meaningful benchmark is your own TTR trend over time, improving month over month indicates a healthier operation.

How is time to resolution different from average handle time?

TTR measures the full elapsed time from ticket creation to resolution, including all wait times and back-and-forth. Average handle time (AHT) measures the active time an agent spends on a single interaction, including talk time, hold time, and after-call work. A ticket with a 2-hour TTR might have only 8 minutes of AHT spread across two contacts.

Does AI actually reduce time to resolution?

Yes. According to Harvard Business Review, AI-assisted support teams resolve issues significantly faster while maintaining quality. The SolarWinds 2025 State of ITSM report found that teams using generative AI resolved tickets 30% faster, averaging 22.6 hours versus 32.5 hours. For AI-native platforms like Maven AGI, the improvement is even more dramatic, with most tickets resolved in seconds rather than hours.

Should I measure TTR in business hours or calendar hours?

Measure both. Business hours reflect your team's efficiency and are useful for internal benchmarking. Calendar hours reflect the customer's experience and correlate more closely with Customer Effort Score and satisfaction. Reporting both gives you a complete picture.

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