Essential Work Sample Exercises for Hiring AI Customer Success Health Scoring Specialists

Customer Success teams increasingly rely on AI-powered health scoring to predict customer behavior, identify at-risk accounts, and proactively address issues before they lead to churn. Finding candidates who can effectively build, implement, and optimize AI health scoring models requires evaluating both technical AI skills and customer success domain knowledge.

Traditional interviews often fail to reveal a candidate's true capabilities in applying AI to customer success challenges. Technical questions may demonstrate theoretical knowledge, but they don't show how candidates approach real-world problems or communicate complex concepts to stakeholders with varying technical backgrounds.

Work samples provide a window into how candidates actually think and work. For AI Customer Success Health Scoring specialists, effective work samples should evaluate their ability to design predictive models, engineer relevant features, interpret results, and translate technical insights into actionable recommendations for customer success teams.

The following exercises are designed to assess candidates' abilities to blend AI expertise with customer success domain knowledge. They evaluate technical skills, problem-solving approaches, communication abilities, and business acumen—all critical components for success in this specialized role. By incorporating these work samples into your hiring process, you'll gain deeper insights into which candidates can truly drive value through AI-powered customer health scoring.

Activity #1: Design a Customer Health Score Model

This exercise evaluates a candidate's ability to design an AI-based customer health scoring system from the ground up. It tests their understanding of both machine learning concepts and customer success metrics, as well as their ability to plan a complex technical project with business objectives in mind.

Directions for the Company:

• Provide the candidate with a fictional B2B SaaS company profile, including product description, customer segments, and business model (e.g., a marketing automation platform with enterprise and mid-market customers on annual contracts).
• Include a list of available data sources: product usage metrics, support tickets, NPS scores, contract information, etc.
• Allow 45-60 minutes for this exercise.
• Prepare questions about specific design choices to probe the candidate's reasoning.
• Have a technical team member and a customer success leader evaluate the response.

Directions for the Candidate:

• Design a customer health scoring model that predicts the likelihood of renewal for the provided company.
• Outline the key components of your model, including:
• Which data sources you would use and why
• How you would preprocess and engineer features
• What machine learning approach you would take (classification, regression, etc.)
• How you would handle class imbalance (typically fewer churned customers than renewed)
• How you would validate model performance
• Create a simple diagram showing the model architecture and data flow.
• Explain how customer success managers would use the outputs of your model.

Feedback Mechanism:

• Provide feedback on the candidate's technical approach, highlighting strengths in their model design or feature selection.
• Offer one improvement suggestion, such as additional data sources they might consider or potential challenges in their approach.
• Ask the candidate to spend 5-10 minutes revising their model design based on the feedback.

Activity #2: Feature Engineering for Predictive Customer Health

This exercise tests the candidate's ability to transform raw customer data into meaningful features that can predict customer health. It evaluates their technical data manipulation skills, creativity in feature design, and understanding of what signals actually matter for customer health.

Directions for the Company:

• Prepare a sanitized dataset (with no real customer information) that includes:
• Product usage logs (e.g., logins, feature usage, session duration)
• Support interactions (tickets opened, resolved, time to resolution)
• Customer information (company size, industry, contract value)
• Historical outcomes (renewed/churned)
• Provide access to a notebook environment (like Jupyter) with necessary libraries.
• Allow 60 minutes for this exercise.
• Have a data scientist or ML engineer available to evaluate the technical aspects.

Directions for the Candidate:

• Analyze the provided dataset to identify patterns that might indicate customer health.
• Create at least 5 engineered features that you believe would be predictive of customer renewal.
• For each feature:
• Explain your hypothesis about why this feature would be predictive
• Show the code used to create the feature
• Provide a simple visualization showing the relationship between the feature and the outcome
• Rank your features by expected predictive power and explain your reasoning.

Feedback Mechanism:

• Provide feedback on the candidate's feature engineering approach, highlighting one particularly innovative or effective feature.
• Suggest one improvement, such as a different transformation technique or an additional feature they might consider.
• Ask the candidate to implement the suggested improvement or refine one of their features based on the feedback.

Activity #3: Interpreting Model Results and Creating Action Plans

This exercise evaluates the candidate's ability to translate technical model outputs into actionable insights for customer success teams. It tests their business acumen, communication skills, and understanding of how AI insights can drive customer success strategies.

Directions for the Company:

• Prepare a mock AI health score dashboard showing:
• Overall health scores for 10-15 customers
• Key contributing factors to each score
• Historical trends in health scores
• Model confidence levels
• Include some interesting edge cases (e.g., high usage but low engagement with new features).
• Allow 45 minutes for this exercise.
• Have a customer success manager participate to evaluate the business relevance of recommendations.

Directions for the Candidate:

• Review the health score dashboard and identify 3 customers that require attention.
• For each identified customer:
• Explain why you selected this customer
• Interpret what the model is telling you about their situation
• Create a prioritized action plan for the customer success manager
• Suggest what additional information might be helpful to validate the model's assessment
• Develop one general recommendation for improving the customer success team's response to health score alerts.

Feedback Mechanism:

• Provide feedback on the candidate's analysis, highlighting effective insights or recommendations.
• Suggest one area where their interpretation could be improved or where they might have missed an important signal.
• Ask the candidate to revise one of their customer action plans based on the feedback.

Activity #4: Explaining Technical Concepts to Stakeholders

This exercise tests the candidate's ability to communicate complex AI concepts to non-technical stakeholders. It evaluates their communication skills, ability to translate technical details into business language, and skill in addressing stakeholder concerns about AI-driven approaches.

Directions for the Company:

• Prepare a scenario where the candidate must explain the new AI health scoring system to:
• The customer success team who will use it daily
• The executive team who approved the investment
• Include some common questions and concerns from each group.
• Allow 30-45 minutes for preparation and 15 minutes for presentation/discussion.
• Include both technical and non-technical team members in the evaluation.

Directions for the Candidate:

• Prepare a 10-minute presentation explaining:
• How the AI health scoring system works (at an appropriate level for each audience)
• What inputs it uses and why they matter
• How to interpret the scores and confidence levels
• Limitations of the model and when human judgment should override it
• How success of the system will be measured
• Create 1-2 simple visual aids to support your explanation.
• Be prepared to answer questions from different stakeholder perspectives.

Feedback Mechanism:

• Provide feedback on the candidate's communication clarity and effectiveness in translating technical concepts.
• Suggest one improvement to make their explanation more accessible or address a specific stakeholder concern better.
• Ask the candidate to revise a portion of their explanation based on the feedback.

Frequently Asked Questions

How long should we allocate for these work samples in our interview process?

Each exercise requires 45-60 minutes, plus time for feedback and revision. We recommend selecting 1-2 exercises most relevant to your specific needs rather than attempting all four in a single interview session. You might consider having candidates complete the design exercise as a take-home assignment before bringing them in for the more interactive exercises.

Should we provide real company data for these exercises?

No, always use synthetic or thoroughly anonymized data. The exercises are designed to test capabilities without requiring access to your actual customer data. Creating realistic but fictional scenarios protects your data while still providing meaningful evaluation opportunities.

What if a candidate's approach is different from our current methods?

This can actually be valuable! Different approaches might highlight improvements you haven't considered. Evaluate the candidate's reasoning and whether their approach is sound, even if it differs from your current methodology. The key is whether they can justify their decisions with clear logic and domain understanding.

How technical should the candidate be to complete these exercises?

These exercises are designed for candidates with both AI/ML technical skills and customer success domain knowledge. However, you can adjust the technical depth based on your specific role requirements. For more technical roles, you might emphasize Activities #1 and #2, while for more strategic roles, Activities #3 and #4 might be more relevant.

What if we don't currently have an AI health scoring system?

These exercises are still valuable! They help evaluate candidates who could build such a system for you. In fact, Activity #1 specifically assesses a candidate's ability to design a system from scratch, which would be particularly relevant for organizations looking to implement AI health scoring for the first time.

How do we evaluate candidates consistently across these exercises?

Create a rubric for each exercise that includes both technical and non-technical criteria. For example, evaluate technical soundness, creativity, communication clarity, and business relevance. Having multiple evaluators (both technical and business-focused) can also help ensure balanced assessment.

The right AI specialist for customer success health scoring needs a rare combination of technical expertise, domain knowledge, and communication skills. These work samples go beyond traditional interviews to reveal how candidates actually approach the complex challenges of this role. By incorporating these exercises into your hiring process, you'll identify candidates who can truly transform your customer success operations through effective AI implementation.

For more resources to improve your hiring process, check out Yardstick's AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator.

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