Effective Work Samples to Evaluate AI Skills in Sales Compensation Design

Implementing artificial intelligence in sales compensation and commission calculation represents a significant competitive advantage for modern organizations. As sales structures become increasingly complex and data-driven, the ability to leverage AI to design, optimize, and manage compensation plans has become a critical capability. Companies that successfully integrate AI into their compensation strategies can create more motivating incentives, reduce calculation errors, identify performance trends, and ultimately drive better sales outcomes.

Evaluating candidates with the right blend of AI technical skills and sales compensation domain knowledge presents unique challenges. Traditional interviews often fail to reveal a candidate's practical abilities in applying machine learning to real-world sales compensation problems. Without proper assessment, organizations risk hiring individuals who understand theoretical concepts but struggle to implement effective solutions that drive sales performance.

Work samples and technical evaluations provide a window into how candidates approach complex compensation problems, their technical proficiency with AI tools, and their ability to communicate sophisticated models to non-technical stakeholders. These exercises reveal not just what candidates know, but how they apply that knowledge to create value in sales organizations.

The following activities are designed to comprehensively evaluate a candidate's capabilities in applying AI to sales compensation challenges. Each exercise targets different aspects of the role, from strategic planning and technical implementation to communication and problem-solving. By observing candidates complete these tasks, hiring managers can make more informed decisions about which individuals possess the unique combination of technical expertise and business acumen required for success.

Activity #1: Commission Structure Optimization Model

This activity evaluates the candidate's ability to design an AI-driven approach to optimize sales commission structures. It tests their understanding of both machine learning concepts and sales compensation principles, while revealing their ability to translate business objectives into technical solutions. This skill is fundamental for anyone working at the intersection of AI and sales compensation, as it demonstrates how they would approach creating systems that maximize sales performance while maintaining cost efficiency.

Directions for the Company:

• Provide the candidate with historical sales data for a fictional company (at least 2 years of data), including sales rep information, territory details, product categories, sales amounts, and commission payouts.
• Include information about the company's current commission structure (e.g., tiered rates, accelerators, SPIFs) and business objectives (e.g., increase new customer acquisition, improve retention, boost specific product lines).
• Allow the candidate 45-60 minutes to complete the exercise.
• Provide access to a computer with basic data analysis tools (Excel, Python/R if the candidate prefers).
• Have a sales operations leader available to answer clarifying questions about the business context.

Directions for the Candidate:

• Review the provided sales data and current commission structure.
• Design an approach for an AI/ML model that would optimize the commission structure to better achieve the company's business objectives.
• Outline the specific variables you would include in your model and why they're important.
• Explain how you would measure the success of your optimized commission structure.
• Create a simple prototype or pseudocode demonstrating your approach.
• Prepare a brief explanation of how your model would work and the benefits it would provide to the sales organization.

Feedback Mechanism:

• After the candidate presents their approach, provide feedback on one aspect they handled well (e.g., variable selection, business alignment) and one area for improvement (e.g., model complexity, practical implementation challenges).
• Give the candidate 10 minutes to refine their approach based on the feedback, focusing specifically on the improvement area.
• Observe how receptive they are to feedback and their ability to quickly iterate on their solution.

Activity #2: Sales Performance Prediction Dashboard

This activity assesses the candidate's ability to translate complex AI predictions into actionable insights for sales managers and representatives. Creating intuitive visualizations of AI-driven compensation insights is crucial for adoption and effectiveness. This exercise reveals the candidate's data visualization skills, understanding of sales metrics, and ability to design user-friendly interfaces that drive behavior change.

Directions for the Company:

• Provide a dataset containing sales rep performance data, including historical achievement against quota, commission earnings, activity metrics, and customer information.
• Include a description of a fictional AI model that predicts future sales performance and optimal commission rates based on various factors.
• Supply wireframing tools (digital or paper-based) for the candidate to design their dashboard.
• Allow 45 minutes for the exercise.
• Provide information about the key stakeholders who would use this dashboard (sales reps, managers, executives).

Directions for the Candidate:

• Design a dashboard that visualizes the AI model's predictions and recommendations for sales compensation.
• Include visualizations that would help sales representatives understand:
• Their projected commission earnings based on current performance
• Recommended actions to maximize their compensation
• How their performance compares to peers and targets
• Include visualizations that would help sales managers:
• Identify representatives who may need coaching
• Understand the effectiveness of current commission structures
• Project team performance and commission expenses
• Create a wireframe or mockup of your dashboard design.
• Prepare to explain the rationale behind your design choices and how they support effective decision-making.

Feedback Mechanism:

• Provide feedback on the dashboard's usability and clarity for the intended audience, highlighting one strength and one area for improvement.
• Ask the candidate to revise one specific section of the dashboard based on your feedback.
• Evaluate their ability to incorporate user-centered design principles and their understanding of how different stakeholders would use the information.

Activity #3: Anomaly Detection in Commission Calculations

This activity evaluates the candidate's ability to design systems that identify errors, fraud, or unusual patterns in commission calculations. As commission systems become more complex, the ability to automatically detect anomalies becomes increasingly valuable. This exercise tests the candidate's problem-solving skills, attention to detail, and understanding of both technical anomaly detection methods and the business context of sales compensation.

Directions for the Company:

• Provide a dataset of commission calculations that includes several deliberate anomalies, such as:
• Duplicate commission payments
• Unusually high commissions relative to sales amount
• Commissions paid on canceled orders
• Inconsistent application of commission rules
• Potential gaming of the system by sales reps
• Include documentation of the current commission rules and calculation process.
• Allow 60 minutes for the exercise.
• Provide access to data analysis tools (Excel, Python/R if preferred).

Directions for the Candidate:

• Review the commission calculation dataset and identify potential anomalies or errors.
• Design an AI-based approach to automatically detect these types of anomalies in the future.
• Outline the specific algorithms or techniques you would use and why.
• Explain how you would handle false positives and ensure the system doesn't flag legitimate transactions.
• Create a simple prototype or pseudocode demonstrating your approach.
• Prepare to discuss how your system would integrate with existing commission processes and the value it would provide to the organization.

Feedback Mechanism:

• After the candidate presents their solution, provide feedback on their anomaly detection approach, highlighting one strength and one area that could be improved.
• Ask the candidate to refine their approach to address the improvement area, giving them 10-15 minutes to make adjustments.
• Evaluate their ability to balance technical sophistication with practical business application, and their understanding of the real-world implications of false positives and false negatives in anomaly detection.

Activity #4: Executive Presentation on AI Commission Strategy

This role play evaluates the candidate's ability to communicate complex AI concepts to non-technical stakeholders and gain buy-in for innovative approaches to sales compensation. Success in implementing AI for commission calculation requires not just technical expertise but also the ability to articulate value and address concerns from business leaders. This exercise reveals the candidate's communication skills, business acumen, and ability to translate technical capabilities into business outcomes.

Directions for the Company:

• Provide the candidate with a scenario: They must present a proposal for implementing a new AI-driven commission calculation system to a fictional executive team.
• Include background information on the company's current challenges with their commission system (e.g., calculation errors, delayed payments, inability to model complex incentives).
• Assign 2-3 interviewers to play the roles of executives with different concerns:
• CFO concerned about cost and ROI
• VP of Sales worried about disruption to the sales team
• CIO questioning integration with existing systems
• Allow the candidate 30 minutes to prepare and 15 minutes for the presentation and Q&A.
• Provide basic presentation tools (whiteboard, flip chart, or slides).

Directions for the Candidate:

• Prepare a brief (10-minute) presentation outlining your proposed approach for implementing AI in the company's commission calculation process.
• Address the following in your presentation:
• The business problems your solution will solve
• The technical approach at a high level (without jargon)
• The expected benefits and ROI
• Implementation timeline and key milestones
• Potential risks and how you'll mitigate them
• Be prepared to answer questions from the executive team and address their specific concerns.
• Focus on communicating complex technical concepts in business terms.

Feedback Mechanism:

• After the presentation and Q&A, provide feedback on the candidate's communication effectiveness, highlighting one strength and one area for improvement.
• Ask the candidate to re-address one specific executive concern that they didn't fully resolve in their initial presentation.
• Evaluate their ability to adapt their communication style based on feedback and effectively address stakeholder concerns without relying on technical jargon.

Frequently Asked Questions

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

Each activity requires approximately 60-90 minutes total, including setup, execution, feedback, and evaluation. For senior roles, consider using 1-2 activities as part of an extended interview process. For mid-level roles, a single well-chosen activity might be sufficient. The activities can also be modified to fit shorter timeframes by reducing scope or complexity.

Do candidates need access to specific software or tools for these exercises?

Basic data analysis tools like Excel are sufficient for most candidates to demonstrate their approach. For more technical candidates, offering the option to use Python, R, or other programming languages can provide additional insights into their technical capabilities. The focus should be on their approach and thinking process rather than specific tool proficiency.

How should we evaluate candidates who have strong AI skills but limited sales compensation knowledge, or vice versa?

Look for candidates who demonstrate strong learning agility and the ability to apply their primary expertise to new domains. A candidate with strong AI skills might propose technically sophisticated solutions that need refinement for practical application, while a sales compensation expert might propose business-sound approaches that need technical enhancement. The ideal candidate shows the ability to bridge both worlds and ask insightful questions about the domain where they have less experience.

Can these activities be conducted remotely?

Yes, all these activities can be adapted for remote interviews. For data analysis exercises, consider using screen sharing or collaborative tools like Google Sheets. For presentations and role plays, video conferencing works well. Provide materials in advance and ensure candidates have clear instructions about the remote format.

How should we weight these practical exercises compared to traditional interviews?

These work samples should account for 40-60% of your evaluation, as they provide direct evidence of how candidates would perform in the role. Traditional interviews remain valuable for assessing cultural fit, career motivation, and broader experiences. The combination of behavioral interviews and practical work samples provides the most comprehensive candidate assessment.

Should we provide these exercises to candidates in advance?

For complex exercises like the Commission Structure Optimization Model, providing some background information 24-48 hours in advance can lead to more thoughtful solutions. For role plays and presentations, giving candidates time to prepare often results in more insightful discussions. However, some elements should remain unrevealed until the interview to assess the candidate's ability to think on their feet.

Implementing AI in sales compensation represents a significant opportunity for organizations to create more effective, fair, and motivating incentive systems. By using these practical work samples, you can identify candidates who not only understand the technical aspects of AI but can also apply those skills to drive real business outcomes in your sales organization.

The right talent at this intersection of technology and sales operations can transform your compensation approach from a administrative function to a strategic advantage. These individuals bring unique capabilities to optimize commission structures, predict performance, detect anomalies, and communicate complex concepts to stakeholders throughout the organization.

For more resources to improve your hiring process, explore Yardstick's suite of AI-powered hiring tools, including our AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator.

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