Essential Work Sample Exercises for Evaluating AI-Enhanced Renewal and Churn Prediction Skills

Customer retention is the lifeblood of subscription-based businesses, and AI-enhanced renewal and churn prediction has become a critical capability for companies looking to maintain and grow their customer base. The ability to accurately predict which customers are at risk of churning allows businesses to take proactive measures, implement targeted retention strategies, and ultimately protect revenue streams.

Evaluating candidates for roles involving AI-enhanced churn prediction requires more than just reviewing resumes or conducting traditional interviews. These positions demand a unique blend of technical data science skills, business acumen, and the ability to translate complex analyses into actionable insights. Without proper assessment of these capabilities, companies risk hiring individuals who may understand the theory but struggle with practical implementation.

Work samples provide a window into how candidates approach real-world churn prediction challenges. They reveal not just technical proficiency with algorithms and data manipulation, but also how candidates think about customer behavior, what factors they consider relevant to churn, and how they would structure solutions that deliver business value. These exercises help distinguish between candidates who merely know the terminology and those who can actually drive results.

The following work samples are designed to evaluate candidates across multiple dimensions of AI-enhanced renewal and churn prediction. They assess technical modeling skills, data preparation abilities, business understanding, communication capabilities, and project planning expertise. By implementing these exercises in your hiring process, you'll gain deeper insights into each candidate's potential to contribute to your customer retention efforts.

Activity #1: Feature Engineering for Churn Prediction

This exercise evaluates a candidate's ability to identify and create meaningful predictive features from raw customer data. Feature engineering is often the most critical step in building effective churn prediction models, requiring both technical skills and business intuition about what signals might indicate a customer's likelihood to churn.

Directions for the Company:

• Prepare an anonymized dataset containing customer information such as usage patterns, support interactions, billing history, and product engagement metrics. Ensure the data has been properly anonymized but retains realistic patterns.
• Include some obvious churn indicators as well as more subtle signals that require deeper analysis.
• Provide access to the dataset in a format the candidate is comfortable with (CSV, SQL database, etc.).
• Allow 60-90 minutes for this exercise.
• Prepare a list of key features you would expect a strong candidate to identify or create.

Directions for the Candidate:

• Review the provided customer dataset and identify patterns or signals that might predict customer churn.
• Create 5-10 new features that you believe would be valuable for predicting customer churn.
• For each feature, provide a brief explanation of why you believe it would be predictive and how you constructed it.
• Rank your features in order of expected predictive power and explain your reasoning.
• Be prepared to discuss how you would validate these features' effectiveness.

Feedback Mechanism:

• After the candidate presents their features, provide feedback on one feature that was particularly insightful or well-constructed.
• Offer one suggestion for improvement, such as an overlooked data relationship or a different approach to feature construction.
• Give the candidate 10-15 minutes to refine their approach based on your feedback and explain how they would incorporate this feedback into their feature engineering process.

Activity #2: Model Selection and Evaluation Strategy

This exercise assesses a candidate's knowledge of appropriate machine learning models for churn prediction and their ability to design a robust evaluation framework. It reveals their understanding of model selection considerations, evaluation metrics specific to churn prediction, and how to address common challenges like class imbalance.

Directions for the Company:

• Create a scenario description of your company's churn prediction needs, including business context, available data, and key objectives.
• Specify constraints such as interpretability requirements, computational resources, or implementation timelines.
• Prepare questions about handling class imbalance, feature importance, and model explainability.
• Allow 45-60 minutes for this exercise.
• Have a technical team member available to answer clarifying questions about the data or business context.

Directions for the Candidate:

• Based on the scenario provided, propose 2-3 machine learning models that would be appropriate for predicting customer churn.
• For each model, explain why it's suitable for this specific churn prediction task and what advantages it offers.
• Design an evaluation framework that includes:
• Appropriate metrics for measuring model performance (beyond just accuracy)
• A validation strategy that accounts for potential time-based effects
• Methods for addressing class imbalance in the churn prediction context
• Approaches for determining feature importance and model explainability
• Outline how you would compare models to select the final approach.

Feedback Mechanism:

• Provide positive feedback on one aspect of the candidate's model selection or evaluation strategy that demonstrates strong understanding.
• Suggest one area where their approach could be improved or a consideration they may have overlooked.
• Ask the candidate to revise their evaluation framework based on your feedback and explain how this change would improve the overall model selection process.

Activity #3: Churn Analysis Presentation

This exercise evaluates a candidate's ability to communicate complex technical findings to business stakeholders and translate model outputs into actionable recommendations. It tests their skill in distilling technical details into business-relevant insights that can drive decision-making.

Directions for the Company:

• Prepare a mock churn prediction model output, including feature importance scores, customer segments with varying churn probabilities, and historical performance metrics.
• Create a fictional executive audience profile (e.g., CMO, Customer Success Director) with specific business questions they want answered.
• Provide the candidate with relevant company context, such as current retention strategies and business constraints.
• Allow 2-3 hours of preparation time (can be done before the interview) and 15-20 minutes for the presentation.
• Prepare role-play questions that executives might ask during such a presentation.

Directions for the Candidate:

• Review the provided churn model outputs and company context.
• Prepare a 15-minute presentation aimed at business executives that:
• Summarizes key findings from the churn prediction model
• Identifies the most significant factors driving customer churn
• Segments customers by churn risk and provides profiles of high-risk segments
• Recommends 3-5 specific, actionable strategies to reduce churn based on the model insights
• Proposes how to measure the effectiveness of these recommendations
• Create visualizations that effectively communicate the insights to non-technical stakeholders.
• Be prepared to answer questions about your methodology and recommendations.

Feedback Mechanism:

• Highlight one aspect of the presentation that effectively translated technical insights into business value.
• Provide one suggestion for improving how the candidate communicated a complex concept or recommendation.
• Ask the candidate to revise one of their slides or explanations based on your feedback, giving them 10 minutes to make adjustments and re-present that section.

Activity #4: Churn Reduction Initiative Planning

This exercise assesses a candidate's ability to plan and execute a complex AI project focused on reducing customer churn. It evaluates their project management skills, cross-functional collaboration approach, and understanding of how to implement AI solutions in a business context.

Directions for the Company:

• Create a scenario where the company needs to reduce churn by a specific percentage within a defined timeframe.
• Provide information about available resources, stakeholders, existing systems, and constraints.
• Include details about data availability, current customer success processes, and technical infrastructure.
• Allow 60-90 minutes for this exercise.
• Have representatives from relevant departments (data science, engineering, customer success) available if possible.

Directions for the Candidate:

• Develop a comprehensive project plan for implementing an AI-enhanced churn reduction initiative that includes:
• Project phases and timeline from data collection to deployment and monitoring
• Required resources (technical, human, and financial)
• Key stakeholders and their involvement at different stages
• Data requirements and potential data gaps that need to be addressed
• Technical architecture for the solution
• Implementation strategy for integrating predictions into customer-facing processes
• Success metrics and evaluation framework
• Potential risks and mitigation strategies
• Create a high-level project roadmap with major milestones.
• Outline how you would ensure collaboration between data science, engineering, and business teams.

Feedback Mechanism:

• Provide positive feedback on one aspect of the project plan that demonstrates strong planning and implementation skills.
• Suggest one area where their plan could be strengthened or a consideration they may have overlooked.
• Ask the candidate to revise their approach to addressing this specific challenge and explain how this change would improve the overall project outcome.

Frequently Asked Questions

How long should we allocate for these work sample exercises?

The time required varies by exercise. Feature Engineering and Model Selection exercises typically require 60-90 minutes each. The Churn Analysis Presentation may need 2-3 hours of preparation plus 20-30 minutes for presentation and feedback. The Churn Reduction Initiative Planning exercise requires 60-90 minutes. Consider spreading these across multiple interview stages rather than conducting all in one session.

Should we provide real company data for these exercises?

While using real data would make the exercises more relevant, it's essential to properly anonymize any sensitive information. Alternatively, you can create synthetic data that mimics your actual customer patterns. The key is ensuring the data contains realistic signals and challenges that the candidate would encounter in the role.

What if candidates don't have experience with our specific industry?

These exercises can be adapted for candidates without industry-specific experience by providing additional context about typical customer behaviors and business models in your sector. Focus on evaluating their analytical approach and ability to ask insightful questions rather than industry-specific knowledge, which can be acquired.

How should we evaluate candidates who use different technical approaches than our current team?

Evaluate the soundness of their methodology and reasoning rather than adherence to your existing approaches. Different techniques may offer valuable new perspectives. Ask candidates to explain their choices and the tradeoffs they considered. Strong candidates should be able to articulate why their approach is appropriate for the specific problem, even if it differs from your current methods.

How can we make these exercises accessible for remote candidates?

All these exercises can be conducted remotely using video conferencing tools and collaborative platforms like Google Colab, Jupyter notebooks, or shared documents. Provide clear instructions about the tools to be used and ensure candidates have access to necessary software before the interview. Consider extending time limits slightly to account for potential technical difficulties.

Should we expect candidates to produce working code during these exercises?

For the Feature Engineering and Model Selection exercises, conceptual solutions with pseudocode are generally sufficient. The focus should be on the candidate's approach and reasoning rather than perfectly functioning code. However, if coding proficiency is critical for the role, you might request simple implementations of key components within a reasonable time frame.

AI-enhanced renewal and churn prediction has become an essential capability for subscription-based businesses looking to maintain healthy growth and profitability. By implementing these work sample exercises in your hiring process, you'll be able to identify candidates who not only understand the technical aspects of predictive modeling but can also translate those insights into business value.

The right talent in this area can dramatically improve your company's ability to retain customers, optimize retention strategies, and ultimately drive sustainable growth. These exercises provide a comprehensive framework for evaluating candidates across the full spectrum of skills needed for success in AI-enhanced churn prediction roles.

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