Essential Work Sample Exercises for Evaluating AI Customer Support Ticket Analysis Skills

AI-powered customer support ticket analysis has become a critical capability for modern support teams. By leveraging natural language processing and machine learning, organizations can automatically categorize, prioritize, and route support tickets, extract key insights, and identify trends that might otherwise remain hidden. This technology dramatically improves response times, agent productivity, and ultimately customer satisfaction.

However, finding candidates with the right combination of AI technical skills and customer support domain knowledge presents a significant challenge. Traditional interviews often fail to reveal a candidate's true capabilities in applying AI to real-world support scenarios. Technical knowledge alone isn't sufficient—successful implementation requires understanding the nuances of customer communication, support workflows, and business objectives.

Work sample exercises provide a window into how candidates approach these complex challenges. By simulating actual tasks they would perform on the job, you can evaluate not just their technical proficiency but also their problem-solving approach, communication skills, and ability to translate business needs into technical solutions. These exercises reveal how candidates think through problems, handle constraints, and adapt to feedback—all critical skills for success in this specialized field.

The following four work sample activities are designed to comprehensively assess candidates for AI customer support ticket analysis roles. Each exercise targets different aspects of the role, from technical implementation to strategic planning, giving you a holistic view of each candidate's capabilities. By incorporating these exercises into your interview process, you'll be better equipped to identify candidates who can truly deliver value through AI-powered support ticket analysis.

Activity #1: Support Ticket Classification Model Design

This exercise evaluates a candidate's ability to design an AI solution for a common customer support challenge: automatically classifying incoming tickets. It tests their understanding of NLP techniques, classification algorithms, and how to translate business requirements into a technical implementation plan. This foundational skill is essential for anyone working on AI-powered support systems.

Directions for the Company:

• Provide the candidate with a document describing a fictional company's customer support operation, including current ticket volumes (e.g., 5,000 tickets/month), existing manual categorization system (6-8 categories like "billing issues," "technical problems," "feature requests"), and business goals (reduce response time, improve routing accuracy).
• Include 10-15 anonymized sample support tickets that represent typical inquiries.
• Allow 45-60 minutes for this exercise.
• Evaluate the candidate's approach to problem definition, technical solution design, and implementation planning.

Directions for the Candidate:

• Review the provided materials to understand the company's support operation and challenges.
• Design an AI-based ticket classification system that would automatically categorize incoming support tickets.
• Create a 1-2 page document that includes:

1. Your recommended approach for building the classification model (algorithms, features, etc.)
2. Data requirements and preprocessing steps
3. Implementation plan with key milestones
4. Evaluation metrics to measure success
5. Potential challenges and how you would address them

Feedback Mechanism:

• After reviewing the candidate's solution, provide specific feedback on one strength (e.g., "Your feature engineering approach is well-thought-out") and one area for improvement (e.g., "The evaluation metrics could be more aligned with business objectives").
• Give the candidate 10 minutes to verbally explain how they would incorporate the improvement feedback into their solution.
• Assess their receptiveness to feedback and ability to adapt their thinking.

Activity #2: Sentiment Analysis Implementation

This hands-on exercise tests a candidate's ability to implement a practical AI solution for analyzing customer sentiment in support tickets. It evaluates their coding skills, familiarity with NLP libraries, and ability to extract meaningful insights from text data—all critical capabilities for enhancing support operations through AI.

Directions for the Company:

• Prepare a dataset of 50-100 anonymized customer support messages (can be synthetic but realistic).
• Set up a development environment with Python and common data science libraries (pandas, scikit-learn, NLTK/spaCy), or allow candidates to use their preferred tools.
• Provide a Jupyter notebook template with the dataset loaded and basic instructions.
• Allow 60-90 minutes for completion.
• Evaluate code quality, analytical approach, and insights derived.

Directions for the Candidate:

• Using the provided dataset of customer support messages, implement a sentiment analysis solution that:

1. Preprocesses the text data appropriately
2. Analyzes sentiment (positive, negative, neutral) for each message
3. Identifies key phrases or topics associated with negative sentiment
4. Visualizes the results in a meaningful way
5. Provides a brief written explanation of your approach, including why you chose specific techniques

• Your solution should be implemented in code (Python preferred) and should include comments explaining your reasoning.
• Focus on creating a practical solution that could provide actionable insights for a support team.

Feedback Mechanism:

• After reviewing the candidate's implementation, provide specific feedback on one strength (e.g., "Your preprocessing steps were thorough and appropriate") and one area for improvement (e.g., "The sentiment model could be more nuanced in handling technical terminology").
• Give the candidate 15 minutes to implement a small improvement based on your feedback.
• Assess their technical adaptability and how they incorporate feedback into their code.

Activity #3: AI Model Troubleshooting Scenario

This exercise evaluates a candidate's problem-solving abilities when faced with a common challenge: an AI model that isn't performing as expected. It tests their diagnostic skills, understanding of model evaluation, and ability to propose practical solutions—essential capabilities for maintaining effective AI systems in production environments.

Directions for the Company:

• Create a scenario description of an existing ticket classification model that's experiencing issues (e.g., declining accuracy, misclassifying certain types of tickets).
• Provide model performance metrics, confusion matrix, and sample misclassified tickets.
• Include information about how the model was trained, what data was used, and how it's currently deployed.
• Allow 45-60 minutes for this exercise.
• Evaluate the candidate's analytical approach, technical understanding, and practical problem-solving.

Directions for the Candidate:

• Review the provided information about the underperforming ticket classification model.
• Analyze the performance metrics and misclassified examples to identify potential issues.
• Prepare a document that includes:

1. Your diagnosis of what might be causing the performance issues
2. At least three specific recommendations for improving the model
3. A prioritized action plan for implementing these improvements
4. Suggestions for additional data or metrics that would help confirm your diagnosis

• Be prepared to explain your reasoning and discuss alternative approaches.

Feedback Mechanism:

• After reviewing the candidate's solution, provide specific feedback on one strength (e.g., "Your diagnosis correctly identified data drift as a likely issue") and one area for improvement (e.g., "Your solution doesn't address how to maintain performance during the transition").
• Give the candidate 10-15 minutes to revise their action plan based on your feedback.
• Assess their ability to incorporate new perspectives and refine their approach.

Activity #4: Explaining AI Insights to Stakeholders

This exercise tests a candidate's ability to translate technical AI findings into business-relevant insights and communicate them effectively to non-technical stakeholders. This skill is crucial for ensuring AI solutions deliver real value to customer support operations and gain adoption across the organization.

Directions for the Company:

• Prepare a dataset and analysis results showing insights derived from support tickets (e.g., common customer pain points, emerging issues, satisfaction drivers).
• Include visualizations, model outputs, and raw data that the candidate will need to interpret.
• Specify the audience as a mix of support team managers and executives with limited technical background.
• Allow 30 minutes for preparation and 15 minutes for presentation.
• Evaluate communication clarity, ability to highlight business relevance, and skill in explaining technical concepts.

Directions for the Candidate:

• Review the provided AI analysis of customer support tickets.
• Prepare a brief (10-15 minute) presentation for non-technical stakeholders that:

1. Explains the key insights discovered through the AI analysis
2. Connects these insights to business impact and support team operations
3. Recommends specific actions based on these insights
4. Explains in simple terms how the AI arrived at these conclusions

• Your presentation should avoid technical jargon while still conveying the value and reliability of the AI analysis.
• Be prepared to answer questions about your recommendations and the underlying analysis.

Feedback Mechanism:

• After the presentation, provide specific feedback on one strength (e.g., "Your explanation of how sentiment analysis works was accessible without oversimplifying") and one area for improvement (e.g., "The business impact of the third insight wasn't clearly articulated").
• Give the candidate 5 minutes to revise their explanation of the improvement area.
• Assess their ability to adapt their communication style and clarify complex concepts.

Frequently Asked Questions

How technical should these exercises be? We're concerned about excluding candidates without deep ML expertise.

These exercises are designed to test applied AI skills rather than theoretical knowledge. Candidates should understand ML concepts and how to apply them to customer support scenarios, but they don't need to be experts in building algorithms from scratch. Focus on their problem-solving approach and ability to use appropriate tools rather than advanced technical details.

Should we provide real company data for these exercises?

No, use synthetic or thoroughly anonymized data. Create realistic examples that reflect your typical support tickets but don't contain sensitive information. This protects your customers' privacy and avoids potential data security issues while still providing a realistic scenario.

How do we evaluate candidates who use different technical approaches than we currently use?

Focus on the effectiveness and reasoning behind their approach rather than whether it matches your current methods. Different approaches might offer valuable new perspectives. Evaluate whether their solution would work for your specific challenges and whether they can clearly explain why they chose that approach.

What if a candidate doesn't complete the exercise in the allotted time?

This is valuable information in itself. Assess what they did complete and their approach. Sometimes a thoughtful, partial solution demonstrates better skills than a rushed complete solution. During feedback, you might ask what they would have done with more time to understand their full thinking process.

How should we accommodate candidates who don't have access to specific tools or environments?

Offer flexibility in tools and environments when possible. For coding exercises, consider providing a cloud-based environment (like Google Colab) or allowing candidates to use pseudocode if necessary. The focus should be on their problem-solving approach and understanding of AI concepts rather than familiarity with specific tools.

Should we expect candidates to have customer support experience in addition to AI skills?

While domain knowledge is valuable, it's often more important that candidates demonstrate the ability to understand the customer support context and ask good questions. Look for candidates who show curiosity about support operations and can translate between technical capabilities and business needs, even if they don't have direct support experience.

AI for customer support ticket analysis represents a powerful intersection of technology and customer experience. By implementing these work sample exercises, you'll be able to identify candidates who not only possess the technical skills to build effective AI solutions but also understand how to apply them in ways that deliver real value to your support operation.

The right candidate will demonstrate a balance of technical proficiency, problem-solving creativity, business acumen, and communication skills. They'll show how AI can transform raw support ticket data into actionable insights that improve customer satisfaction, agent efficiency, and overall business performance.

For more resources to enhance your hiring process, explore Yardstick's suite of AI-powered tools, including our AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator.

Build a complete interview guide for AI Customer Support Ticket Analysis skills by signing up for a free Yardstick account