Zendesk ticket analysis: The best way to Analyze your Support Tickets

What is ticket analysis in Zendesk?

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The Zendesk ticket analysis examines customer support interactions within your Zendesk platform to uncover trends, customer sentiment, and issues expressed in customers' tickets. This process turns routine support feedback into strategic insights so you can improve customer service quality and efficiency. To properly read and tag Zendesk ticket data, analysis tools typically leverage one of three types of natural language processing:

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1. Keyword extraction: Basic keyword extraction tools will automatically tag ticket text based on specific keywords (e.g., adding the "refund" tag if that word appears in the conversation). However, it often fails to capture the true context or variations in how customers express themselves. This may lead to inaccuracies and superficial categorizations that don't reflect how your customers feel.

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2. Rule-based NLP: This approach uses a library of language rules to tag terms with similar meanings (for instance, both "liberty" and "freedom" might be tagged under a common theme). While it's more advanced than keyword extraction, rule-based NLP's effectiveness is limited by the extent of its rules library. Tools that use this type of NLP may not fully understand nuanced expressions relevant to specific business contexts.

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3. Machine learning-based NLP: This NLP model understands speech and text similarly to humans. After digesting a dataset being trained in it, it uses statistical inference to carry that knowledge into new environments it's never seen before. For example, it can identify and infer the meaning of misspellings, omitted words, and new words like slang by itself. Machine Learning learns the patterns between phrases and sentences and constantly optimizes itself to improve accuracy over time. In the same way, our Zendesk ticket analysis software, ClientZen, incorporates machine-learning-based NLP based on your Zendesk and custom support ticket data.

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Why Zendesk Ticket Analysis is help your ticket support

Zendesk is one of the most powerful tools for managing customer service interactions. But while it’s great for organizing and resolving tickets, it’s not designed to dig deep into the content of those conversations. When you’re handling hundreds or thousands of tickets, finding out which issues are most common or which areas of your product are causing the most frustration can be a nightmare. And let’s face it, even the best teams don’t have time to manually analyze every single ticket.

That’s the beauty of Zendesk Ticket Analysis Software's. It acts as a set of extra eyes (and a brain) to help you understand what’s really going on with your customers at scale. By pulling together trends and identifying recurring pain points, Zendesk Ticket Analysis Software's can help you turn raw ticket data into a roadmap for better customer experience.

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What options you have to analyze your Zendesk tickets

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You can analyze Zendesk support ticket data in three different ways:

1. Zendesk’s built-in tool: Zendesk's tool uses a basic rule-based system, resulting in simple categorizations.
2. Advanced Zendesk ticket analysis software: Tools like ClientZen utilize NLP and machine learning to recognize nuances and contextual meanings without predefined rules. This provides high accuracy and detailed categorization for deeper and more useful insights.

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Zendesk's Built-in Ticket analysis - Best approach for Basic Insights

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Zendesk's automated tagging system classifies tickets using a basic rule-based NLP system. It applies tags based on specific keywords detected in the customer's query.

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This approach is good for a high-level analysis or when you have just a few dozen or hundreds of support tickets in a month because your agent can handle this amount of data and manually check if the system labels the specific ticket correctly or not.

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Manual review while using Zendesk built-in tool? Yes, you've heard it right; it is more than essential because Zendesk's tool uses a standard rule-based NLP system, and that means that your findings will be more like high-level insights, and, because of this, your action points as well.

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To manually tag tickets, follow these steps:

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1. Create or edit a ticket.

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2. In the "Tags" section, enter new tags separated by a space. As you type the tag, keep the following in mind:

  - If the tag you are typing does not exist, pressing "Enter" will create a new one.

  - If the tag you are typing exists, autocomplete will display suggested tags for you to choose from.

  - You can use only alphanumeric characters, dash, underscore, colon, and the forward slash.

  - Special characters such as #, @, or ! cannot be used in tags. If you try to add tags with special characters, they will disappear when the ticket is updated.

  - Zendesk supports UTF-8 (Unicode), allowing all languages supported by Zendesk to be added to tags.

  - You can create a tag with more than one word if the words are connected with an underscore.

(There is no limit on the number of tags for a ticket, but the "Tags" field has a character limit of 5096. Once you reach this limit, you will no longer be able to add more tags. Existing tags will not be automatically removed).

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3. Click "Submit" to create or update the ticket.

Manual review while using Zendesk built-in tool? Yes, you've heard it right; it is more than essential because Zendesk's tool uses a standard rule-based NLP system, and that means that your findings will be more like high-level insights, and, because of this, your action points as well.

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Before using this analysis approach, it's important to be aware of a few things. The complex nature of customer support interactions, such as spelling errors, grammatical mistakes, and varied expression styles, can make it difficult for automatic tagging to fully understand customer issues. This can lead to:

• Incorrect tags: Tickets are often mislabeled because the system struggles to understand context or variations in language.
• Generic and/or high-level tags: The tool's assigned tags may not always specify the underlying reasons for customer contact, which may require additional manual analysis.
• Manual data handling: Even if Zendesk tags your tickets correctly, you may still need to sort through the data for more precise insights manually.

If your team deals with thousands of tickets every day, manually tagging each one can be extremely tedious and time-consuming. Instead, agents should focus on solving customer issues and improving satisfaction. If you prefer to automatize your support ticket analysis the next approach is for you.

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Zendesk ticket analysis Software - The best approach for quick, easy, granular and accurate insights

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Our ticket analysis software, ClientZen, uses machine learning-based NLP to accurately categorize support tickets. It can handle tickets from various channels such as emails, chats, reviews, surveys, messages, you name it. This comprehensive approach provides a better understanding of customer needs, enabling you to enhance your services and build customer loyalty.

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With ClientZen, you can expect:

- High accuracy tags: Our machine learning algorithms can detect context, variations, and nuances in customer language, resulting in more precise tagging.

- Specific reasons for contact tags: ClientZen assigns detailed tags such as "invoice receipt delay" instead of broad categories like "invoice issue," offering direct insights into customer problems.

- Insights into customer sentiment: ClientZen automatically assigns sentiment tags to support tickets (positive, negative, or neutral), a feature not available in Zendesk's built-in tool.

- Real-time automatic tagging: Support queries are immediately categorized in real-time as they enter the queue, providing efficient and accurate ticket management.

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Additionally, our Mantra AI feature offers quick, meaningful answers on specific customer support topics. It can also address any questions or queries related to support tickets, allowing you to gain deeper insights from your data analysis.

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ClientZen's taxonomy and tagging hierarchy provide more insightful and actionable ticket analysis. While Zendesk might tag an issue as "missing item," ClientZen tags specific subtopics like the product involved, along with its top-level categorization tag. This hierarchy enables your support team to understand both the broad issue overview and the deeper root cause at a glance.

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How to Perform Sentiment Analysis on Zendesk Support Tickets

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Step 1: Log in to ClientZen

First, ensure you are logged into your ClientZen account.

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Step 2: Connect Zendesk Support as a Data Source

1. Navigate to your ClientZen dashboard.
2. From the dashboard, click on “Sources” located in the navigation menu.
3. In the Sources section, locate Zendesk Support from the list of available data sources.
4. Click on Zendesk Support.
5. You’ll be prompted to add your Zendesk support domain. Enter the domain name associated with your Zendesk account.
6. Click “Connect” to start syncing your customer support tickets.

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Note: The syncing process might take a few seconds. Once completed, you’ll be able to proceed with analyzing your Zendesk data.

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Step 3: Filtering Zendesk Data for Sentiment Analysis

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1. After your Zendesk support tickets have been synced, navigate to the “Discovery” section in ClientZen.
2. To focus specifically on your Zendesk data, we’ll add a filter:
   • Click on “Filter”.
   • Choose “Sources” from the dropdown.
   • Select Zendesk to filter the data source.

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At this point, your Zendesk support tickets are now being filtered, but don’t worry if no data appears yet.

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Step 4: Adjust the Date Range (If Necessary)

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By default, ClientZen filters data for the last 3 months. If your Zendesk support tickets are older than this, you won’t see any data initially.

1. Click on the date range filter to adjust it.
2. Select a broader time frame, such as “Last 6 months” or “Custom” to include older tickets.

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Once the date range is adjusted, you should see your customer support tickets appear in the data view.

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Step 5: Save Your Data Segment

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Now that you have filtered the Zendesk data, it’s time to save this specific view as a segment for future reference.

1. Click on “Save Segment”.
2. Name your segment (e.g., “Zendesk Sentiment Analysis”).
3. You can also add categories or tags to further classify this segment.
4. Click “Save” to store this custom segment.

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Step 6: Perform Sentiment Analysis

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Now, let’s analyze the sentiment of the customer feedback:

1. Look at the sentiment breakdown for your tickets, which will show neutral, positive, and negative sentiment.
2. For more specific insights:
   • Change the filters to view only negative or positive sentiment if you’re interested in particular feedback types.

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In our example, after adjusting the time frame, we found both neutral and negative sentiments from the support interactions in the past 6 months.

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Step 7: Review and Interpret the Results

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Once you’ve performed the sentiment analysis, you can:

• Dive deeper into specific feedback.
• Understand common pain points or positive experiences.
• Identify patterns in customer satisfaction or dissatisfaction.

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This can be incredibly valuable for improving your customer service processes and addressing recurring issues.

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4 Unexpected Ways Your Zendesk Ticket Analysis Can Drive Change & Improvement

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1. Handling Growing Support Volumes Smoothly

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As your business scales, so do the number of customer support tickets. It’s common for support teams to struggle with managing an ever-growing workload without sacrificing quality. But with Zendesk ticket analysis and tools like ClientZen, you can make this process far more manageable.

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For example, Plannable saw a major spike in support tickets as their user base expanded. By integrating ClientZen, they reduced the manual workload by 15-20%, which allowed their team to handle the extra volume without missing a beat. The automated analysis took care of the heavy lifting, meaning they could keep up with demand while maintaining great service.

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In a similar situation, PrestaShop, which gets over 150,000 pieces of feedback a year, was relying on a tiny two-person team to handle it all. With ClientZen's help, they managed a workload that would usually require a much larger team, proving that automation can make a small team just as effective as a big one.

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2. Giving Customer Service Agents More Time to Help Customers

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Zendesk ticket analysis doesn’t just organize your feedback—it can also give your customer service team more breathing room. By automating repetitive tasks, you free up your agents to focus on what really matters: solving problems and improving the customer experience.

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Take Plannable, for example. Automating parts of the ticket analysis gave their team more time to tackle customer feature requests, which led to a 30% increase in positive feedback about those new features in just six months. It’s a clear win: less time on busywork means more time making customers happy.

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PrestaShop also saw major benefits. With their ticket analysis automated, their small Voice of the Customer (VoC) team could focus on bigger picture issues, like fixing bottlenecks in their product registration process. This proactive approach meant fewer tickets related to registration problems, freeing up their customer service team to focus on other important tasks.

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3. Spotting and Fixing Negative Sentiment Early

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One of the best things about using ClientZen to analyze Zendesk tickets is how quickly it can spot negative sentiment and bring key problems to your attention. Instead of spending hours digging through feedback manually, you get real-time insights into what’s going wrong.

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For example, PrestaShop used ClientZen to catch an issue with their product registration process. The sentiment analysis flagged it, allowing the team to address it before it snowballed into a bigger problem. This kind of instant feedback means you can fix issues fast and keep the customer experience running smoothly.

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Plus, it’s not just a one-time fix. As you continue to track sentiment over time, you’ll start seeing patterns, allowing you to stay ahead of potential issues before they become widespread.

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4. Reducing Churn and Keeping Customers Happy

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Zendesk ticket analysis can be a game-changer when it comes to keeping your customers from jumping ship. By identifying negative sentiment early on, you can tackle issues before they turn into reasons for customers to leave.

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At PrestaShop, using ClientZen to analyze their Zendesk tickets led to a 15% drop in negative sentiment during the onboarding phase and a 9% decrease in support tickets related to their main products. These improvements meant happier customers, fewer frustrations, and a noticeable reduction in churn.

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And it didn’t stop there—over time, customer conversations became more positive. PrestaShop saw a 4% decrease in negative sentiment and a 1% drop in the number of customer interactions. This shift shows how tackling issues early can lead to long-term improvements in customer loyalty.

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Thoughts on Zendesk Ticket Analysis

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Zendesk's built-in tagging and analysis system is suitable for tracking customer issues at a basic level. However, to obtain accurate, detailed insights into key issues and sentiment, it's essential to utilize machine-learning powered Zendesk ticket analysis software. With these insights, you can:

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- Decrease first response times for critical issues

- Identify recurring problems and address them before customers churn

- Foster loyalty with your top customers

- Ensure agent quality

- Construct evidence-based cases for change

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Are you ready to delve into your Zendesk ticket data to gain critical customer experience insights? Schedule a demo with ClientZen to witness it in action for yourself.

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