Ticket Resolution Time - How to Reduce and Measure

Sure, not every case will lead to the reduction of your audience but each one is a new bullet in a gun that someday may destroy your company's reputation.

There's a difference between a perfect and a quality product. The last one has much more satisfied users and that's what you should aim for. Once your development, marketing, and sales teams are working hard you should ensure that they and your constantly growing user base are getting proper customer service experience.

‍

Ticket Resolution Time - Definition and Metrics

The value equals the term between points where an inquiry is received and resolved by an agent. The lower, the better. The metric that allows measuring the efficiency of your support team in this perspective is average resolution time or ART.

There are other metrics related to speed in customer support that should be tracked separately and not instead of ART. Let's examine them one by one to avoid confusion and be able to value and properly use each to improve CX.

‍

First Response Time (FRT): The period between the initial response received by a customer after one has submitted an inquiry. Tracking: issue recognition speed. How to calculate: 

‍

‍

Average Resolution Time (ART): The average term between two points: the first response and ultimate case closing. Tracking: issue resolution speed. How to calculate: 

‍

‍

Average Handle Time (AHT): The average duration of active and passive (waiting period) interaction with a customer spent by an agent. Tracking: team efficiency. How to calculate: 

‍

‍

Time to Resolution (TTR): The hours, etc. it takes to fully resolve an inquiry after the first reply including followups, pauses between interactions, and escalations. How to calculate: 

‍

‍

These metrics may require alterations in the settings of the used customer service platform to be able to reach the necessary numbers.

‍

Ticket Resolution Time - Standards and Goals

As many efficiency metrics standards or maximum aspected values for each of the listed metrics are dependent on the industry, issue complexity, buyer expectation and channels used for support contact. Each of them directly impacts CSAT along with resolution success and the approach taken for communication.

For example, let's review the numbers for online inquiries you should stay within for SaaS business.

‍

‍

These numbers clearly emphasize the difference between the metrics and underline the core problem - how to reduce the ticket resolution period without losing accuracy and quality of customer experience. One of the solutions is a customer service chatbot.

‍

How to Reduce Resolution Time with Ioni

There are two basic aspects of the problem to address: response term and response accuracy. The following steps should be considered for action after analysis of time and satisfaction metrics.

‍

Route definition

Create a step-by-step funnel for a buyer's inquiry. How is it received? How is it recorded? How is it prioritized? How should it be processed? Who should handle it? How long wait is acceptable for response and resolution?

Start without any customer service software or automation tools because if you don't have answers to these questions their application doesn't make sense. And don't wait until your ticket turnover exceeds - define ways, numbers, and executives from day one.

‍

Performance analysis

The earlier you'll be able to identify gaps the more days you'll have to address them. But to get meaningful numbers you should track basic metrics for at least one-two month. As months go by you should extend the list of values to monitor.

The longer the period the more data you'll have to analyze and ergo identify trends, dependencies, and patterns that will help build better CS and CX strategies, correlate with industry standards, and improve decision-making concerning necessary changes.

‍

Process optimization

Before any actions, it's all about answers to the following questions using the gathered metrics. What to change in the process? Should you use or change a help desk/ its settings? Should you reduce or extend your support team? What to add to a knowledge base?

How to improve employee performance? Can the team act proactive on issues? Is automation required? Could conversational ai chatbot correspond with accuracy standards? And so on. Don't forget that a change in one aspect can require alternations in others to take actual impact on daily and weekly tasks.

‍

Workflow automation

Although it's the first step thought it's only fourth in the queue. 39% of buyers say that they won't purchase from a company that doesn't provide a personalized approach across the whole experience.

That is why it requires clear understanding of its cost-efficiency or in other words the balance between customer service response time, reply accuracy, and cost including employee onboarding is right for your company.

Ioni is one of the tools that thanks to chatbot technology enables process automation without losing brand identity. It helps a team quickly find resolutions for untypical issues thanks to response suggestions based on a product knowledge base.

‍

Employee support

So far even a ChatGPT chatbot can't replace experienced support agents. That's why you should care not only about KPIs and the department's productivity but also about their onboarding, training, work amount, motivation, and mental health.

Create a productive environment with efficient communication and predefined patterns; Ensure initial and regular qualification updates, especially for multichannel support; 

Provide self-service options for users like FAQs, how-to videos, documentation, etc.; 

Run team-building events and benefits programs for quality and fast performance;

Promote a well-being culture to provide truly responsive support.

‍

Time is a valuable and impactful metric in customer service. Agent AI assist tools are now taking over the market since they help achieve industry benchmarks without losing response quality and brand voice.

‍