Essential Work Samples for Evaluating AI-Enhanced Customer Lifetime Value Prediction Skills

Customer Lifetime Value (CLV) prediction has evolved significantly with the integration of artificial intelligence. Organizations seeking professionals skilled in AI-enhanced CLV prediction need to evaluate candidates beyond traditional interviews and resume reviews. The complexity of this specialized skill—combining data science, machine learning, business analytics, and domain knowledge—requires hands-on assessment through practical work samples.

Effective AI-enhanced CLV prediction requires candidates to demonstrate proficiency in multiple areas: data preprocessing, feature engineering, model selection, business context understanding, and the ability to translate technical insights into actionable business strategies. Traditional interviews often fail to reveal a candidate's true capabilities in these areas, as theoretical knowledge doesn't always translate to practical application.

Work samples provide a window into how candidates approach real-world problems. For CLV prediction specifically, these exercises reveal how candidates balance technical sophistication with business practicality—a critical skill when developing models that will guide customer retention strategies and marketing investments.

The following work samples are designed to evaluate different facets of AI-enhanced CLV prediction skills. They range from technical implementation to strategic planning and communication, providing a comprehensive assessment of a candidate's abilities. By implementing these exercises, hiring managers can make more informed decisions and identify candidates who can truly deliver value through AI-enhanced CLV prediction.

Activity #1: CLV Dataset Analysis and Feature Engineering

This activity evaluates a candidate's ability to work with real customer data, identify relevant features, and prepare data for CLV modeling. Feature engineering is often the most critical component of successful predictive models, requiring both technical skills and business intuition. This exercise reveals how candidates approach data exploration, handle missing values, create meaningful features, and prepare data for modeling.

Directions for the Company:

• Provide the candidate with an anonymized customer dataset containing transaction history, customer demographics, and engagement metrics. This can be a modified version of your actual data or a publicly available e-commerce/subscription dataset.
• Include some intentional challenges in the data such as missing values, outliers, and features that require transformation.
• Allow the candidate 2-3 hours to complete this exercise.
• Provide access to a notebook environment (like Jupyter) with necessary libraries pre-installed, or allow them to use their preferred tools.
• Have a data scientist or analytics leader review the submission before the follow-up discussion.

Directions for the Candidate:

• Explore the provided dataset to understand the available customer information.
• Identify and handle any data quality issues (missing values, outliers, etc.).
• Engineer features that you believe would be valuable for predicting customer lifetime value.
• Document your approach, including your rationale for handling specific data issues and creating certain features.
• Prepare a brief explanation of how these features would contribute to an effective CLV prediction model.
• Submit your code (with comments) and a brief summary of your approach.

Feedback Mechanism:

• During the follow-up discussion, provide feedback on the candidate's approach to feature engineering, highlighting one particularly effective technique they used.
• Offer one suggestion for improvement, such as an additional feature they could have created or a different approach to handling a data issue.
• Ask the candidate to explain how they would implement this improvement and what impact they think it would have on the model's performance.

Activity #2: CLV Model Selection and Evaluation

This activity assesses the candidate's knowledge of different modeling approaches for CLV prediction and their ability to select and evaluate appropriate models. It tests technical ML implementation skills as well as the candidate's understanding of model evaluation in a business context. This exercise reveals how candidates balance model complexity with interpretability and how they approach model selection decisions.

Directions for the Company:

• Provide a preprocessed dataset ready for modeling (either the output from Activity #1 or a separate prepared dataset).
• Include a clear business objective for the CLV prediction (e.g., identifying high-value customers for a loyalty program, predicting churn risk, optimizing marketing spend).
• Allow the candidate 3-4 hours to complete this exercise.
• Ensure the candidate has access to necessary computational resources.
• Have a data scientist or ML engineer review the submission before the follow-up discussion.

Directions for the Candidate:

• Develop at least two different approaches to predict customer lifetime value using the provided dataset.
• One approach should be a traditional statistical or machine learning method.
• One approach should leverage more advanced techniques (deep learning, ensemble methods, etc.).
• Evaluate each model using appropriate metrics and explain why these metrics are relevant for the business objective.
• Compare the models in terms of performance, interpretability, and practical implementation considerations.
• Recommend which approach would be most suitable for the given business context and explain your reasoning.
• Submit your code, model evaluation results, and a brief report explaining your approach and recommendations.

Feedback Mechanism:

• During the follow-up discussion, highlight one strength in the candidate's modeling approach or evaluation methodology.
• Provide one suggestion for improvement, such as an alternative model that might perform better or a different evaluation metric that might be more business-relevant.
• Ask the candidate to explain how they would implement this suggestion and what impact they think it would have on the final recommendation.

Activity #3: CLV Prediction System Design

This planning exercise evaluates the candidate's ability to design a comprehensive CLV prediction system that integrates with existing business processes. It tests their understanding of the entire ML lifecycle, from data collection to model deployment and monitoring. This exercise reveals how candidates think about practical implementation challenges, cross-functional collaboration, and long-term maintenance of AI systems.

Directions for the Company:

• Provide a brief description of your company's current data infrastructure, key customer touchpoints, and existing analytics capabilities.
• Outline the business objectives for implementing an AI-enhanced CLV prediction system.
• Include information about key stakeholders who would use the CLV predictions.
• Allow the candidate 2-3 hours to prepare their design document.
• Have a technical leader and a business stakeholder review the submission before the follow-up discussion.

Directions for the Candidate:

• Design an end-to-end system for AI-enhanced CLV prediction that would integrate with the company's existing infrastructure.
• Your design should include:
• Data sources and collection methods
• Data preprocessing and feature engineering pipeline
• Model training and evaluation approach
• Deployment strategy and integration points with existing systems
• Monitoring and maintenance plan
• Timeline and resource requirements for implementation
• Create a visual representation of your system architecture (diagram or flowchart).
• Identify potential challenges and risks in implementation, along with mitigation strategies.
• Prepare a 1-2 page design document explaining your approach and rationale.

Feedback Mechanism:

• During the follow-up discussion, highlight one particularly strong aspect of the candidate's system design.
• Provide one suggestion for improvement, such as addressing a potential implementation challenge they may have overlooked or an alternative approach to a specific component.
• Ask the candidate to revise that portion of their design based on the feedback and explain how the revision addresses the concern.

Activity #4: Communicating CLV Insights to Business Stakeholders

This activity assesses the candidate's ability to translate technical CLV predictions into actionable business insights and effectively communicate them to non-technical stakeholders. It tests their business acumen, communication skills, and ability to bridge the gap between data science and business strategy. This exercise reveals how candidates think about the practical application of CLV predictions and their ability to influence business decisions.

Directions for the Company:

• Provide the candidate with the outputs of a CLV prediction model, including predicted values for different customer segments, key factors influencing CLV, and model performance metrics.
• Include a brief description of the business context and the key stakeholders who will receive the presentation (e.g., marketing team, executive leadership).
• Specify any business questions that need to be addressed (e.g., which customer segments to prioritize, how to allocate marketing budget).
• Allow the candidate 2-3 hours to prepare their presentation.
• Have both technical and business stakeholders participate in the presentation and feedback session.

Directions for the Candidate:

• Analyze the provided CLV prediction outputs to identify key insights and actionable recommendations.
• Prepare a 10-15 minute presentation aimed at non-technical business stakeholders that includes:
• A brief, non-technical explanation of how CLV is predicted
• Key findings from the CLV analysis
• Specific, actionable recommendations based on the CLV predictions
• Expected business impact of implementing these recommendations
• Any limitations or considerations stakeholders should be aware of
• Create visualizations that effectively communicate the CLV insights.
• Be prepared to answer questions about your recommendations and the underlying analysis.

Feedback Mechanism:

• After the presentation, highlight one aspect of the candidate's communication that was particularly effective (e.g., clear explanation of a complex concept, compelling visualization).
• Provide one suggestion for improvement, such as addressing a different business question or presenting a key insight more effectively.
• Give the candidate 5-10 minutes to revise their approach to that specific portion of the presentation based on the feedback.

Frequently Asked Questions

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

Each activity requires 2-4 hours of candidate preparation time, plus 30-60 minutes for follow-up discussion. We recommend selecting 1-2 activities most relevant to your specific needs rather than using all four. The entire process, including feedback and discussion, should be completed within a week to maintain candidate engagement.

Should we compensate candidates for completing these work samples?

For extensive exercises requiring several hours of work, offering compensation is recommended, especially for senior roles. This demonstrates respect for the candidate's time and expertise. Alternatively, you can scale down the exercises to shorter versions that require 60-90 minutes of effort.

How can we evaluate candidates who have limited experience with AI but strong analytics backgrounds?

For candidates transitioning from traditional analytics to AI-enhanced CLV prediction, focus on Activities #3 and #4, which test system design and business application skills. Modify Activity #1 to focus more on data exploration and less on advanced feature engineering. Provide more context and resources for these candidates.

Can these exercises be adapted for remote hiring processes?

Yes, all these activities can be conducted remotely. Use collaborative coding platforms for technical exercises, video conferencing for presentations, and shared document editing for design exercises. Ensure candidates have clear instructions and points of contact for questions that may arise during the exercise.

How should we evaluate candidates with different technical approaches to the same problem?

Focus on the candidate's reasoning rather than whether they used a specific technique. Strong candidates should be able to explain why they chose a particular approach, demonstrate awareness of alternatives, and articulate the tradeoffs involved. The quality of their thought process and communication is often more important than the specific technical solution.

Should we provide our actual company data for these exercises?

Using anonymized or modified versions of your actual data can make the exercise more relevant, but it's not necessary. Public datasets related to e-commerce, subscription services, or customer behavior can be equally effective. The key is providing enough context for candidates to demonstrate their skills in a realistic scenario.

AI-enhanced Customer Lifetime Value prediction is a specialized skill that combines technical expertise with business acumen. By implementing these work samples, you'll gain deeper insights into candidates' abilities than traditional interviews alone can provide. Remember that the goal is not just to find technically proficient candidates, but to identify those who can translate AI capabilities into tangible business value.

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.

Ready to build a complete interview guide for AI-Enhanced Customer Lifetime Value Prediction skills? Sign up for a free Yardstick account today!