Customer Service Metric Examples in HappyFox Business Intelligence

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Ticket Inflow by Category

Identify the classification of tickets and determine what business areas of your organization get the most traffic by seeing the split between multiple categories of issue types.

HappyFox BI
HappyFox BI
Ticket Distribution By Priority

Have real-time visibility of your ticket queue and their priorities. Understand the percentage breakdown of your tickets by each priority to eliminate any guesswork from tickets that might need your immediate attention.

Ticket Backlog

Track backlog and actively work to reduce your queue to zero. This visualization helps gain insight on possible bottlenecks such as staffing issues or if your team needs more training.

HappyFox BI
HappyFox BI
Average First Response Time

Acknowledge your customers quicker and optimize your processes by tracking this visualization that shows the average time taken by the agent to send the first response to a ticket.

Average Response Time

Reduce the average time it takes for your agents to respond to customers. Optimizing this metric is vital as it has a direct impact on customer satisfaction, as customers do not like to wait.

HappyFox BI
HappyFox BI
Average Resolution Time

Measure how well your customer support team is doing in resolving issues in a timely manner. Track this metric to keep the resolution time relatively low leading to better customer experience.

Top Agents

Measure the number of tickets coming to your inbox. Gauge the ticket bandwidth of your agents, track trends over time, and improve team accountability.

HappyFox BI
HappyFox BI
Agent Productivity

Empower your agents by analyzing how much time an agent takes to resolve tickets when comparing to the ticket volume they’re handling.

Breached SLAs

Scale down customer churn by taking a glimpse into Breached SLAs and drill down deeper to dig into issue types and agents to pinpoint the exact cause of the breach.

HappyFox BI
HappyFox BI
Frequent Agent Actions

Enhance your staff’s performance by analyzing their frequent actions during the ticket lifecycle using this word cloud and find repetitive actions that could use automation.

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