AI-driven ticket tagging

6 Ways to Use AI Ticket Tags to Optimise Customer Service

Imagine running an eCommerce company selling furniture. A customer contacts your support team with a refund request because half the screws are missing from the package. Without advanced technology, handling this issue efficiently would be challenging. This is where Natural Language Processing (NLP) steps in, transforming the way customer support operates.

Natural Language Processing (NLP) technology is the answer to efficiently, consistently, and objectively uncovering topic and sentiment insights from support tickets. By automating ticket tagging and analysis, businesses can streamline operations, improve customer satisfaction, and drive growth.

In this blog post, we’ll explore how AI and NLP can revolutionize customer support in eCommerce. We’ll dive into the benefits, use cases, and companywide advantages of AI-driven ticket tagging.

Read More: Ticket Deflection: Enhance your Self-Service with AI

Understanding NLP in Customer Support

Natural Language Processing (NLP) is a branch of AI that enables computers to understand, interpret, and respond to human language. In customer support, NLP can analyze support tickets to extract valuable insights. This technology helps identify key issues, track customer sentiment, and automate responses.

Consider our eCommerce example. A customer reports missing screws in their furniture package and requests a refund. NLP analyzes this ticket, tagging it with terms like “refund request,” “missing item,” and “screws.” These tags help the support team quickly understand the issue and respond appropriately.

Automated ticket tagging with NLP improves efficiency by categorizing tickets in real-time. This process eliminates the need for manual tagging, saving time and reducing errors. It also ensures that tickets are routed to the right team, enhancing response times and customer satisfaction.

Over time, NLP can identify trends in support tickets. For example, if screws are frequently reported missing, the operations team can address this issue, reducing refund requests and improving customer experience.

The Power of Automated Ticket Tagging

Automated ticket tagging uses NLP to analyze and categorize support tickets instantly. This technology offers several benefits for eCommerce businesses. First, it enhances operational efficiency by automating repetitive tasks. This allows support teams to focus on more complex issues that require human intervention.

For instance, when a ticket is tagged as a “refund request,” it is automatically routed to the refunds team. This ensures that issues are handled by the appropriate department, speeding up the resolution process. Additionally, tags like “urgent” can prioritize tickets that need immediate attention, improving response times for critical issues.

Automated ticket tagging also helps in identifying potential customer churn. Tickets tagged with terms indicating dissatisfaction can be flagged for follow-up. This proactive approach helps retain customers by addressing their concerns promptly.

Furthermore, NLP can provide insights into common issues. By analyzing tagged tickets, businesses can identify patterns and trends. This information can be used to address recurring problems, reducing support ticket volume and enhancing customer satisfaction.

Use Cases for AI Customer Service Tags

1. Reducing Average Handle Time

AI-driven ticket tagging can significantly reduce the average handle time for support tickets, providing substantial benefits for businesses aiming to enhance their customer service efficiency.

Auto-Routing Tickets to the Right Team

AI-driven ticket tagging automatically categorizes support tickets and routes them to the appropriate team based on the issue identified. For instance, if a ticket is tagged with “technical issue,” it is immediately directed to the technical support team. This automated process eliminates the time-consuming manual routing, ensuring that tickets are handled by the experts best equipped to resolve them.

By reducing the time tickets spend in the queue, auto-routing enhances overall efficiency. Customers receive quicker responses, which leads to higher satisfaction and improved perception of the company’s customer service capabilities. Moreover, this system helps in balancing the workload among various support teams, ensuring that no team is overwhelmed while others remain underutilized.

Auto-Prioritization of Tickets

AI-driven ticket tagging can also auto-prioritize tickets based on their urgency and impact on customer experience. Tickets tagged with keywords such as “urgent” or those related to negative customer feedback are flagged for immediate attention. This ensures that critical issues are addressed promptly, preventing escalation and reducing the risk of customer churn.

For example, a ticket tagged as “refund request” due to a missing item might be auto-prioritized because it indicates a potentially unhappy customer. Prioritizing such tickets helps in addressing the customer’s concern swiftly, thereby increasing the likelihood of retaining them and maintaining a positive relationship.

Auto-Suggesting Macros for Efficiency

Another significant feature of AI-driven ticket tagging is its ability to auto-suggest macros. AI understands the context and topic of the ticket and suggests pre-created responses to agents. This feature saves agents the time and effort required to search for or compose appropriate responses, allowing them to respond to customer queries more quickly and consistently.

For example, if a ticket is about a common issue like “password reset,” AI can suggest a standard macro response that guides the customer through the process. This not only speeds up the resolution time but also ensures that customers receive accurate and consistent information.

Automatically Turning Negative Social Media Comments into Support Tickets

AI can automatically monitor social media platforms for negative comments about the company or its products. When such comments are detected, AI tags them appropriately and converts them into support tickets. This ensures that negative feedback is addressed promptly, preventing potential PR crises and improving overall customer satisfaction.

For instance, a customer tweeting about a defective product can trigger AI to create a support ticket tagged with “defective product” and “social media complaint.” The support team can then reach out to the customer proactively, offering a resolution and demonstrating the company’s commitment to customer service.

2. Improving Agent Productivity

AI-driven ticket tagging significantly enhances agent productivity by automating repetitive tasks and providing valuable insights that help agents perform their jobs more efficiently.

Auto-Suggesting Macros

As previously mentioned, AI can suggest macros based on the topic of the ticket. This feature is especially useful in handling frequently asked questions or common issues. By providing pre-written responses, AI reduces the time agents spend crafting replies, allowing them to handle more tickets in less time.

For example, if an agent receives a ticket about tracking a shipment, AI can suggest a macro with detailed tracking instructions. This speeds up the resolution process and ensures that the customer receives a quick and accurate response.

Enhancing Agent Training

AI-driven ticket tagging also plays a crucial role in improving agent training. By analyzing support tickets, AI can identify common issues, niche problems, and high-impact queries. This data can be used to tailor training programs, ensuring that agents are well-prepared to handle various customer inquiries effectively.

Training can be focused on addressing the most frequent issues or those that have the greatest impact on customer satisfaction. For instance, if AI identifies that a significant number of tickets are related to billing errors, training sessions can be designed to help agents resolve these issues more efficiently.

Automatically Handling Negative Social Media Comments

AI’s ability to convert negative social media comments into support tickets not only improves customer satisfaction but also enhances agent productivity. Agents no longer need to manually scan social media platforms for negative feedback. Instead, AI identifies these comments and creates support tickets automatically, allowing agents to focus on resolving the issues rather than searching for them.

This automation helps in promptly addressing customer concerns, demonstrating responsiveness and commitment to customer service. It also frees up agents’ time, enabling them to handle a higher volume of tickets more effectively.

3. Reducing Ticket Volume

AI-driven insights can help reduce the volume of support tickets by addressing the root causes of common issues and empowering customers with self-service options.

Identifying and Addressing Root Causes

By analyzing ticket tags, AI can identify patterns and trends in customer complaints. This information is invaluable in addressing the root causes of frequent issues. For example, if a large number of tickets are related to missing screws in furniture packages, the operations team can implement better quality control measures to ensure all components are included.

Addressing these root causes not only reduces the volume of support tickets but also enhances overall customer satisfaction by preventing recurring problems.

Building a Knowledge Base

Creating a comprehensive, data-driven knowledge base is another effective way to reduce support ticket volume. By addressing common issues and providing detailed solutions, businesses can empower customers to find answers independently. This reduces the need for customers to contact support for help with routine queries.

For instance, if customers frequently ask about assembly instructions for a particular piece of furniture, the company can create a detailed guide in the knowledge base. This allows customers to resolve the issue on their own, reducing the burden on support agents.

Proactive Anomaly Detection and Trend Analysis

AI-driven anomaly detection and trend analysis can help businesses proactively address emerging issues. By identifying unexpected trends in support tickets, businesses can take preventive measures to avoid potential problems.

For example, if AI detects an increase in tickets related to a specific product defect, the company can investigate and resolve the issue before it affects more customers. This proactive approach helps in maintaining a high level of customer satisfaction and reduces the overall ticket volume.

Companywide Benefits of AI Ticket Tags

Implementing AI support ticket analytics and automation offers companywide benefits. By connecting customer support to broader business goals, businesses can drive growth, retention, and product adoption.

AI simplifies analytics by uncovering insights from vast amounts of data quickly. This eliminates the need for manual tagging, saving time and reducing errors. For example, AI can analyze over 100,000 surveys, tickets, or reviews in seconds, providing valuable insights for strategic decision-making.

Increasing customer lifetime value is another significant benefit. By addressing the root causes of customer pain points, businesses can enhance satisfaction and loyalty. Satisfied customers are more likely to stay longer and spend more, contributing to revenue growth.

AI-driven insights also bring the customer to life for different departments. Quantitative insights ensure that customer-centric thinking underpins strategic roadmaps, aligning business goals with customer needs.

Monitoring third-party vendor performance becomes easier with AI. Quantitative evidence helps pinpoint which providers negatively impact customers. This information can be used to make informed decisions about vendor partnerships, ensuring better service quality.

Customer Experience Benefits

AI-driven ticket tagging offers several customer experience benefits. Simplifying analytics is one of the key advantages. Instead of manually tagging data points, AI can analyze and categorize vast amounts of data quickly. This provides valuable insights into customer sentiment and common issues.

Increasing customer lifetime value is another benefit. By understanding and addressing the root causes of customer pain points, businesses can enhance satisfaction and loyalty. Happy customers are more likely to stay longer and spend more, contributing to revenue growth.

AI insights also help bring the customer to life for different departments. Quantitative insights ensure that customer-centric thinking underpins strategic roadmaps, aligning business goals with customer needs. This holistic approach improves overall customer experience.

Monitoring third-party vendor performance becomes easier with AI. Quantitative evidence helps pinpoint which providers negatively impact customers. This information can be used to make informed decisions about vendor partnerships, ensuring better service quality.

Product Benefits

AI-driven ticket tagging offers several benefits for product teams. One significant advantage is optimizing eCommerce conversions. By identifying key friction points on the website, businesses can reduce barriers to checkout and improve the overall shopping experience.

Tracking product performance is another benefit. AI can analyze support ticket volume to assess the impact of new product or feature releases. This information helps product teams understand customer feedback and make necessary adjustments.

Roadmap prioritization becomes more data-driven with AI. By identifying user feedback, feature requests, and pain points, businesses can prioritize product development efforts. This ensures that the most critical issues are addressed, enhancing customer satisfaction.

Additionally, AI insights help in identifying potential product improvements. By analyzing support tickets, product teams can uncover common issues and areas for enhancement. This proactive approach leads to better product quality and customer experience.

Marketing Team Benefits

AI-driven ticket tagging provides valuable insights for marketing teams. One benefit is elevating what customers love about the brand. By discovering positive feedback and common praises, businesses can emphasize these aspects in brand communications, copy, and advertising.

Directing content strategy is another advantage. AI insights help identify problems and areas of interest to customers. This information can be used to create targeted content that addresses customer needs and interests, improving engagement and brand loyalty.

AI also helps in understanding customer sentiment. By analyzing ticket tags, marketing teams can gauge customer satisfaction and identify areas for improvement. This information can be used to refine marketing strategies and enhance overall customer experience.

Additionally, AI-driven insights can be used to monitor the effectiveness of marketing campaigns. By tracking customer feedback and support ticket volume, businesses can assess the impact of their marketing efforts. This helps in optimizing campaigns and achieving better results.


Integrating NLP and AI in customer support offers numerous benefits for eCommerce businesses. From improving operational efficiency to enhancing customer satisfaction, AI-driven ticket tagging transforms customer support processes. By leveraging these technologies, businesses can drive growth, retention, and product adoption.

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