Essential Work Sample Exercises for AI Customer Support Analysts

Customer support data contains a wealth of insights that can transform product development and business strategy. As organizations increasingly leverage AI to analyze support tickets, the role of specialists who can bridge the gap between customer feedback, data science, and product insights has become critical. These professionals need a unique blend of technical AI knowledge, customer empathy, analytical thinking, and business acumen.

Evaluating candidates for roles involving AI in customer support ticket analysis requires more than traditional interviews. While resumes may highlight relevant experience, only practical exercises can reveal a candidate's ability to extract meaningful patterns from support data, apply appropriate AI techniques, and translate findings into actionable product insights.

The work samples outlined below are designed to assess candidates' abilities to work with real-world support data, select appropriate AI approaches, communicate insights effectively, and plan AI implementations. These exercises simulate the actual challenges these professionals face daily, providing a window into how candidates think, solve problems, and deliver value.

By incorporating these exercises into your hiring process, you'll be able to identify candidates who not only understand AI concepts but can apply them practically to improve customer experience and product development. The best candidates will demonstrate technical proficiency while maintaining focus on the business outcomes that matter most.

Activity #1: Support Ticket Classification and Pattern Recognition

This exercise evaluates a candidate's ability to work with raw customer support data, apply appropriate classification techniques, and identify meaningful patterns that could inform product decisions. It tests both technical AI skills and the ability to derive business-relevant insights from customer feedback.

Directions for the Company:

• Prepare a dataset of 100-200 anonymized customer support tickets related to your product. Include the ticket text, resolution notes, and metadata like time to resolution, customer segment, etc.
• If you don't have real data available, create a synthetic dataset that mimics your typical support issues.
• Provide access to a Jupyter notebook or similar environment where candidates can analyze the data.
• Allow 60-90 minutes for this exercise.
• Have a product manager or data scientist available to review the results and provide feedback.

Directions for the Candidate:

• Review the provided dataset of customer support tickets.
• Use AI/ML techniques to classify the tickets into meaningful categories.
• Identify the top 3-5 patterns or trends that might indicate product issues or improvement opportunities.
• Create a brief visualization that highlights these patterns.
• Prepare a 5-minute presentation explaining your approach, findings, and what product insights you would recommend based on the data.
• Be prepared to explain your methodology and the AI techniques you chose.

Feedback Mechanism:

• After the presentation, the interviewer should provide feedback on one aspect the candidate did well (e.g., "Your clustering approach effectively identified related issues that weren't obvious from manual review").
• The interviewer should also provide one area for improvement (e.g., "Consider how sentiment analysis might add another dimension to your findings").
• Give the candidate 10 minutes to incorporate the feedback by adjusting their analysis or recommendations.

Activity #2: AI Model Selection and Justification Role Play

This role play assesses the candidate's understanding of different AI approaches for support ticket analysis and their ability to communicate technical concepts to non-technical stakeholders. It evaluates both technical knowledge and business communication skills.

Directions for the Company:

• Prepare a scenario where your company needs to implement an AI solution for a specific support ticket analysis challenge (e.g., predicting customer churn based on support interactions, identifying emerging product issues, or automating ticket routing).
• Assign an interviewer to play the role of a product manager who has limited technical knowledge but needs to understand the AI options.
• Provide the candidate with basic information about your company's current support process, data availability, and business goals.
• Allow 30 minutes for preparation and 20 minutes for the role play.

Directions for the Candidate:

• Review the scenario and prepare to recommend 2-3 potential AI approaches that could address the business need.
• For each approach, be ready to explain:
• How the AI model works (in non-technical terms)
• What data it would require
• The expected benefits and limitations
• Implementation considerations
• During the role play, explain your recommendations to the "product manager" and respond to their questions and concerns.
• Your goal is to help them make an informed decision about which AI approach to pursue.

Feedback Mechanism:

• The interviewer should provide feedback on the candidate's ability to explain complex concepts clearly (e.g., "Your explanation of how sentiment analysis works was very accessible").
• They should also suggest one area for improvement (e.g., "Consider focusing more on the business outcomes rather than the technical implementation details").
• Give the candidate 5 minutes to refine their explanation of one of the AI approaches based on the feedback.

Activity #3: Insight-to-Action Workshop

This exercise evaluates a candidate's ability to translate data insights into actionable product recommendations. It tests their product thinking, business acumen, and ability to connect customer support issues to product development priorities.

Directions for the Company:

• Prepare a summary report of AI-generated insights from customer support data. This should include:
• Top issue categories by volume
• Trending topics over the past quarter
• Customer sentiment analysis
• Support tickets correlated with customer churn
• Include some actual quotes from customer tickets to provide context.
• Provide a brief overview of your current product roadmap and business priorities.
• Allow 45 minutes for preparation and 30 minutes for presentation and discussion.

Directions for the Candidate:

• Review the AI-generated insights from customer support data.
• Identify the 3 most significant opportunities for product improvement based on the data.
• For each opportunity:
• Describe the customer pain point revealed by the support data
• Propose a specific product enhancement or feature
• Explain how this change would impact key metrics (e.g., support volume, customer satisfaction, retention)
• Suggest how to measure the success of the improvement
• Prepare a brief presentation (10-15 minutes) for a cross-functional team of product managers, engineers, and customer support leaders.

Feedback Mechanism:

• After the presentation, the interviewer should highlight one particularly strong recommendation (e.g., "Your proposal to address the onboarding friction point was well-supported by the data").
• They should also suggest one area to strengthen (e.g., "Consider how you might prioritize these recommendations given limited engineering resources").
• Give the candidate 10 minutes to refine their prioritization approach or strengthen one of their recommendations based on the feedback.

Activity #4: AI Implementation Planning Exercise

This exercise assesses the candidate's ability to plan and execute an AI implementation project for support ticket analysis. It evaluates project management skills, technical understanding, and awareness of organizational change management.

Directions for the Company:

• Create a scenario where your company has decided to implement an AI system to analyze support tickets for product insights.
• Provide information about:
• Current support ticket volume and processing
• Available data and its format
• Key stakeholders (support team, product team, data science team)
• Business objectives for the implementation
• Allow 60 minutes for preparation and 30 minutes for presentation and discussion.

Directions for the Candidate:

• Develop a high-level implementation plan for an AI system that would analyze support tickets and generate product insights.
• Your plan should include:
• Data requirements and preparation steps
• AI model selection and training approach
• Integration with existing systems
• Stakeholder involvement and training
• Timeline with key milestones
• Success metrics and evaluation approach
• Create a one-page visual roadmap and be prepared to walk through your implementation strategy.
• Consider potential challenges and how you would address them.

Feedback Mechanism:

• The interviewer should provide positive feedback on one aspect of the implementation plan (e.g., "Your phased approach to implementation reduces risk and allows for learning").
• They should also suggest one area for improvement (e.g., "Consider how you might better involve the support team in the model training process").
• Give the candidate 15 minutes to revise one section of their implementation plan based on the feedback.

Frequently Asked Questions

How long should we allocate for these work samples?

Each exercise requires 1.5-2 hours total, including preparation, execution, and feedback. We recommend conducting these over multiple interview rounds or selecting the 1-2 most relevant exercises for your specific role requirements.

What if we don't have real customer support data to share?

You can create a synthetic dataset that mimics your typical support issues. Alternatively, you can describe representative support scenarios and ask candidates to explain their approach hypothetically. However, working with actual data (properly anonymized) will provide the most realistic assessment.

How technical should candidates be to complete these exercises?

Candidates should have working knowledge of AI/ML concepts and some experience with data analysis tools. However, the focus should be on their ability to derive insights and translate them into business value, not just technical implementation. Adjust the technical depth based on your specific role requirements.

Can these exercises be completed remotely?

Yes, all of these exercises can be adapted for remote interviews. For data analysis exercises, consider using collaborative notebooks like Google Colab or providing access to a virtual environment. For presentations and role plays, video conferencing tools work well.

Should we provide these exercises before the interview or during it?

For the data analysis exercise and implementation planning, consider providing materials 24-48 hours in advance to allow candidates time for thoughtful analysis. The role play and insight-to-action workshop can be conducted during the interview with brief preparation time.

How do we evaluate candidates consistently across these exercises?

Create a scorecard for each exercise that evaluates specific competencies (e.g., technical understanding, analytical thinking, communication skills, business acumen). Have multiple interviewers use the same criteria to reduce bias and ensure consistent evaluation.

The intersection of AI, customer support, and product development requires professionals who can bridge technical and business domains. By using these work samples, you'll identify candidates who not only understand AI concepts but can apply them to extract meaningful insights from support data and drive product improvements.

Remember that the best candidates will demonstrate both technical proficiency and business acumen—they'll show how AI can transform customer feedback into product innovation. By incorporating these practical exercises into your hiring process, you'll build a team capable of leveraging AI to turn support tickets into your company's competitive advantage.

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