Customer Support Metrics

Agent touches per ticket measures the average number of agent interactions required to resolve a single support ticket. It captures the efficiency of the resolution process and directly reflects the effort placed on both agents and customers throughout the support journey.

Average handle time measures the average total duration of a single customer support interaction, including talk time, hold time, and after-call work. It is one of the most widely tracked efficiency metrics in contact centres and support operations, directly influencing staffing models, cost forecasts, and service level planning.

Average reply time measures the mean elapsed time between a customer sending a message and an agent responding within an ongoing support conversation. Unlike first response time, which covers only the initial reply, ART tracks responsiveness throughout the entire interaction, capturing the experience customers have after the conversation has started.

Average resolution time measures the mean elapsed time from when a support ticket is created to when it is fully resolved and closed. It captures the end-to-end customer experience of getting an issue fixed, encompassing wait times, agent work time, escalations, and any back-and-forth exchanges required to reach a solution.

Call abandonment rate measures the percentage of inbound calls where the caller disconnects before reaching a live agent. It is a direct indicator of whether a contact centre can meet demand, and every abandoned call represents a customer whose issue remains unresolved and whose patience has been exhausted.

Conversations per teammate measures the average number of active support conversations each agent handles during a given period. It is a core workload and capacity metric that influences response times, resolution quality, agent wellbeing, and the overall cost efficiency of a support operation.

Escalation rate measures the percentage of support tickets that are transferred from one tier or team to a higher tier or specialist group for resolution. It reflects the gap between the issues customers raise and the ability of frontline agents to resolve them, making it a key indicator of agent readiness, process maturity, and product complexity.

First response time measures the elapsed time between a customer creating a support ticket and receiving the first substantive response from a human agent. It is the metric that shapes the customer's initial impression of the support experience and sets the tone for the entire interaction.

Knowledge base views is the total number of times self-service help articles are viewed within a given period. It is the foundational volume metric for understanding how customers engage with your help content and a leading indicator of self-service adoption and support deflection effectiveness.

Most common issues is a ranked distribution of support ticket types by frequency, revealing which problems generate the highest volume of customer contacts. It is the diagnostic metric that tells support and product teams where to invest to reduce ticket volume and improve customer experience.

Percentage of positive votes measures the proportion of knowledge base article ratings that are positive, typically captured through "Was this article helpful?" yes/no prompts. It is the most direct signal of whether self-service content is actually solving customer problems.

Predicted CSAT is a machine-learning-generated satisfaction score that estimates how a customer would rate a support interaction before they respond to a survey. It transforms CSAT from a retrospective sample into a real-time, comprehensive quality signal across 100% of interactions.

The ratio of knowledge base views to tickets submitted measures how many self-service article views occur for every new support ticket created. It is the core metric for evaluating whether your self-service content is effectively deflecting tickets and reducing the load on human agents.

Ticket backlog is the total number of unresolved support tickets at a given point in time. It is the stock metric that reveals whether a support operation has the capacity to keep up with incoming demand, and it is the earliest warning signal of a growing gap between ticket inflow and resolution throughput.

Ticket volume is the total number of new support tickets created within a defined period. It is the fundamental demand metric for support operations, determining staffing requirements, budget allocation, and the urgency of self-service and product quality investments.

Repeat contact rate measures the percentage of customers who contact support more than once about the same issue or within a defined time window. It is a direct measure of resolution quality: when a customer contacts support again, it typically means their problem was not fully resolved on the previous interaction. High repeat contact rates increase support costs, frustrate customers, and signal systemic issues in either the product or the support process.

Self-service success rate measures the percentage of customer support queries that are resolved through self-service channels without requiring interaction with a human agent. These channels include knowledge bases, help centres, chatbots, FAQ pages, in-app guidance, and community forums. A high self-service success rate means customers can find answers independently, which reduces support costs, improves response times, and often provides a better customer experience than waiting for an agent.

Conversation resolution rate measures the percentage of customer support conversations that are resolved, meaning the customer's issue is fully addressed and the conversation is closed. It captures the effectiveness of the support team at actually solving problems rather than simply responding to them. A high resolution rate indicates that the team is closing the loop on customer issues, while a low rate suggests that conversations are going unanswered, being abandoned, or left in limbo.

Agent utilisation rate measures the percentage of an agent's available working time spent on active customer conversations. It captures how effectively support capacity is being used, balancing the need for productivity against the risk of burnout and declining service quality. The metric helps support leaders right-size their teams, plan shifts, and understand whether staffing levels match conversation demand.

Support cost per conversation measures the total cost of running the support operation divided by the number of conversations handled. It captures the unit economics of customer support, providing a clear picture of how much each customer interaction costs the business. The metric is essential for budgeting, staffing decisions, channel strategy, and evaluating investments in automation and self-service.