Effective Work Samples to Evaluate AI for Customer Advocacy Identification Skills

Customer advocacy has become a critical component of modern business strategy, with advocates driving referrals, providing testimonials, and enhancing brand reputation. Identifying these advocates efficiently and accurately is increasingly being powered by artificial intelligence. Professionals skilled in AI for Customer Advocacy Identification combine technical AI expertise with customer behavior analysis to transform raw customer data into actionable advocacy insights.

When hiring for roles requiring AI for Customer Advocacy Identification skills, traditional interviews often fail to reveal a candidate's true capabilities. Technical knowledge can be memorized, but the practical application of AI to identify and nurture customer advocates requires a nuanced understanding that's best evaluated through hands-on exercises.

The work samples outlined below are designed to assess candidates' abilities to analyze customer data, design appropriate AI models, implement advocacy identification systems, and develop strategic programs based on AI-derived insights. These exercises simulate real-world scenarios that professionals in this field encounter, providing a window into how candidates approach complex problems at the intersection of AI and customer advocacy.

By incorporating these work samples into your hiring process, you'll gain deeper insights into candidates' technical proficiency with AI tools, their understanding of customer advocacy principles, and their ability to bridge these two domains effectively. This approach helps identify candidates who can not only talk about AI and customer advocacy in theory but can actually deliver results in practice.

Activity #1: Customer Data Analysis and Advocate Segmentation

This exercise evaluates a candidate's ability to analyze customer data, identify meaningful patterns, and segment potential advocates using AI-driven approaches. It tests both technical data analysis skills and strategic thinking about customer advocacy indicators. Candidates must demonstrate their ability to translate raw customer data into actionable advocacy insights using appropriate AI techniques.

Directions for the Company:

• Prepare an anonymized dataset of customer interactions, including metrics such as purchase history, product usage data, support interactions, NPS scores, social media engagement, and community participation.
• Include some "noise" in the data to test the candidate's ability to identify relevant signals.
• Provide access to basic analysis tools (spreadsheet software, Python notebook, or similar).
• Allow 45-60 minutes for this exercise.
• Have a subject matter expert available to evaluate the technical soundness of the approach.

Directions for the Candidate:

• Analyze the provided customer dataset to identify potential customer advocates.
• Develop a segmentation approach that uses AI/ML concepts to categorize customers by their advocacy potential.
• Create at least three distinct segments of potential advocates with clear criteria for each.
• Prepare a brief explanation of your methodology, including:
• Which data points you found most predictive of advocacy potential
• How you would validate your segmentation approach
• What AI/ML techniques you would apply to automate this process at scale
• Recommendations for engaging with each identified segment

Feedback Mechanism:

• The interviewer should provide feedback on one strength of the candidate's approach (e.g., creative use of available data points, sound statistical reasoning).
• The interviewer should also provide one area for improvement (e.g., overlooking certain indicators, making assumptions without validation).
• Give the candidate 10 minutes to refine their segmentation approach based on the feedback, focusing specifically on the improvement area identified.

Activity #2: AI Advocacy Program Strategy Design

This activity assesses the candidate's ability to develop a comprehensive strategy for implementing AI-powered customer advocacy identification within an organization. It evaluates strategic thinking, understanding of organizational challenges, and the ability to align AI capabilities with business objectives in the context of customer advocacy.

Directions for the Company:

• Create a fictional company profile including industry, size, current customer base, and business objectives.
• Outline current challenges in identifying customer advocates (e.g., manual processes, low response rates to advocacy requests).
• Provide information about available data sources and current technology infrastructure.
• Allow 45-60 minutes for this exercise.
• Have both technical and business stakeholders participate in the evaluation.

Directions for the Candidate:

• Design a strategic roadmap for implementing an AI-powered customer advocacy identification program for the company.
• Your strategy should include:
• Assessment of current state and key challenges
• Proposed AI approach and required data sources
• Implementation timeline with key milestones
• Required resources (technical, human, financial)
• Success metrics and measurement approach
• Potential risks and mitigation strategies
• Prepare a brief presentation (5-7 minutes) outlining your strategy.
• Be prepared to explain how your approach leverages AI to improve upon traditional advocacy identification methods.

Feedback Mechanism:

• The interviewer should highlight one particularly strong element of the candidate's strategy (e.g., innovative use of AI, practical implementation approach).
• The interviewer should also identify one area where the strategy could be strengthened (e.g., overlooking a key challenge, unrealistic timeline).
• Allow the candidate 10 minutes to revise one specific aspect of their strategy based on the feedback.

Activity #3: AI Model Evaluation for Advocacy Prediction

This exercise tests the candidate's technical understanding of AI models specifically for advocacy prediction. It evaluates their ability to assess model performance, identify limitations, and make improvements to AI systems designed for customer advocacy identification.

Directions for the Company:

• Prepare documentation for two different machine learning models designed to predict customer advocacy potential.
• Include model architecture, features used, training methodology, and performance metrics.
• Intentionally include some suboptimal choices in the models (e.g., overlooking important features, using inappropriate algorithms).
• Provide sample outputs from both models showing predictions vs. actual advocacy behaviors.
• Allow 30-45 minutes for this exercise.
• Have a technical AI expert available to evaluate the candidate's assessment.

Directions for the Candidate:

• Review the documentation for two AI models designed to predict customer advocacy potential.
• Evaluate the strengths and weaknesses of each model for the specific purpose of identifying customer advocates.
• Identify which features are most important for advocacy prediction and explain why.
• Recommend specific improvements to each model, including:
• Additional data sources that could improve predictions
• Alternative algorithms or approaches that might perform better
• Methods to reduce false positives/negatives
• Ways to make the models more explainable to business stakeholders
• Prepare to discuss how you would validate that your proposed improvements actually enhance model performance.

Feedback Mechanism:

• The interviewer should acknowledge one particularly insightful observation or recommendation made by the candidate.
• The interviewer should also point out one technical consideration the candidate may have overlooked.
• Give the candidate 10 minutes to refine their recommendations based on the feedback, specifically addressing the overlooked consideration.

Activity #4: Advocacy Identification Workflow Implementation

This activity evaluates the candidate's ability to design practical, implementable workflows that operationalize AI for customer advocacy identification. It tests their understanding of how AI insights translate into actionable processes within an organization's customer advocacy program.

Directions for the Company:

• Create a scenario describing a company that has just implemented a new AI system that scores customers on advocacy potential.
• Provide sample outputs from the system (e.g., customer profiles with advocacy scores and contributing factors).
• Outline the company's current manual advocacy program processes.
• Include information about available tools and systems (CRM, marketing automation, etc.).
• Allow 45-60 minutes for this exercise.
• Have someone familiar with customer advocacy programs evaluate the practicality of the solution.

Directions for the Candidate:

• Design an operational workflow that leverages the AI advocacy scoring system to identify, verify, and engage potential customer advocates.
• Your workflow should include:
• How the AI scores will be integrated into existing systems
• Processes for human verification of AI-identified advocates
• Triggers for different types of advocacy outreach based on scores
• Feedback loops to improve the AI system over time
• Roles and responsibilities for managing the workflow
• Create a visual representation of your workflow (flowchart, process diagram, etc.).
• Prepare to explain how your workflow balances automation with human judgment.
• Describe how you would measure the effectiveness of this workflow compared to previous manual processes.

Feedback Mechanism:

• The interviewer should highlight one particularly effective element of the candidate's workflow design.
• The interviewer should also identify one practical challenge that might arise in implementing the workflow.
• Allow the candidate 10-15 minutes to revise their workflow to address the identified challenge.

Frequently Asked Questions

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

Each exercise is designed to take 30-60 minutes, plus time for feedback and revision. For a comprehensive assessment, you might include 1-2 of these exercises in a technical interview round, depending on your overall interview structure. Consider which skills are most critical for your specific role and select exercises accordingly.

Should candidates complete these exercises live or as take-home assignments?

Activities #1 and #3 can work well as live exercises during an interview, while Activities #2 and #4 might be more effective as take-home assignments due to their strategic nature. For take-home assignments, consider setting a reasonable time limit (e.g., 2-3 hours) to respect candidates' time while still getting meaningful insights.

What if our company doesn't have the sample data needed for these exercises?

You can create simplified synthetic data that mimics your actual customer data patterns. Alternatively, there are public datasets available that can be adapted for these purposes. The key is ensuring the data includes variables relevant to customer advocacy (engagement metrics, satisfaction scores, etc.) even if simplified.

How should we evaluate candidates who use different AI approaches than we currently use?

Focus on the candidate's reasoning rather than specific tools or algorithms. A strong candidate should be able to explain why their chosen approach is appropriate for the problem, demonstrating sound AI principles and customer advocacy understanding. Different approaches may offer valuable new perspectives for your team.

How can we make these exercises accessible to candidates with varying technical backgrounds?

Consider offering options for completing the technical components. For example, allow candidates to use familiar tools (Python, R, Excel) for the data analysis exercise. For candidates with less technical backgrounds but strong customer advocacy experience, you might emphasize the strategic exercises while simplifying the technical requirements.

Should we share our actual AI models with candidates during the interview process?

No, create simplified versions or descriptions of models that capture the essential characteristics without revealing proprietary information. The goal is to test the candidate's thinking process and technical understanding, not their ability to work with your specific implementation.

The intersection of AI and customer advocacy represents a powerful opportunity for businesses to identify and nurture their most valuable brand champions. By using these work samples in your hiring process, you'll be able to identify candidates who can truly leverage artificial intelligence to transform your customer advocacy programs. The right talent will not only understand the technical aspects of AI but also how to apply these capabilities to create meaningful customer relationships that drive business growth.

For more resources to enhance your hiring process, check out Yardstick's AI Job Descriptions, AI Interview Question Generator, and AI Interview Guide Generator. These tools can help you build comprehensive interview processes that identify the best talent for your AI and customer advocacy initiatives.

Ready to build a complete interview guide for AI for Customer Advocacy Identification? Sign up for a free Yardstick account today!