Help Desk Ticket Categories Best Practices & List

Sharad Khandelwal
SentiSum CEO & Customer Service Expert
Understand your customer’s problems and get actionable insights
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Our AI team builds ticket categorisation algorithms for 100s of top brands. It’s our speciality.

Over the years working closely with customer service teams, we’ve seen mistakes and mishaps as well as huge triumphs. 

The secret? There’s a few.

For this article, we’re going to share the good and the bad of our learnings over the years to bring you our experience-led help desk ticket categorisation best practices.

Contents:
  • Why categorize help desk tickets
  • What most companies get wrong
  • 8 help desk ticket categories best practices
  • Examples of how other companies are doing it

Why You Should Categorize Help Desk Tickets

In our recent article, the importance of customer service analytics, we discussed a number of impactful reasons to understand and use support conversations in your business. 

We went as far as to call them your “most valuable customer experience assets”.

This entire benefit is predicated on you having a high quality and consistent categorisation process in place.

Without a tagging taxonomy in place that:

  • Is built properly so it helps you meet the right goals (for your team and others)
  • Is consistently and accurately applied

None of it works. No CX insights. No service improvements. No reduction of costs. No revenue boost.

Building a best practice help desk ticket categorization process is the lynch pin of your customer service analytics success. It’s your foundation.

8 Help Desk Ticket Categories Best Practices

There’s lots to get through here before you start categorizing your help desk conversations. Let’s start at the beginning—your goals.

1. Identify your goals—and make it a team sport

Every well done project starts with a ‘why’. The outcome you want is everything to knowing what to build.

Start by bringing other teams together to figure out the ideal end result. Jenny Dempsey, CX Manager at Apeel, said it well when I talked to her recently,

“I typically start by talking to other teams to understand what needs to be measured outside of what I/ my team want to measure. I learned early on that if I just measure what I want, I don't have access to data that other teams need.”

Most often, especially if you want to use support insights to help product, operations or CX improvements, you must rope in those teams. For example, you might want to align some of your categorizations with their most important ongoing projects—feeding them insight that makes their lives easier.

One example of this comes from an interview with Aistė Sobutienė, customer support director at Vinted. In the interview, we learned how Vinted had built customer service listening into the product development lifecycle. When a new product was launched, initial feedback was gathered from conversations so the product could be quickly iterated—a perfect use case for temporary categories to track feature feedback. 

Without being on the same page as her product team in the planning stages, Aiste would likely have collected the wrong insights and they probably wouldn’t have listened.

The UK Government recently ran a ticket categorization project, they kicked off with an internal exercise with their product teams:

“In these sessions we printed off a random sample of tickets and gave each team member 5. Everyone was asked to write on Post-Its what things they’d want to capture about the ticket, for example the type of request or the date it came in.”

That’s a great way to get started, but ultimately you need to be in control of determining which tags to use.

2. Build a hierarchical taxonomy

We explain the difference between a single layered taxonomy and a multi-layered one here

Ticket tagging taxonomy

There are pros and cons to both, but ultimately if you have the resourcing then a hierarchical taxonomy is infinitely more useful. It gets you closer to the granular insights with less further root cause analyses.

Jenny Dempsey weighed-in with her best practice here, too, agreeing with us that hierarchical categorization has its benefits:

“I get very specific and create High Level Tags + Specific Reason Tags, so each ticket will have multiple tags: for example, if there is a product issue, we'll use the "product_issue" tag + a reason tag like, "shipping_delay". This helps me see how many total of one type of issue + breaking it down into smaller chunks.”

This adds complexity to your ticket categories, which brings us to point three.

3. There’s a balance to be had

“Think about your tagging structure carefully; there’s a balance between too generic and too specific” - Kirsty Pinner, Chief Product Officer at SentiSum

When building your ticket categorization taxonomy, there’s risk in going to either extreme. 

In the past, customer service leaders have come to us with 400 or 500 different categories. Over time they’ve expanded with different needs and new team members, and ultimately they’ve gotten lost in the sheer volume of them. We typically streamline their taxonomy down to 30-50 tags which cover the main problems, questions and feedback in enough detail to be useful.

Tag “bloat” is a huge problem if your agents are manually categorizing conversations. Your agents probably categorize in about three seconds, if you have 500 tags they’ll likely choose the first relevant one they see and move on. Even if it’s not the best or most accurate choice.

Here’s an example of the UK Government’s (mentioned above) categories. Imagine this but 500 of them?!

help desk ticket categories

That being said—if you’re too broad and high-level, using only five tags like “Complaint”, “Request”, and “Question”...what use is that to anyone?

The end result still requires a deep dive, and if you’ve got 100,000 help desk interactions a week like our customer Gousto, that’s an impossible task.

4. Be intentional with the names of each categories

What you name your tags matters. Call them what agents would expect them to be called so they’re easy to find, easy to understand and easy to apply.

If your category for “first time login issue” is called “FTLI”, then an agent is unlikely to find it quickly.

Here are four examples of great category names:

  1. Damaged_package
  2. Size_small
  3. Refund_requested
  4. Payment_failed_paypal

Put the most important topic at the front (e.g Size) and the detail later on (e.g. Size_small), that makes it easier to spot.

5. Avoid general tags—they’ll probably become catch-alls

When we dig into our clients existing tagging systems, we find that overly general tags are often a key cause of inaccurate insights.

Generally applicable categories become catch-alls, for example something like “packaging” which could apply to several different issues the customer faced with their packaging. They often get used willy-nilly by agents in a rush to move onto their next ticket.

“Avoid general tags like ‘packaging’. They tend to become a catch-all for any kind of issue, so agents apply them quickly and move on.” - Kirsty Pinner, Chief Product Officer at SentiSum.

We strongly recommend have tier 1 (high level topic tags) and tier 2 (specific subtopics tags) in place to ensure agents go deeper with their tagging.

Artificial intelligence-powered tagging doesn't run into this issue because it never tires or rushes. As long the taxonomy is set up correctly, AI will apply tags consistently every time. Read more here.

6. Track sentiment and other interesting elements

So far, we’ve been talking about topic tagging—i.e. Categorizing conversations based on the reason for contact.

But, there are TONS of different ways to categorize. For example, if you categorize by sentiment (or do a topic-based sentiment analysis) you can begin to understand which topics drive the most negativity.

7. Create a knowledge base

If you’re going down either the manual or automated tagging route, a knowledge-base is critical documentation to get in place.

Make sure yours clearly says what each category means, where it’s supposed to be applied and why it’s important. This nuance will come in handy when new agents join or existing agents forget, or if any external teams want to sift through the data themselves.

Read our guide on 'how to create a knowledge base' here.

8. Iterate your categories regularly—but keep them clean

You’ll quickly learn what’s useful and what’s not, and new needs will arise as new bugs appear or new products are released.

It’s important that you keep an element of flexibility and allow your team to suggest new tags.

We use machine learning at SentiSum to identify a new topic that hasn’t been seen before—the speed at which it works means our customers can fight flames before they become fires.

Help Desk Ticket Categorization Project Examples

There’s no better way to learn than by watching the innovative ways other companies categorize tickets.

Here are a few great examples:

  1. James Villas, the global holiday rental company, auto-categorizes tickets based on topics they know are urgent for customers. Using those tags, they triage particular conversations to the “priority” queue—response time reduced by 46%. Read the case study here.
  2. Gousto, the British meal-kit delivery giant, uses ticket categorization to reduce lead time on CX improvement projects. They tag based on common topics, like which foods are most mentioned as an issue when the customer receives their meal-kit. This data helps prioritize which issues their operations team should focus on first. Read the case study here.
  3. Organic Basics, the sustainable underwear brand, categorizes inbound tickets based on sales funnel depth. If they know a contact is coming from someone highly likely to make a purchase soon, they categorize it and prioritize it in the queue. Listen to the podcast episode here.
  4. Paul Tucket, Head of Support at EveryoneSocial, shared his internal guide for coaching agents about ticket categorization. He says when to add tags and when not to—read our favorite part of his guide here.

What Do Most Companies Get Wrong With Ticket Categorization?

This is a quick but relevant interlude. I asked the above question to SentiSum’s Chief of Product, Kirsty Pinner, recently and her answers were directly relevant to this article.

She said, most companies:

“Get the how wrong. 1. They report on ‘reasons for contact’ through manual categorization which almost always brings inaccurate or subjective results. 2. They let customers categorize support tickets with self-reported ‘reasons for contact’. This causes an inherent distrust in the analysis, we see many support managers hesitate to share the results with other teams because they themselves don’t trust this data to back up business decisions.”

Kirsty’s point is important to note up front. However you decide to build a categorisation taxonomy, if the person or tool applying the tags gets it wrong or isn’t detailed enough, then your entire end analyses are nullified.

If you’re going to invest in support conversations as a source of company improvement—it’s worth getting right. (AI categorisation is your best bet here)

Automate Your Help Desk Categorization With AI

SentiSum customers use our platform to automatically surface important topics and customer sentiments from customer service conversations. Every customer gets a custom data-driven ticket categorization taxonomy that’s continuously maintained and iterated by our team according to your needs. Our AI algorithm then applies it accurately and consistently in real-time, and the insights are presented in a highly visual and easy to deep-dive dashboard.

Book a product tour with us here to see how we can help you.

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Customer Sentiment Analysis FAQs

How is sentiment analysis useful? 5 examples of customer sentiment analysis

Here are 5 ways sentiment analysis is useful in customer service:

Prioritize customer issues:
Sentiment analysis can help businesses quickly identify and prioritize customer issues based on the emotional tone of their messages. This can enable customer service agents to respond promptly to unhappy customers and resolve issues before they escalate.

Personalize customer interactions: By detecting the emotional tone of a customer's message, sentiment analysis can help businesses tailor their responses to the customer's needs. For example, if a customer is expressing frustration, a customer service agent can respond with empathy and offer a solution to address the issue.

Improve customer experience: By providing personalized and efficient customer service, sentiment analysis can help improve the overall customer experience. Customers who receive prompt and effective solutions to their issues are more likely to remain loyal to a business and recommend it to others.

Analyze customer feedback: Sentiment analysis can be used to analyze large volumes of customer feedback to identify trends and patterns. This can help businesses identify areas for improvement and make data-driven decisions to improve their products and services.

Monitor brand reputation: Sentiment analysis can be used to monitor online mentions of a brand or product to detect negative sentiment and address issues before they become a larger problem. This can help businesses protect their brand reputation and maintain customer loyalty.

What is real time sentiment analysis in customer service?

Real-time sentiment analysis in customer service refers to the process of analyzing the emotional tone of customer messages or conversations as they are happening, in real-time. This enables businesses to quickly identify and respond to customer issues, prioritize certain conversations, and personalize interactions based on the customer's emotional state.Here are some examples and analogies to help understand real-time sentiment analysis in customer service:

Real-time monitoring: Real-time sentiment analysis involves monitoring customer messages or conversations as they are happening, in real-time. This is similar to a security guard monitoring a building in real-time for any signs of danger or security threats. Just as the security guard can quickly respond to any threats they detect, businesses can quickly respond to customer issues as they are identified.

Prompt customer service: Real-time sentiment analysis allows businesses to quickly identify and respond to customer issues before they become larger problems. For example, if a customer is expressing frustration about a product issue, real-time sentiment analysis can alert customer service agents to prioritize that customer's message for a quick response. This can help the business resolve the issue before it leads to a negative online review or loss of customers.

Personalized interactions: Real-time sentiment analysis can help businesses personalize their interactions with customers based on their emotional state. For example, if a customer is expressing happiness about a recent purchase, a customer service agent can respond with enthusiasm and congratulations. Conversely, if a customer is expressing frustration or anger, a customer service agent can respond with empathy and an apology. This personalized approach can help businesses build stronger relationships with their customers.

Improved customer experience: Real-time sentiment analysis can help improve the overall customer experience by providing prompt and effective customer service. Customers who receive quick and effective solutions to their issues are more likely to remain loyal to a business and recommend it to others.

Continuous monitoring: Real-time sentiment analysis can be used to continuously monitor customer messages or conversations, providing businesses with a wealth of data that can be used to improve their products and services. For example, if customers are expressing negative sentiment about a particular product feature, a business can use that information to make improvements and better meet the needs of its customers.

Overall, real-time sentiment analysis is a valuable tool in customer service that can help businesses quickly respond to customer issues, personalize interactions, and improve the overall customer experience.

What type of information do companies analyze when conducting sentiment analysis?

Here are the two overarching areas of customer information you can include in your sentiment analysis:

Text data: Sentiment analysis of text data is like analyzing a written letter to detect the writer's emotional tone. By detecting the emotional tone of customer feedback, customer service chats, reviews, or social media posts, companies can gain valuable insights into how their customers feel about their products or services.

Voice data: Sentiment analysis of voice data is like interpreting a person's tone of voice during a conversation to detect their emotional state. By analyzing phone calls or video chats with customers, companies can detect the emotional cues in a customer's tone of voice, such as frustration or anger, and provide a more personalized response.

What are the main goals of sentiment analysis?

The main goals of sentiment analysis are to gain insights into customer emotions and opinions, and to use these insights to improve customer satisfaction and loyalty. Here are some examples of the main goals of sentiment analysis:

Understand customer feedback: One of the main goals of sentiment analysis is to understand customer feedback and opinions about a product, service, or brand. By analyzing the emotional tone of customer feedback, companies can gain insights into what customers like and dislike about their products or services, and make improvements accordingly.

Improve customer experience: Another goal of sentiment analysis is to improve the overall customer experience. By understanding customer emotions and opinions, companies can address any issues or pain points and provide a better customer experience. For example, if sentiment analysis reveals that customers are frequently complaining about long wait times, the company can take steps to reduce the wait times and improve the customer experience.

Enhance customer engagement: Sentiment analysis can also be used to enhance customer engagement by identifying opportunities for positive interactions with customers. For example, if sentiment analysis reveals that customers are expressing positive emotions towards a new product or service, the company can engage with those customers to learn more about what they like and how they can improve the product or service even further.

Prevent negative customer experiences: Another goal of sentiment analysis is to prevent negative customer experiences by identifying potential issues and addressing them proactively. For example, if sentiment analysis reveals that customers are frequently complaining about a specific product feature, the company can address the issue before it becomes a bigger problem and affects customer satisfaction.

Monitor brand reputation: Sentiment analysis can also be used to monitor brand reputation by tracking what customers are saying about a brand, product or service on social media, review sites, and other online platforms. This information can be used to prevent a potential PR crisis and maintain a positive brand reputation.

Want to learn more about how SentiSum automates your customer sentiment analysis? Book a meeting with our team here.

Help Desk Ticket Categories Best Practices & List

Sharad Khandelwal
Sharad Khandelwal
CEO & Co-founder at SentiSum, Expert in AI Analytics

Our AI team builds ticket categorisation algorithms for 100s of top brands. It’s our speciality.

Over the years working closely with customer service teams, we’ve seen mistakes and mishaps as well as huge triumphs. 

The secret? There’s a few.

For this article, we’re going to share the good and the bad of our learnings over the years to bring you our experience-led help desk ticket categorisation best practices.

Contents:
  • Why categorize help desk tickets
  • What most companies get wrong
  • 8 help desk ticket categories best practices
  • Examples of how other companies are doing it

Why You Should Categorize Help Desk Tickets

In our recent article, the importance of customer service analytics, we discussed a number of impactful reasons to understand and use support conversations in your business. 

We went as far as to call them your “most valuable customer experience assets”.

This entire benefit is predicated on you having a high quality and consistent categorisation process in place.

Without a tagging taxonomy in place that:

  • Is built properly so it helps you meet the right goals (for your team and others)
  • Is consistently and accurately applied

None of it works. No CX insights. No service improvements. No reduction of costs. No revenue boost.

Building a best practice help desk ticket categorization process is the lynch pin of your customer service analytics success. It’s your foundation.

8 Help Desk Ticket Categories Best Practices

There’s lots to get through here before you start categorizing your help desk conversations. Let’s start at the beginning—your goals.

1. Identify your goals—and make it a team sport

Every well done project starts with a ‘why’. The outcome you want is everything to knowing what to build.

Start by bringing other teams together to figure out the ideal end result. Jenny Dempsey, CX Manager at Apeel, said it well when I talked to her recently,

“I typically start by talking to other teams to understand what needs to be measured outside of what I/ my team want to measure. I learned early on that if I just measure what I want, I don't have access to data that other teams need.”

Most often, especially if you want to use support insights to help product, operations or CX improvements, you must rope in those teams. For example, you might want to align some of your categorizations with their most important ongoing projects—feeding them insight that makes their lives easier.

One example of this comes from an interview with Aistė Sobutienė, customer support director at Vinted. In the interview, we learned how Vinted had built customer service listening into the product development lifecycle. When a new product was launched, initial feedback was gathered from conversations so the product could be quickly iterated—a perfect use case for temporary categories to track feature feedback. 

Without being on the same page as her product team in the planning stages, Aiste would likely have collected the wrong insights and they probably wouldn’t have listened.

The UK Government recently ran a ticket categorization project, they kicked off with an internal exercise with their product teams:

“In these sessions we printed off a random sample of tickets and gave each team member 5. Everyone was asked to write on Post-Its what things they’d want to capture about the ticket, for example the type of request or the date it came in.”

That’s a great way to get started, but ultimately you need to be in control of determining which tags to use.

2. Build a hierarchical taxonomy

We explain the difference between a single layered taxonomy and a multi-layered one here

Ticket tagging taxonomy

There are pros and cons to both, but ultimately if you have the resourcing then a hierarchical taxonomy is infinitely more useful. It gets you closer to the granular insights with less further root cause analyses.

Jenny Dempsey weighed-in with her best practice here, too, agreeing with us that hierarchical categorization has its benefits:

“I get very specific and create High Level Tags + Specific Reason Tags, so each ticket will have multiple tags: for example, if there is a product issue, we'll use the "product_issue" tag + a reason tag like, "shipping_delay". This helps me see how many total of one type of issue + breaking it down into smaller chunks.”

This adds complexity to your ticket categories, which brings us to point three.

3. There’s a balance to be had

“Think about your tagging structure carefully; there’s a balance between too generic and too specific” - Kirsty Pinner, Chief Product Officer at SentiSum

When building your ticket categorization taxonomy, there’s risk in going to either extreme. 

In the past, customer service leaders have come to us with 400 or 500 different categories. Over time they’ve expanded with different needs and new team members, and ultimately they’ve gotten lost in the sheer volume of them. We typically streamline their taxonomy down to 30-50 tags which cover the main problems, questions and feedback in enough detail to be useful.

Tag “bloat” is a huge problem if your agents are manually categorizing conversations. Your agents probably categorize in about three seconds, if you have 500 tags they’ll likely choose the first relevant one they see and move on. Even if it’s not the best or most accurate choice.

Here’s an example of the UK Government’s (mentioned above) categories. Imagine this but 500 of them?!

help desk ticket categories

That being said—if you’re too broad and high-level, using only five tags like “Complaint”, “Request”, and “Question”...what use is that to anyone?

The end result still requires a deep dive, and if you’ve got 100,000 help desk interactions a week like our customer Gousto, that’s an impossible task.

4. Be intentional with the names of each categories

What you name your tags matters. Call them what agents would expect them to be called so they’re easy to find, easy to understand and easy to apply.

If your category for “first time login issue” is called “FTLI”, then an agent is unlikely to find it quickly.

Here are four examples of great category names:

  1. Damaged_package
  2. Size_small
  3. Refund_requested
  4. Payment_failed_paypal

Put the most important topic at the front (e.g Size) and the detail later on (e.g. Size_small), that makes it easier to spot.

5. Avoid general tags—they’ll probably become catch-alls

When we dig into our clients existing tagging systems, we find that overly general tags are often a key cause of inaccurate insights.

Generally applicable categories become catch-alls, for example something like “packaging” which could apply to several different issues the customer faced with their packaging. They often get used willy-nilly by agents in a rush to move onto their next ticket.

“Avoid general tags like ‘packaging’. They tend to become a catch-all for any kind of issue, so agents apply them quickly and move on.” - Kirsty Pinner, Chief Product Officer at SentiSum.

We strongly recommend have tier 1 (high level topic tags) and tier 2 (specific subtopics tags) in place to ensure agents go deeper with their tagging.

Artificial intelligence-powered tagging doesn't run into this issue because it never tires or rushes. As long the taxonomy is set up correctly, AI will apply tags consistently every time. Read more here.

6. Track sentiment and other interesting elements

So far, we’ve been talking about topic tagging—i.e. Categorizing conversations based on the reason for contact.

But, there are TONS of different ways to categorize. For example, if you categorize by sentiment (or do a topic-based sentiment analysis) you can begin to understand which topics drive the most negativity.

7. Create a knowledge base

If you’re going down either the manual or automated tagging route, a knowledge-base is critical documentation to get in place.

Make sure yours clearly says what each category means, where it’s supposed to be applied and why it’s important. This nuance will come in handy when new agents join or existing agents forget, or if any external teams want to sift through the data themselves.

Read our guide on 'how to create a knowledge base' here.

8. Iterate your categories regularly—but keep them clean

You’ll quickly learn what’s useful and what’s not, and new needs will arise as new bugs appear or new products are released.

It’s important that you keep an element of flexibility and allow your team to suggest new tags.

We use machine learning at SentiSum to identify a new topic that hasn’t been seen before—the speed at which it works means our customers can fight flames before they become fires.

Help Desk Ticket Categorization Project Examples

There’s no better way to learn than by watching the innovative ways other companies categorize tickets.

Here are a few great examples:

  1. James Villas, the global holiday rental company, auto-categorizes tickets based on topics they know are urgent for customers. Using those tags, they triage particular conversations to the “priority” queue—response time reduced by 46%. Read the case study here.
  2. Gousto, the British meal-kit delivery giant, uses ticket categorization to reduce lead time on CX improvement projects. They tag based on common topics, like which foods are most mentioned as an issue when the customer receives their meal-kit. This data helps prioritize which issues their operations team should focus on first. Read the case study here.
  3. Organic Basics, the sustainable underwear brand, categorizes inbound tickets based on sales funnel depth. If they know a contact is coming from someone highly likely to make a purchase soon, they categorize it and prioritize it in the queue. Listen to the podcast episode here.
  4. Paul Tucket, Head of Support at EveryoneSocial, shared his internal guide for coaching agents about ticket categorization. He says when to add tags and when not to—read our favorite part of his guide here.

What Do Most Companies Get Wrong With Ticket Categorization?

This is a quick but relevant interlude. I asked the above question to SentiSum’s Chief of Product, Kirsty Pinner, recently and her answers were directly relevant to this article.

She said, most companies:

“Get the how wrong. 1. They report on ‘reasons for contact’ through manual categorization which almost always brings inaccurate or subjective results. 2. They let customers categorize support tickets with self-reported ‘reasons for contact’. This causes an inherent distrust in the analysis, we see many support managers hesitate to share the results with other teams because they themselves don’t trust this data to back up business decisions.”

Kirsty’s point is important to note up front. However you decide to build a categorisation taxonomy, if the person or tool applying the tags gets it wrong or isn’t detailed enough, then your entire end analyses are nullified.

If you’re going to invest in support conversations as a source of company improvement—it’s worth getting right. (AI categorisation is your best bet here)

Automate Your Help Desk Categorization With AI

SentiSum customers use our platform to automatically surface important topics and customer sentiments from customer service conversations. Every customer gets a custom data-driven ticket categorization taxonomy that’s continuously maintained and iterated by our team according to your needs. Our AI algorithm then applies it accurately and consistently in real-time, and the insights are presented in a highly visual and easy to deep-dive dashboard.

Book a product tour with us here to see how we can help you.

Join a community of 2139+ customer-focused professionals and receive bi-weekly articles, podcasts, webinars, and more!