A Comprehensive Glossary of the Top 20 Metrics Used in Customer Support

In today’s fast-paced business environment, customer support metrics play a crucial role in assessing efficiency, improving customer satisfaction, and optimizing resources. Companies leveraging AI customer service solutions and AI-powered customer support tools are increasingly using data-driven strategies to enhance customer experience (CX).

This glossary outlines the top 20 customer support metrics, explaining their importance and providing real-world examples to help support teams refine their strategies.

1. First Response Time (FRT)

What It Is: FRT measures the time taken for a customer to receive an initial response after submitting a request.

Why It Matters: Prompt responses enhance customer satisfaction and reduce frustration. Implementing AI-driven solutions can significantly reduce response times.

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Example: A financial services company utilizing an AI-powered helpdesk reduced FRT from 24 hours to under 5 minutes through automated chatbot responses.

2. First Contact Resolution (FCR)

What It Is: FCR tracks the percentage of issues resolved in a single interaction, eliminating the need for follow-ups.

Why It Matters: High FCR rates indicate efficient customer support, leading to improved customer experience.

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Example: A generative AI customer service chatbot at a telecom company achieved an 80% FCR, minimizing customer effort.

3. Customer Satisfaction Score (CSAT)

What It Is: CSAT measures customer happiness through post-interaction surveys.

Why It Matters: High CSAT scores correlate with increased loyalty and retention.

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Example: A CSAT tool helped an e-commerce brand track and improve satisfaction by offering AI-generated responses.

4. Net Promoter Score (NPS)

What It Is: NPS evaluates customer loyalty by asking, "How likely are you to recommend us?"

Why It Matters: A high NPS indicates that customers trust and advocate for the brand.

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Example: A tech startup using an AI customer service chatbot boosted NPS by providing AI-powered self-service options.

5. Ticket Volume

What It Is: The total number of support tickets received within a given time frame.

Why It Matters: Tracking volume helps in workload management and optimizing AI chatbot deflection rates.

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Example: A customer AI tool at an IT firm handled 30% of tickets autonomously, reducing the human agent workload.

6. Ticket Deflection Rate

What It Is: The percentage of tickets resolved through self-service or automation.

Why It Matters: A higher deflection rate indicates that AI-powered customer service is effectively reducing manual support load.

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Example: An AI chatbot at a SaaS company deflected 40% of inquiries, saving over 500 agent hours monthly.

7. Average Handle Time (AHT)

What It Is: AHT measures the average duration of a customer support interaction, including hold and talk time.

Why It Matters: Optimizing AHT leads to higher efficiency without sacrificing customer experience.

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Example: An AI agent startup integrated AI customer support chatbots, reducing AHT from 12 to 7 minutes.

8. Customer Effort Score (CES)

What It Is: CES assesses how easy it is for customers to get their issues resolved.

Why It Matters: A lower CES indicates a smoother support experience, often achieved with AI agent assist tools.

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Example: A startup customer service team implemented AI customer service software to streamline issue resolution.

9. Abandonment Rate

What It Is: The percentage of customers who abandon a support request before resolution.

Why It Matters: Lower rates suggest better response times and AI-powered solutions for instant assistance.

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Example: A generative AI contact center reduced abandonment rates by deploying virtual agents.

10. Self-Service Utilization

What It Is: Tracks how many customers resolve issues via AI customer care tools like FAQs or chatbots.

Why It Matters: Self-service improves scalability and cost efficiency.

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Example: A fintech firm used an AI-powered customer support chatbot, deflecting 50% of inquiries.

11. Escalation Rate

What It Is: The percentage of cases that require higher-tier support.

Why It Matters: A lower rate means AI-powered automation is handling queries effectively.

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Example: A tech support AI tool deflected 60% of common issues, lowering escalation rates.

12. Customer Churn Rate

What It Is: The percentage of customers who stop doing business with a company.

Why It Matters: Lower churn indicates better support experiences and proactive engagement.

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Example: A CSAT tool alerted a SaaS company to at-risk customers, reducing churn by 15%.

13. Service Level Agreement (SLA) Compliance

What It Is: Measures adherence to promised support response

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14. Cost Per Resolution

What It Is:
The total cost incurred to resolve a single support ticket.

Why It Matters:
Optimizing this metric helps businesses reduce operational expenses while maintaining high service quality. AI-powered customer support solutions lower costs by automating repetitive tasks. (Forrester Research)

Example:
A global retail brand using AI-powered customer service reduced the cost per resolution by 30% by automating FAQs and low-complexity tickets.

15. Reopen Rate

What It Is:
The percentage of tickets reopened due to incomplete or ineffective resolution.

Why It Matters:
A lower reopen rate suggests that customer issues are being resolved correctly on the first attempt, reducing friction and increasing satisfaction. (Gartner Research)

Example:
A customer support AI chatbot integrated into a banking app reduced reopen rates by 35% by using AI-powered decision-making to provide more accurate solutions.

16. Resolution Time

What It Is:
The average time taken to fully resolve a customer inquiry or complaint.

Why It Matters:
Faster resolution times mean higher efficiency and better customer satisfaction. AI-powered customer service bots accelerate resolution by handling common queries instantly. (Harvard Business Review)

Example:
A helpdesk AI system reduced resolution times by 40% for a major SaaS company by automating tier-one customer support.

17. Agent Utilization Rate

What It Is:
The percentage of time support agents spend actively handling customer queries.

Why It Matters:
A well-optimized agent utilization rate ensures that resources are used effectively, without overloading support teams. (Zendesk Blog)

Example:
A startup customer service team using AI agent assist improved agent utilization by 25% by automating simple ticket handling.

18. Helpdesk Response Rate

What It Is:
The percentage of tickets that receive a response within a specific time frame.

Why It Matters:
A higher response rate ensures customers aren’t left waiting, leading to improved satisfaction and retention. (Forbes)

Example:
An AI-powered helpdesk used by an enterprise IT team reduced response time by 60% by automatically categorizing and prioritizing tickets.

19. Repeat Contact Rate

What It Is:
The percentage of customers who have to reach out multiple times for the same issue.

Why It Matters:
A high repeat contact rate signals inefficiencies in problem resolution. AI-driven tools help improve this metric by offering more accurate and context-aware responses. (HubSpot Blog)

Example:
An AI support chatbot reduced repeat contacts by 50% by using conversational AI to diagnose and resolve issues more accurately.

20. Customer Retention Rate

What It Is:
The percentage of customers who continue using a company’s services over a given period.

Why It Matters:
Customer retention is critical for long-term revenue growth and is often improved through AI-enhanced customer support experiences. (McKinsey & Company)

Example:
A generative AI customer experience platform helped a SaaS provider increase retention by 18% by personalizing support interactions.

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