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Analyze Your Service Desk Ticket Data

Take a data-driven approach to service desk optimization.

  • Leverage your service desk ticket data to gain insights for your service desk strategy.

Our Advice

Critical Insight

  • Properly analyzing ticket data is challenging for the following reasons:
    • Poor ticket hygiene and unclear ticket handling means the data is often inaccurate or incomplete.
    • Service desk personnel are not sure where to start with analysis.
    • Too many metrics are tracked to parse actionable data from the noise.
  • Ticket data won’t give you a silver bullet, but it can help point you in the right direction.

Impact and Result

  • Create an iterative framework for tracking metrics, keeping data clean, and actioning your data on day-to-day and month-to-month timelines.

Analyze Your Service Desk Ticket Data Research & Tools

Start here – read the Executive Brief

Read our concise Executive Brief to find out why you should analyze your service desk ticket data, review Info-Tech’s methodology, and understand the four ways we can support you in completing this project.

1. Import your ticket data

Enter your data into our tool. Compare your own ITSM ticket fields to improve ticket data moving forward.

2. Analyze your ticket data

Use the ticket analysis tool as a guide to build your own operational dashboards to measure metrics over time. Gain actionable insights from your data.

3. Action your ticket data

Use the data to communicate your findings to the business and leadership using the Ticket Analysis Report.


INFO-TECH RESEARCH GROUP

Analyze Your Service Desk Ticket Data

Take a data-driven approach to service desk optimization.

Table of Contents

Executive Brief
Analyst Perspective
Executive Summary
Phase 1: Import Your Ticket Data
Phase 2: Analyze Your Ticket Data
Step 2.1: Analyze Your High-Level Ticket Data
Step 2.2: Analyze Incidents, Service Requests, and Ticket Categories
Phase 3: Communicate Your Insights
Step 3.1: Build Recommendations Based on Your Ticket Data
Step 3.2: Action and Communicate Your Ticket Data
Summary of Accomplishment
Additional Support
Bibliography

EXECUTIVE BRIEF

Analyst Perspective

Photo of Benedict Chang, Research Analyst, Infrastructure & Operations, Info-Tech Research Group

Benedict Chang
Research Analyst, Infrastructure & Operations
Info-Tech Research Group

Photo of Ken Weston ITIL MP, PMP, Cert.APM, SMC, Research Director, Infrastructure & Operations, Info-Tech Research Group

Ken Weston ITIL MP, PMP, Cert.APM, SMC
Research Director, Infrastructure & Operations
Info-Tech Research Group

The perfect time to start analyzing your ticket data is now

Service desks improve their services by leveraging ticket data to inform their actions. However, many organizations don’t know where to start. It’s tempting to wait for perfect data, but there’s a lot of value in analyzing your ticket data as it exists today.

Start small. Track key tension metrics based on the out-of-the-box functionality in your tool. Review the metrics regularly to stay on track.

By reviewing your ticket data, you’re going to get better organically. You’re going to learn about the state of your environment, the health of your processes, and the quality of your services. Regularly analyze your data to drive improvements.

Make ticket analysis a weekly habit. Every week, you should be evaluating how the past week went. Every month, you should be looking for patterns and trends.

Executive Summary

Your Situation

Leverage your service desk ticket data to gain insights for improving your operations:

  1. Use a data-based approach to allocate service desk resources.
  2. Design appropriate SLOs and SLAs to better service end users.
  3. Gain efficiencies for your shift-left strategy.
  4. Communicate the current and future value of the service desk to the business.

Common Obstacles

Properly analyzing ticket data is challenging for the following reasons:

  • Poor ticket hygiene and unclear ticket handling guidelines can lead to untrustworthy results.
  • Undocumented tickets from various intake channels prevents you from seeing the whole picture.
  • Service desk personnel are not sure where to start with analysis and are too busy to find time.
  • Too many metrics are tracked to parse actionable insights from the noise.

Info-Tech’s Approach

Info-Tech’s approach to improvement:

  • To reduce the noise, standardize your ticket data in a format that will ease analysis.
  • Start with common analyses using the cleaned data set.
  • Identify action items based on your ticket data.

Analyze your ticket data to help continually improve your service desk.

Slow down. Give yourself time.

Give yourself time to observe the new metrics and draw enough insights to make recommendations for improvement. Then, execute on those recommendations. Slow and steady improvement of the service desk only adds business value and will have a positive impact on customer satisfaction.

Your challenge

This research is designed to help service desk managers analyze their ticket data

Analyzing ticket data involves:

  • Collecting ticket data and keeping it clean. Based on the metrics you’re analyzing, define ticket expectations and keep the data up to date.
  • Showing the value of the service desk. SLAs are meaningless if they are not met consistently. The prerequisite to implementing proper SLAs is fully understanding the workload of the service desk.
  • Understanding – and improving – the user experience. You cannot improve the user experience without meaningful metrics that allow you to understand the user experience. Different user groups will have different needs and different expectations of the level of service. Your metrics should reflect those needs and expectations.

36% of organizations are prioritizing ticket handling in IT for 2021 (Source: SDI, 2021)

12% of organizations are focusing directly on service desk improvement (Source: SDI, 2021)

Common obstacles

Many organizations face these barriers to analyzing their ticket data:

  • Finding time to properly analyze ticket data is a challenge. Not knowing where to start can lead to not analyzing the proper data. Service desks end up either tracking too much data or not tracking the proper metrics.
  • Data, even if clean, can be housed in various tools and databases. It’s difficult to aggregate data if the data is stored throughout various tools. Comparisons may also be difficult if the data sets aren’t consistent.
  • Shifting left to move tickets toward self-service is difficult when there is no visibility into which tickets should be shifted left.

What your peers are saying about why they can’t start analyzing their ticket data:

  • “My technicians do not consistently update and close tickets.”
  • “My ITSM doesn’t have the capabilities I need to make informed decisions on shifting tickets left.”
  • “My tickets are always missing data”
  • “I’m constantly firefighting. I have no time for ticket data analysis.”
  • “I have no idea where to start with the amount of data I have.”
(Source: Info-Tech survey, 2021; N=20.)

Common obstacles that prevent effective ticket analysis

We asked IT service desk managers and teams about their biggest hurdles

Missing or Inaccurate Information
  • Lack of information in the ticket
  • Categories are too general/specific to draw insights
  • Poor ticket hygiene
Missing Updates
  • Tickets aren’t updated while being resolved
Correlating Tickets to Identify Trends
  • Not sure where to start with all the data at hand
No Time
  • No time to figure out the tool or analyze the data properly
Ineffective Categorization Schemes
  • Reduces the power of ticket data
Tool Limitations
  • Can’t be easily customized
  • Too customized to be effective
  • Desired dashboards unavailable
(Source: Info-Tech survey, 2021; N=20)

Info-Tech’s approach

Repeat this analysis every business cycle:

  • Gather Your Data
    Collect your ticket data OR start measuring the right metrics.
  • Extract & Analyze
    Organize and visualize your data to extract insights
  • Action the Results
    Implement low-effort improvements and celebrate quick successes.
  • Implement Larger Changes
    Reference your ticket data while implementing process, tooling, and other changes.
  • Communicate the Results
    Use your data to show the value of your effort.

Measure the value of this blueprint

Track these metrics as you improve

Use the data to tell you which aspects of IT need to be shifted left and which need to be automated

Your data will show you where you can improve.

As you act on your data, you should see:

  • Lower costs per ticket
  • Decreased average time to resolve
  • Increased end-user satisfaction
  • Fewer tickets escalated beyond Tier 1

An illustration of the 'Shift Left Strategy' using three line graphs arranged in a table with the same axes but representing different metrics. The header row is 'Metrics,' then values of the x-axes are 'Auto-Fix,' 'User,' 'Tier 1,' 'Tier2/Tier3,' and 'Vendor.' Under 'Metrics' we see 'Cost,' 'Time,' and 'Satisfaction.' The 'Cost' graph begins 'Low' at 'Auto-Fix' and gradually moves to 'High' at 'Vendor.' The 'Time' graph begins 'Low' at 'Auto-Fix' and gradually moves to 'High' at 'Vendor.' The 'Satisfaction' graph begins 'High' at 'Auto-Fix' and gradually moves to 'Low' at 'Vendor.' Below is an arrow directing us away from the 'Vendor' option and toward the 'Auto-Fix' option, 'Shift Ticket Resolution Left.'

See Info-Tech’s blueprint Optimize the Service Desk With a Shift-Left Strategy.

Info-Tech’s methodology for analyzing service desk tickets

1. Import Your Ticket Data 2. Analyze Your Ticket Data 3. Communicate Your Insights
Phase Steps
  1. Import Your Ticket Data
  1. Analyze High-Level Ticket Data
  2. Analyze Incidents, Service Requests, and Ticket Categories
  1. Build Recommendations
  2. Action and Communicate Your Ticket Data
Phase Outcomes Enter your data into our tool. Compare your own ITSM ticket fields to improve ticket data moving forward. Use the Service Desk Ticket Analysis Tool as a guide to build your own operational dashboards to measure metrics over time. Gain actionable insights from your data. Use the data to communicate your findings to the business and leadership using the Ticket Analysis Report.

Insight summary

Slow down. Give yourself time.

Give yourself time to observe the new metrics and draw enough insights to make recommendations for improvement. Then, execute on those recommendations. Slow and steady improvement of the service desk only adds business value and will have a positive impact on customer satisfaction.

Iterate on what to track rather than trying to get it right the first time.

Tracking the right data in your ticket can be challenging if you don’t know what you’re looking for. Start with standardized fields and iterate on your data analysis to figure out your gaps and needs.

If you don’t know where to go, ticket data can point you in the right direction.

If you have service desk challenges, you will need to allocate time to process improvement. However, prioritizing your initiatives is easier if you have the ticket data to point you in the right direction.

Start with data from one business cycle.

Service desks don’t need three years’ worth of data. Focus on gathering data for one business cycle (e.g. three months). That will give you enough information to start generating value.

Let the data do the talking.

Leverage the data to drive organizational and process change in your organization by tracking meaningful metrics. Choose those metrics using business-aligned goals.

Paint the whole picture.

Single metrics in isolation, even if measured over time, may not tell the whole story. Make sure you design tension metrics where necessary to get a holistic view of your service desk.

Blueprint deliverables

This blueprint’s key deliverable is a ticket analysis tool. Many of the activities throughout this blueprint will direct you to complete and interpret this tool. The other main deliverable is a stakeholder presentation template to help you document the outcomes of the project.
Service Desk Ticket Analysis Tool Ticket Analysis Report
Use this tool to identify trends and patterns in your ticket data to action improvement initiatives.

Sample of the Service Desk Ticket Analysis Tool blueprint deliverable.

Use this template to document the justification for addressing service desk improvement, the results of your analysis, and your next steps.

Sample of the Ticket Analysis Report blueprint deliverable.

Blueprint benefits

IT Benefits

  • Discover and implement the proper metrics to improve your service desk
  • Use a data-based approach to improve your customer service and operational goals
  • Increase visibility with the business and other IT departments using a structured presentation

Business Benefits

  • Quicker resolutions to incidents and service requests
  • Better expectations for the service desk and IT
  • Better visibility into the current state, challenges, and goals of the service desk
  • More effective support when contacting the service desk

Info-Tech offers various levels of support to best suit your needs

DIY Toolkit

Guided Implementation

Workshop

Consulting

"Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful." "Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keep us on track." "We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place." "Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project."

Diagnostics and consistent frameworks used throughout all four options

Guided Implementation

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 3-4 calls over the course of 2-3 months.

What does a typical GI on this topic look like?

    Phase 1

  • Call #1: Scope requirements, objectives, and your specific challenges. Enter your data into the tool.
  • Phase 2

  • Call #2: Assess the current state across the different dashboards.
  • Phase 3

  • Call #3: Identify improvements and insights to include in the communication report.
  • Call #4: Review the service desk ticket analysis report.

PHASE 1

Import Your Ticket Data

This phase will walk you through the following activities:

  • 1.1.1 Define your objectives for analyzing ticket data
  • 1.1.2 Identify success metrics
  • 1.1.3 Import your ticket data into the tool
  • 1.1.4 Update your ticket fields for future analysis

This phase involves the following participants:

  • Service Desk Manager
  • ITSM Manager
  • Service Desk Technician

1.1.1 Define your objectives for analyzing ticket data

Input: Understanding of current service desk process and ticket routing

Output: Defined objectives for the project

Materials: Whiteboard/flip charts, Ticket Analysis Report

Participants: Service Desk Staff, Service Desk Manager, IT Director, CIO

Use the discussion questions below as a guide
  1. Identify your main objective for analyzing ticket data. Use these three sample objectives as a starting point:
    • Demonstrate value to the business by improving customer service.
    • Improve service desk operations.
    • Reduce the number of recurring incidents.
  2. Answer the following questions as a group:
    • What challenges do you have getting accurate data for this objective?
    • What data is missing for supporting this objective?
    • What kind of issues must be solved for us to make progress on achieving this objective?
    • What decisions are held up from a lack of data?
    • How can better ticket data help us to more effectively manage our services and operations?

Document in the Ticket Analysis Report.

1.1.2 Identify success metrics

Select metrics that will track your progress on meeting the objective identified in Activity 1.1.1.

Input: Understanding of current service desk process and ticket routing

Output: Defined objectives for the project

Materials: Whiteboard/flip charts, Ticket Analysis Report

Participants: Service Desk Manager, IT Director, CIO

Use these sample metrics as a starting point:
Demonstrate value to the business by improving customer service
Ticket trends by category by month # tickets by business department % SLAs met by IT teams
Average customer satisfaction rating % incident tickets closed in one day Service request SLAs met by % Annual IT satisfaction survey result
Improve service desk operations
Incident tickets assigned, sorted by age and priority Scheduled requests for today and tomorrow Knowledgebase articles due for renewal this month Top 5-10 tickets for the quarter
Unassigned tickets by age # incident tickets assigned by tech Open tickets by category Backlog summary by age
Reducing the number of recurring incidents
# incidents by category and resolution code Number of problem tickets opened and resolved Correlation of ticket volume trends to events Reduction of volume of recurring tickets
Use of knowledgebase by users Use of self-service for ticket creation Use of service catalog Use of automated features (e.g. password resets)
Average call hold time % calls abandoned Average resolution time Number of tickets reopened

Document in the Ticket Analysis Report.

Inefficient ticket-handling processes lead to SLA breaches and unplanned downtime

Analyze the ticket data to catch mismanaged or lost tickets that lead to unnecessary escalations and impact business profitability

  • Ticket Category – Are your tickets categorized by type of asset? By service?
  • Average Ticket Times – How long does it take to resolve or fulfill tickets?
  • Ticket Priority – What is the impact and urgency of the ticket?
  • SLA/OLA Violations – Did we meet our SLA objectives? If not, why?
  • Ticket Channel – How was the issue reported or ticket received?
  • Response and Fulfillment – Did we complete first contact resolution? How many times was it transferred?
  • Associated Tasks and Tickets – Is this incident associated with any other tasks like change tickets or problem tickets?

Encourage proper ticket-handling procedures to enable data quality

Ensure everyone understands the expectations and the value created from having ticket data that follows these expectations

  • Create and update tickets, but not at the expense of good customer service. Agents can start the ticket but shouldn’t spend five minutes creating the ticket when they should be troubleshooting the problem.
  • Update the ticket when the issue is resolved or needs to be escalated. If agents are escalating, they should make sure all relevant information is passed along within the ticket to the next technician.
  • Update user of ETA if issue cannot be resolved quickly.
  • Ticket templates for common incidents can lead to fast creation, data input, and categorizations. Templates can reduce the time it takes to create tickets from two minutes to 30 seconds.
  • Update categories to reflect the actual issue and resolution.
  • Reference or link to the knowledgebase article as the documented steps taken to resolve the incident.
  • Validate with the client that the incident is resolved; automate this process with ticket closure after a certain time.
  • Close or resolve the ticket on time.

Info-Tech Insight

Ticket handling ensures clean handovers, whether it is to higher tiers or back to the customer. When filling the ticket out with information intended for another party, ensure the information is written for their benefit and from their point of view.

Service Desk Ticket Analysis Tool overview

The Service Desk Ticket Analysis Tool will help you standardize your ticket data in a meaningful format that will allow you to apply common analyses to identify the actions you need to take to improve service desk operations

TABS 1 & 2
INSTRUCTIONS & DATA ENTRY
TAB 3 : TICKET SUMMARY
TICKET SUMMARY DASHBOARDS
TABS 4 to 8: DASHBOARDS
INCIDENT SERVICE REQUEST CATEGORY
Sample of the Service Desk Ticket Analysis Tool, tabs 1 & 2.
Input at least three months of your exported ticket data into the corresponding columns in the tool to feed into the common analysis graphs in the other tabs.
Sample of the Service Desk Ticket Analysis Tool, tab 3.
This tab contains multiple dashboards analyzing how tickets come in, who requests them, who resolves them, and how long it takes to resolve them.
Sample of the Service Desk Ticket Analysis Tool, tabs 4 to 8.
These tabs each have dashboards outlining analysis on incidents and service requests. The category tab will allow you to dive deeper on commonly reported issues.

1.1.3 Import your data into our Service Desk Ticket Analysis Tool

You can still leverage your current data, but use this opportunity to improve your service desk ticket fields down the line

Input: ITSM data log

Output: Populated Service Desk Ticket Data Analysis Tool

Materials: Whiteboard/flip charts, Service Desk Ticket Analysis Tool

Participants: Service Desk Manager, Service Desk Technicians

Start here:

  • Extract your ticket data from your ITSM tool in an Excel or text format.
  • Look at the fields on the data entry tab of the Service Desk Ticket Analysis Tool.
  • Fill the fields with your ticket data by copying and pasting relevant sections. It is okay if you don’t have all the fields, but take note of the fields you are missing.
  • With the list of the fields you are missing, run through the following activity to decide if you will need to adopt or add fields to your own service desk ticket tool.
Fields Captured
Ticket Number Open Date
Open Time Closed Date
Closed Time Intake Channel
Time to Resolve Site Location
First Contact Resolution Resolution Code
Category (I, II, III) Ticket Type (Request or Incident)
Status of Ticket Resolved by Tier
Ticket Priority Requestor/Department
SLA Fulfilled Subject
Technician

When entering your data, pay close attention to the following fields:

  • Time to Resolve: This is automatically calculated using data in the Open Date, Open Time, Close Date, and Close Time fields. You have three options for entering your data in these fields:
    1. Enter your data as the fields describe. Ensure your data contain only the field description (e.g. Open Date separated from Open Time). If your data contain Open Date AND Open Time, Excel will not show both.
    2. Enter your data only in Open Date and Close Date. If your ITSM does not separate date and time, you can keep the data in a single cell and enter it in the column. The formula in Time to Resolve will still be accurate.
    3. If your ITSM outputs Time to Resolve, overwrite the formula in the Time to Resolve column.
  • SLA: If your ITSM outputs SLA fulfilled: Y/N, enter that directly into the SLA Fulfilled column.
  • Blank Columns: If you do not have data for all the columns, that is okay. Continue with the following activity. Note that some stock dashboards will be empty if that is the case.
  • Incidents vs. Service Requests: If you separate incidents and service requests, be sure to capture that in the SR/Incident for Tabs 4 and 5. If you do not separate the two, then you will only need to analyze Tab 3.
Fields Captured
Ticket Number Open Date
Open Time Closed Date
Closed Time Intake Channel
Time to Resolve Site Location
First Contact Resolution Resolution Code
Category (I, II, III) Ticket Type (Request or Incident)
Status of Ticket Resolved by Tier
Ticket Priority Requestor/Department
SLA Fulfilled Subject
Technician

Use Info-Tech’s tool instead of building your own. Download the Service Desk Ticket Analysis Tool.

1.1.4 Update your ticket fields for future analysis

Input: Populated Service Desk Ticket Data Analysis Tool

Output: New ticket fields to track

Materials: Whiteboard/flip charts, Service Desk Ticket Analysis Tool

Participants: Service Desk Manager, Service Desk Technicians

As a group, pay attention to the ticket fields populated in the tool as well as the ticket fields that you were not able to populate. Use the example “Fields Captured” table to the right, which lists all fields present in the ticket analysis tool.

Discuss the following questions:

  1. Consider the fields not captured. Would it be valuable to start capturing that data for future analysis?
  2. If so, does your ITSM support that field?
  3. Can you make the change in-house or do you have to bring in an external ITSM administrator to make the change?
  4. Capture the results in the Ticket Analysis Report.
Example: Fields Captured - Fields Not Captured
Ticket Number Open Date
Open Time Closed Date
Closed Time Intake Channel
Time to Resolve Site Location
First Contact Resolution Resolution Code
Category (I, II, III) Ticket Type (Request or Incident)
Status of Ticket Resolved by Tier
Ticket Priority Requestor/Department
SLA Fulfilled Subject
Technician

Document in the Ticket Analysis Report.

Info-Tech Insight

Don’t wait for your ticket quality to be perfect. You can still draw actions from your ticket data. They will likely be process improvements initially, but the exercise of pulling the data is a necessary first step.

Common ticket fields tracked by your peers

Which of these metrics do you track and action?

  • Remember you don’t have to track every metric. Only track metrics that are actionable.

For each metric that you end up tracking:

  • Look for trends over time.
  • Brainstorm reasons why the metric could rise or fall.

Associate a metric with each improvement you execute.

  • Performing this step will allow you to better see the value from your team’s efforts.
  • It will also give you a quicker response than waiting for spikes in your data.

A bar chart of 'Metrics tracked by other organizations' with the x-axis populated by different metrics and the y-axis as '% organizations who track the metric'. The highest percentage of businesses track 'Ticket volume', then 'Ticket trends by category', then 'Tickets by business units'. The lowest three shown are 'Reopened tickets', 'Cost per ticket', and 'Other'.(Source: Info-Tech survey, 2021; N=20)

PHASE 2

Analyze Your Ticket Data

This phase will walk you through the following activities:

  • 2.1.1 Review high-level ticket dashboards
  • 2.2.1 Review incident, service request, and ticket category dashboards

This phase involves the following participants:

  • Service Desk Manager
  • Service Desk Technicians
  • IT Managers

Visualize your ticket data as a first step to analysis

Identifying trends is easier when looking at diagrams, graphs, and figures

Start your analysis with common visuals employed by other service desk professionals

  • Phase 2 will walk you through visualizing your data to get a better understanding of your ticket intake, incident management, and service request management.
  • Each step will walk you through:
    • Common visualizations used by service desks
    • Patterns to look for in your visualizations
    • Actions to take to address negative patterns and to continue positive trends
  • Share diagrams that underscore both the value being provided by the service desk as well as the scope of the pain points. Use Info-Tech’s Ticket Analysis Report template as a starting point.

“Being able to tell stories with data is a skill that’s becoming ever more important in our world of increasing data and desire for data-driven decision making. An effective data visualization can mean the difference between success and failure when it comes to communicating the findings of your study, raising money for your nonprofit, presenting to your board, or simply getting your point across to your audience.” - Cole Knaflic, Founder and CEO, Storytelling with Data: A Data Visualization Guide for Business Professionals

Use the detailed dashboards to determine the next steps for improvement

A single number doesn’t tell the whole picture

Analyze trends over time:

  • Analyze trends by day, by week, by month, and by year to determine:
    • When are the busy periods? (E.g. Do tickets tend to spike every morning, every Monday, or every September?)
    • When are the slow periods? (E.g. Do tickets drop at the end of the day, at midday, on Fridays, or over the summer?)
  • Are spikes or drops in volume consistent trends or one-time anomalies?

Then build a plan to address them:

  • How will you handle volume spikes, if they’re consistent?
  • What can your resources work on during slow times, if they are consistent?
  • If you assume no shrinkage, can you handle the peaks in volume if you make all FTEs available to work on tickets at a certain time of day?

Sample of a bar chart comparing tickets that were 'Backlog versus Closed by Month Opened'.

Look for seasonal trends. In this example, we see high ticket volumes in May and January, with lower ticket volumes in June and July when many staff are taking holidays. However, also be careful to look at the big picture of how you pulled the data. August through October sees a high volume of open tickets because the data set is pulled in November, not because there’s a seasonal spike on tickets not closing at the end of the fiscal year.

Track ticket data over time

Make low-effort adjustments before major changes

Don’t rush to a decision based off the first numbers you see

Review ticket summary dashboard

Ideally, you should track ticket patterns over an entire year to get a full sense of trends within each month of the year. At minimum, track for 30 days, then 60, then 90, and see if anything changes. The longer you can track ticket patterns, the more accurate your picture will be.

Review additional dashboards

If you separate incidents and service requests, and you have accurate ticket categories, then you can use these dashboards to further break down the data to identify ticket trends.

The output of the ticket analysis will only be as accurate as its input.
To get the most accurate results, first ensure your data is accurate, then analyze it over as much time as possible. Aggregating with accurate data will give you a better picture of the trends in demand that your service desk sees.

Not separating incidents and service requests? Need to fix your ticket categories? Visit Standardize the Service Desk to get started.

Analyze incidents and requests separately

Each type has its own set of customer experiences and expectations

  • Different ticket types are associated with radically different prioritization, routing, and service levels. For instance, most incidents are resolved within a business day, but requests take longer to implement.
  • If you fail to distinguish between ticket types, your metrics will obscure service desk performance.
  • From a ticket analysis standpoint, separating ticket types prior to analysis or, better yet, at intake allows for cleaner data. In turn, this means more structured analyses, better insights, and more meaningful actions. Not separating ticket types may still get you to the same conclusions, but it will be much more difficult to sift through the data.

Incident

An unanticipated interruption of a service.
The goal of incident management is to restore the service as soon as possible, even if the resolution involves a workaround.

Request

A generic description for a small change or service access.
Requests are small, frequent, and low risk. They are best handled by a process distinct from incident, change, and project management.

Not separating incidents and service requests? Need to fix your ticket categories? Visit Standardize the Service Desk to get started.

Step 2.1

Analyze Your High-Level Ticket Data

Dashboards
  • Ticket Volume
  • Ticket Intake
  • Ticket Handling and Resolution
  • Ticket Categorization

This step will walk you through the following activities:

Visualize the current state of your service desk.

This step involves the following participants:

  • Service Desk Manager
  • Service Desk Technicians
  • IT Managers

Outcomes of this step

Build your metrics baseline to compare with future metric results.

Dashboards: Ticket Volume

Example of a dashboard for ticket volume with two bar charts, one breaking down volume by month, and the other marking certain days or weeks in each month.

Analyze your data for insights

  • Analyze volume trends by day, by week, by month, and by year to determine:
    • When are the busy periods? (E.g. Do tickets tend to spike every morning, every Monday, or every September?)
    • When are slow periods? (E.g. Do tickets drop at the end of the day, at midday, on Fridays, or over the summer?)
  • Are spikes or drops in volume consistent trends or one-time anomalies?
  • What can your resources be working on during slow times? Are you able to address ticket backlog?

Dashboards: Ticket Intake

Example of a dashboard for ticket intake with three bar charts, one breaking it down by 'Intake Channel', one by 'Requestor/Department', and one by 'Location'.

Analyze your data for insights

  • Determine how to drive intake to the most appropriate solution for your organization:
    • A web portal is the most efficient intake method, but it must be user friendly to increase its adoption.
    • The phone should be available for urgent requests or incidents. Encourage those who call with a request to submit a ticket through the portal.
    • Discourage use of email if it is unstructured, as users don’t provide enough detail, and often two or three transactions are required for triage.
    • If walk-ups are encouraged, structure and formalize the support so it can be resourced and managed rather than interrupt-driven.

Dashboard: Ticket Handling and Resolution

Example of a dashboard for ticket handling and resolution with three bar charts, one breaking down 'Tickets Resolved by Technician', one by 'Tier', and one by 'Average Time to Resolve (Hours)'.

Analyze your data for insights

  • Look at your ticket load by technician and by tier. This is an essential step to set your baseline to measure your shift-left initiatives. If you are focusing on self-service or Tier 1 training, the ticket load from higher tiers should decrease over time.
  • If Tiers 2 and 3 are handling the majority of the tickets, this could be a red flag indicating tickets are inappropriately escalated or Tier 1 could use more training and support.
  • For average time to resolve and average time to resolve by tier, are you meeting your SLAs? If not, are your SLAs too aggressive? Are tickets left open and not properly closed?

Dashboard: Ticket Categorization

Analyze your data for insights

  • Ticket categorization is critical to clean data. Having a categorization scheme with categories that are miscellaneous, too specific, or too general easily leads to inaccurate reporting or confusing workflows for technicians.
  • When looking at your ticket categories, first look for duplicate categories that could be collapsed into one.
  • Also look at your top five to seven categories and see if they make sense. Are these good candidates in your organization for automation or shift-left?
  • Compare your Tier 1 categories. The level of specificity for these categories should be comparable to easily run reports. If they are not, assess the need for a category redesign.

Example of a dashboard for ticket categorization with one horizontal bar chart, 'Incident Ticket Volume by Level 1 Category'.

Step 2.2

Analyze Incidents, Service Requests, and Ticket Categories

Dashboards
  • Incidents
  • Service Requests
  • Volume by Ticket Category
  • Resolution Times by Priority and/or Category
  • Tabs for More Granular Investigation and Reporting

This step will walk you through the following activities:

Visualize your incident and service request ticket load and analyze trends. Use this information and cross reference data sets to gain a holistic view of how the service desk interacts with IT and the business.

This step involves the following participants:

  • Service Desk Manager
  • Service Desk Technicians
  • IT Managers

Outcomes of this step

Gain actionable, data-driven improvements based on your incident and service request data. Show the value of the service desk and highlight improvements needed.

Incident and Service Requests Dashboard: Priority and SLA

Example of an Incident and Service Requests dashboard for priority and SLA with three charts, one breaking down 'Incident Priority', one 'Average time to resolve (in hours) by priority', and one '% of SLA met'.

Analyze your data for insights

  • Your ticket priority distribution for overall load and time to resolve (TTR) should look something like above with low-priority tickets having higher load and TTR and high/critical-priority tickets having a lower load and lower TTR. If it is reversed, that is a good indication that the service desk is too reactive or isn’t properly prioritizing its work.
  • If your SLA has a high failure rate, consider reassessing your targets with SLOs that you can meet before publishing them as achievable SLAs.

Incident and Service Requests Dashboard: Priority and SLA

Example of an Incident and Service Requests dashboard for resolution and close with three bar charts, one breaking down 'Incident Volume by Resolution Code', one 'Incidents Resolved by Tier', and one 'Average time to resolve (in hours) by Resolution Code'.

Analyze your data for insights

  • Examine your ticket handling by looking at ticket status and resolution codes.
    • If you have a lot of blanks, then tickets are not properly handled. Consider reinforcing your standards for close codes and statuses.
    • Alternatively, if tickets are left open, you may have to build follow-ups on stale tickets into your process or introduce proper auto-close processes.

Category, Resolution Time, and Resolution Code Dashboards

These PivotCharts allow you to dig deeper

Investigate whether there are trends in ticket volume and resolution times within specific categories and subcategories

Tab 6, Category Dashboard; tab 7, Resolution Time Dashboard; and tab 8, Resolution Code Dashboard are PivotCharts. Use these tabs to investigate whether there are trends in ticket volume, resolution times, and resolution codes within specific categories and subcategories.

Start with the charts that are available. The +/- buttons will allow you to show more granular information. By default, this granularity will be into the levels of the ticket categorization scheme.

For most categorization schemes, there will be too many categories to properly graph. You can apply a filter to investigate specific categories by clicking on the drop-down buttons.

Example of dashboards featured on next slide

Use these tabs for more granular investigation and reporting

TAB 6
CATEGORY DASHBOARD
TAB 7
RESOLUTION TIME DASHBOARD
TAB 8
RESOLUTION TIME DASHBOARD
Sample of the 'Ticket Volume by Second, Third Level Category' dashboard tab.
Investigate ticket distributions in first, second, and third levels. Are certain categories overcrowded, suggesting they can be split? Are certain categories not being used?
Sample of the 'Average Resolution Times' dashboard tab.
Do average resolution times match your service level agreements? Do certain categories have significantly different resolution times? Are there areas that can benefit from shift-left?
Sample of the 'Volume of Resolution Codes' dashboard tab.
Are resolution codes being accurately used? Are there trends in resolution codes? Are these codes providing sufficient information for problem management?

PHASE 3

Communicate Your Insights

This phase will walk you through the following activities:

  • 3.1.1 Review common recommendations
  • 3.2.1 Review ticket reports daily
  • 3.2.2 Incorporate ticket data into retrospectives and team updates
  • 3.2.3 Regularly review trends with business leaders
  • 3.2.4 Tell a story with your data

This phase involves the following participants:

  • Service Desk Manager
  • Service Desk Technicians
  • IT Managers

Step 3.1

Build Recommendations Based on Your Ticket Data

Activities
  • 3.1.1 Review common recommendations

This step will walk you through the following activities:

Review common recommendations as a first step to extracting insights from your own data.

This step involves the following participants:

  • Service Desk Manager
  • Service Desk Technicians

Outcomes of this step

You will gain an understanding of the common challenges with service desks and ticket analysis in general. See which ones apply to you to inform your ticket data analysis moving forward.

Review these common recommendations

  1. Fix your ticket categories
    Organize your ticket categorization scheme for proper routing and reporting.
  2. Focus more on self-service
    Self-service is essential to enable shift-left strategies. Focus on knowledgebase processes and portal ease of use.
  3. Update your service catalog
    Improve your service catalog, if necessary, to make it easy for end users to request services and for the service desk to provide those services.
  4. Direct volume toward other channels
    Walk-ups make it more difficult to properly log tickets and assign service desk resources. Drive volume to other channels to improve your ticket quality.
  5. Crosstrain Tier 1 on certain topics
    Tier 1 breadth of knowledge is essential to drive up first contact resolution.
  6. Build more automation
    Identify bottlenecks and challenges with your ticket data to streamline ticket handling and resolution.
  7. Revisit service level agreements
    Update your SLAs and/or SLOs to prioritize expectation management for your end users.
  8. Improve your data quality
    You can only analyze data that exists. Revisit your ticket-handling guidelines and more regularly check tickets to ensure they comply with those standards.

Optimize your processes and look for opportunities for automation

Leverage Info-Tech research to improve service desk processes

Review your service desk processes and tools for optimization opportunities:

  • Clearly establish ticket-handling guidelines.
  • Use ticket templates to reduce time spent entering tickets.
  • Document incident management and service request fulfillment workflows and eliminate any unnecessary steps.
  • Automate manual tasks wherever possible.
  • Build or improve a self-service portal with a knowledgebase to allow users to resolve their own issues, reducing incoming ticket volume to the service desk.
  • Optimize your internal knowledgebase to reduce time spent troubleshooting recurring issues.
  • Leverage AI capabilities to speed up ticket processing and resolution.

Standardize the Service Desk

This project will help you build and improve essential service desk processes, including incident management, request fulfillment, and knowledge management.

Optimize the Service Desk With a Shift-Left Strategy

This project will help you build a strategy to shift service support left to optimize your service desk operations and increase end-user satisfaction.

Step 3.2

Action and Communicate Your Ticket Data

Activities
  • 3.2.1 Review your ticket queues daily
  • 3.2.2 Incorporate ticket data into retrospectives and team status updates
  • 3.2.3 Regularly review trends with business leaders
  • 3.2.4 Tell a story with your data

This step will walk you through the following activities:

Organize your scrums to report on the metrics that will inform daily and monthly operations.

This step involves the following participants:

  • Service Desk Manager
  • Service Desk Technicians
  • IT Managers

Outcomes of this step

Use the dashboards and data to inform your daily and monthly scrums.

3.2.1 Review your ticket queues daily

Clean data is still useless if not used properly

  • The metrics you’ve chosen to measure and visualize in the previous step are useful for informing your day-to-day, week-to-week, and month-to-month strategies for the service desk and IT. Conduct scrums daily to action your dashboard data to help clear ticket queues.
  • Reference your dashboards daily with each IT team.
  • You need to have a dashboard of open tickets assigned to each team.

Review Daily

  • Ticket volume over the last day (look for spikes)
  • SLA breach risks/SLA breaches
  • Recurring incidents
  • Tickets open
  • Tickets handed over (confirmation of handover)

3.2.2 Incorporate ticket data into retrospectives and team status updates

Explain your metric spikes and trends

  • Hold weekly or monthly meetings to review the ticket trends selected during Phases 1 and 2 of this blueprint.
  • Review ticket spikes, identify seasonal trends, and discuss root causes (e.g. projects/changes going live, onboarding blitz).
  • Discuss any actions associated with spikes and seasonal trends (e.g. resource allocation, hiring, training).
  • You can incorporate other IT leaders or departments in this meeting as needed to discuss action items for improvement, quality assurance concerns, customer service concerns, and/or operating level agreement concerns.

Review Weekly/Monthly

  • Ticket volume
  • Ticket category by priority level over time
  • Tickets from different business groups, VIP groups, and different vertical levels
  • Tickets escalated, tickets that didn’t need to be escalated, tickets that were incorrectly escalated
  • Ticket priority levels over time
  • Most requested services
  • Tickets resolved by which group over time
  • Ability to meet SLAs and OLAs over time by different groups

3.2.3 Regularly review trends with business leaders

Use your data to help improve business relationships

Review the following with business leaders:

  • Volume of work done this past time cycle for the leader’s group
  • Trends and spikes in the data and possible explanations for them (note: get their input on the potential causes of trends)
  • Improvements you plan to execute within the service desk
  • Action items you need from the business leader

Use your data to show the value you provide to the group. Schedule quarterly meetings with the heads of different business groups to discuss the work that the service desk does for each group.

Show trends in incidents and service requests: “I see you have a spike in CRM tickets. I’ve been working with the CRM team to address this issue.”

3.2.4 Tell a story with your data

Effectively communicate with the business and leadership

  • With your visualized metrics, organize your story into a presentation for different stakeholder groups. You can use the Ticket Analysis Report as a starting point to provide data about:
    • Value provided by the service desk
    • Successes
    • Opportunities for Improvements
    • Current state of KPIs
  • Include information about the causes of data trends and actions you will take in response to the data.
  • For each of these themes, look at the metrics you’ve chosen to track and see which ones fit to tell the story. Let the data do the talking.
  • Consider supplementing the ticket data with data from other systems. For example, you can include data on transactional customer satisfaction surveys, knowledgebase utilization, and self-service utilization.

Sample of the Ticket Analysis Report.

Download the Ticket Analysis Report.

Ticket Analysis Report

Include the following information as you build your ticket analysis report:

  • Value Provided by the Service Desk
    Start with the value provided by the service desk to different areas of the business. Include information about first contact resolution, average resolution times, ticket volume (e.g. by category, priority, location, requestor).
  • Successes
    Successes is a general field that can include how process improvements have impacted the service desk or how initiatives have enhanced shift-left opportunities. Highlight any positive trends over time.
  • Opportunities for Improvement
    Let the data guide the conversation to where improvements can be made. Day-to-day ops, self-service tools, shifting work left from Tier 2, Tier 3, standardizing a non-standard service, and staffing adjustments are possibilities for this section.
  • Current State of KPIs
    Mean time to resolve, FCR, ticket volume, and end-user satisfaction are great KPIs to include as a starting point.

Sample of the Ticket Analysis Report.

Download the Ticket Analysis Report.

Summary of Accomplishment

Problem Solved

You now have a better understanding of how to action your service desk ticket data, including improvements to your current ticket templates for incidents and service requests.

You also have the data to craft a story to different stakeholder groups to celebrate the successes of the service desk and highlight possible improvements. Continue this exercise iteratively to continue improving the service desk.

Remember, ticket analysis is not a single event but an ongoing initiative. As you track, analyze, and action more data, you will find more improvements.

If you would like additional support, have our analysts guide you through other phases as part of an Info-Tech workshop.

Contact your account representative for more information.

workshops@infotech.com 1-888-670-8889

Additional Support

If you would like additional support, have our analysts guide you through other phases as part of an Info-Tech workshop.

Photo of Benedict Chang.

Contact your account representative for more information.

workshops@infotech.com 1-888-670-8889

To accelerate this project, engage your IT team in an Info-Tech workshop with an Info-Tech analyst team. Info-Tech analysts will join you and your team at your location or welcome you to Info-Tech’s historic Toronto office to participate in an innovative onsite workshop.

The following are sample activities that will be conducted by Info-Tech analysts with your team:

Sample of dashboards we saw earlier. Sample of the 'Ticket Analysis Report'.
Analyze your dashboards
An analyst will walk through the ticket data and dashboards with you and your team to help interpret the data and tailor improvements
Populate your ticket data report
Given the action items from this solution set, an analyst will help you craft a report to celebrate the successes and highlight needed improvements in the service desk.

Related Info-Tech Research

Optimize the Service Desk With a Shift-Left Strategy

The best type of service desk ticket is the one that doesn’t exist.

Incident & Problem Management

Don’t let persistent problems govern your department.

Design & Build a User-Facing Service Catalog

Improve user satisfaction with IT with a convenient menu-like catalog.

Bibliography

Bayes, Scarlett. “ITSM: 2021 & Beyond.” Service Desk Institute, 2021. Web.

“Benchmarking Report v.9.” Service Desk Institute, 17 Jan. 2020. Web.

Bennett, Micah. “The 9 Help Desk Metrics That Should Guide Your Customer Support.” Zapier, 3 Dec. 2015. Web.

“Global State of Customer Service: The transformation of customer service from 2015 to present day.” Microsoft Dynamics 365, Microsoft, 2020. Web.

Goodey, Ben. “How to Manually Analyze Support Tickets.” SentiSum, 26 July 2021. Web.

Jadhav, Megha. “Four Metrics to Analyze When Using Ticketing Software.” Vision Helpdesk Blog, 21 Mar. 2016. Web.

Knaflic, Cole Nussbaumer. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley, 2015.

Li, Ta Hsin, et al. “Incident Ticket Analytics for IT Application Management Services.” 2014 IEEE International Conference on Services Computing, 2014. Web.

Olson, Sarah. “10 Help Desk Metrics for Service Desks and Internal Help Desks.” Zendesk Blog, Sept. 2021. Web.

Paramesh, S.P., et al. “Classifying the Unstructured IT Service Desk Tickets Using Ensemble of Classifiers.” 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), 2018. Web.

Volini, Erica, et al. “2021 Global Human Capital Trends: Special Report.” Deloitte Insights, 21 July 2021. Web.

“What Kind of Analysis You Can Perform on a Ticket Management System.” Commence, 3 Dec. 2019. Web.

INFO-TECH RESEARCH GROUP

About Info-Tech

Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.

We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

What Is a Blueprint?

A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.

Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.

Need Extra Help?
Speak With An Analyst

Get the help you need in this 3-phase advisory process. You'll receive 4 touchpoints with our researchers, all included in your membership.

Guided Implementation #1 - Import your ticket data
  • Call #1 - Scope requirements, objectives, and your specific challenges. Enter your data into the tool.

Guided Implementation #2 - Analyze your ticket data
  • Call #1 - Assess the current state across the different dashboards.

Guided Implementation #3 - Communicate your insights
  • Call #1 - Identify improvements and insights to include in the communication report.
  • Call #2 - Review the Ticket Analysis Report.

Authors

Benedict Chang

Allison Kinnaird

Ken Weston

Contributors

  • 17 anonymous contributors
  • 20 anonymous survey respondents
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