Resolution Rate

Understanding Resolution Rate

Resolution rate is a customer service metric that measures the percentage of support inquiries where the customer's problem was fully solved. Unlike deflection rate, which tracks whether a ticket was redirected away from a human agent, resolution rate answers a more important question: did the customer actually get their issue fixed?

For support teams evaluating AI tools, this distinction matters. A high deflection rate might look good on a dashboard, but it says nothing about whether the customer left satisfied or simply gave up. Resolution rate closes that gap.

How to Calculate Resolution Rate

Resolution Rate = (Issues Resolved / Total Issues) x 100

For example, if your team handles 500 tickets in a week and 425 are fully resolved without follow-up, your resolution rate is 85%.

An issue counts as "resolved" when:

• The customer's question was answered completely
• Any requested action was completed (refund processed, account updated, subscription changed)
• No follow-up contact was needed for the same issue within 48 hours

That last point is critical. Many tools count a ticket as resolved the moment it closes, even if the customer contacts you again the next day about the same problem. Tracking repeat contacts within a 48-hour window gives you a more honest picture of true resolution.

Resolution Rate Benchmarks

Industry benchmarks vary by channel and complexity, but here is what the data shows:

• Traditional rule-based bots: 10-30% resolution (most issues escalate to human agents)
• First-generation AI tools: 30-50% resolution (better language understanding, but limited ability to take action)
• Modern AI agents: 70-93% resolution (full conversation context, action-taking capability, knowledge grounding)

According to the Freshworks 2025 Customer Service Benchmark Report, which analyzed over 1.2 billion tickets across 32,000+ teams, AI-powered automation can cut resolution times by up to 40% and handle a significant share of tickets instantly.

First contact resolution (FCR) across the industry sits around 70-75%, but this includes human agents. For AI-only resolution, the range is much wider and depends heavily on the platform and how "resolution" is defined.

Why Resolution Rate Matters More Than Deflection

Most AI support tools are optimized for deflection. They report metrics like "70% of customers never reached a human agent." That sounds impressive, but deflection only tells you the customer did not escalate. It does not tell you:

• Whether the customer's problem was actually solved
• Whether they left frustrated and quietly churned
• Whether they found a workaround on their own and gave up on your support entirely
• Whether they will come back tomorrow with the same issue

Resolution rate forces a harder question. It asks: of the people who interacted with your AI, how many had their issue fully handled? That is a higher bar, and it requires better AI. But it is the only metric that aligns with what customers actually want.

Industry Research: Gartner notes that self-service deflection often masks unresolved demand, and recommends measuring true containment (resolution) alongside deflection to avoid misleading KPIs.

What Drives High Resolution Rates

Several factors separate teams with 30% resolution from those hitting 90%+:

• Knowledge grounding: AI that pulls from verified documentation, past tickets, and product data rather than generating answers from scratch. This reduces hallucinations and builds trust.
• Action-taking capability: Resolving an issue often means doing something, not just answering a question. Processing a refund, updating an account, or changing a subscription requires the AI to integrate with backend systems.
• Context across conversations: Customers do not explain their issue from scratch every time. AI that remembers prior interactions and pulls account history can resolve issues faster and more accurately.
• Smart escalation: Knowing when not to resolve is just as important. When an issue requires human judgment, the AI should hand off with full context so the agent does not start from zero.
• Continuous improvement: Monitoring which topics get resolved vs. escalated, then feeding that data back to improve the AI over time.

Resolution Rate vs. Other Support Metrics

• vs. Deflection Rate: Deflection measures ticket prevention. Resolution measures whether the problem was solved. A customer who abandons a chatbot in frustration counts as "deflected" but not "resolved."
• vs. First Contact Resolution (FCR): FCR tracks whether an issue was solved on the first interaction, including by human agents. Resolution rate for AI measures fully automated resolution without human involvement.
• vs. CSAT: Resolution drives satisfaction, but they measure different things. CSAT captures sentiment. Resolution rate captures operational outcomes. Both matter.
• vs. Average Handle Time (AHT): AHT measures speed. Resolution rate measures effectiveness. Rushing to close tickets faster can actually hurt resolution if issues are not fully addressed.

Proven Resolution Rates from Maven AGI Customers

Maven AGI measures resolution, not deflection, as its north star metric. Here is what that looks like in practice:

• Mastermind (EdTech): 93% of live chat conversations resolved. Deployed in 6 weeks.
• Enumerate (PropTech): 91% resolution rate across support channels.
• Papaya Pay (FinTech): 90% autonomous resolution.
• K1x (FinTech): 80% resolution rate, a 10x improvement over their prior AI tool. Live in 1 week.

Maven AGI Approach: Resolution is the only metric Maven AGI optimizes for. Every customer deployment tracks whether the problem was actually solved, not whether a ticket was avoided. That focus on outcomes over volume is what drives 80-93% resolution rates across industries including EdTech, FinTech, PropTech, and SaaS.

Frequently Asked Questions

What is a good resolution rate?

It depends on the channel and whether you are measuring human or AI resolution. For human support teams, first contact resolution benchmarks sit around 70-75%. For AI-only resolution, legacy chatbots hit 10-30%, while modern AI agents with action-taking capabilities reach 80-93%.

How is resolution rate different from deflection rate?

Deflection rate measures how many tickets were prevented from reaching a human agent. Resolution rate measures how many customer issues were actually solved. A ticket can be "deflected" without the problem being fixed. Resolution is the harder, more honest metric.

How do you improve resolution rate?

Focus on three areas: better knowledge grounding (give your AI accurate, up-to-date information), action-taking integrations (let AI process refunds, update accounts, not just answer questions), and feedback loops (track which topics fail and improve them over time).

Can AI really resolve 90%+ of support tickets?

Yes, but only with the right architecture. Rule-based chatbots top out at 10-30%. Agentic AI platforms that combine language understanding, knowledge retrieval, and backend integrations consistently reach 80-93% in production environments.

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