Effective Work Samples for Evaluating AI-Powered Win/Loss Analysis Skills

AI-powered win/loss analysis has become a critical function for businesses seeking to understand why they win or lose deals and how to improve their competitive positioning. Unlike traditional win/loss analysis, which often relies solely on manual reviews and subjective feedback, AI-powered approaches can process vast amounts of data, identify patterns, and generate insights that might otherwise remain hidden. Companies that excel at this practice gain a significant competitive advantage through data-driven decision-making.

Evaluating candidates for roles involving AI-powered win/loss analysis requires assessing a unique combination of analytical skills, AI tool proficiency, business acumen, and communication abilities. Traditional interviews often fail to reveal a candidate's true capabilities in these areas, as theoretical knowledge doesn't necessarily translate to practical application. Work samples provide a window into how candidates actually approach these complex tasks.

The exercises outlined below are designed to simulate real-world scenarios that professionals in this field encounter. They test not only technical proficiency with AI tools but also the ability to design effective analysis frameworks, extract meaningful insights from data, and communicate recommendations that drive business value. By observing candidates complete these tasks, hiring managers can make more informed decisions about who will truly excel in the role.

Implementing these work samples as part of your interview process will help identify candidates who can bridge the gap between technical AI capabilities and business outcomes. The best practitioners in this field aren't just technically proficient—they understand how to translate complex data into actionable strategies that improve win rates. These exercises will help you distinguish between candidates who merely understand AI-powered win/loss analysis in theory and those who can execute it effectively in practice.

Activity #1: AI-Assisted Deal Analysis

This exercise evaluates a candidate's ability to use AI tools to analyze deal data, identify patterns in wins and losses, and extract meaningful insights. It tests their technical proficiency with AI analysis tools, their understanding of sales processes, and their ability to identify significant factors that influence deal outcomes.

Directions for the Company:

• Prepare a sanitized dataset of 20-30 closed deals (both won and lost) with relevant attributes such as deal size, industry, sales cycle length, competitor, key decision-makers involved, product features discussed, and any available customer feedback.
• Provide access to an AI analysis tool the company uses (or a common tool like Tableau with AI features, Power BI, or even a spreadsheet with sample prompts for ChatGPT).
• Create a brief document explaining the company's sales process and typical buyer journey.
• Allocate 60-90 minutes for this exercise.
• Have a subject matter expert available to answer clarifying questions about the data or tools.

Directions for the Candidate:

• Review the provided deal data and sales process documentation.
• Use the AI tool to analyze patterns in won versus lost deals, identifying at least 3-5 key factors that appear to influence outcomes.
• Create a brief analysis document (1-2 pages) that outlines:
• The methodology you used to analyze the data
• Key patterns discovered in won versus lost deals
• Surprising or counter-intuitive findings
• Initial hypotheses about why these patterns exist
• Recommendations for further investigation
• Be prepared to walk through your analysis process and findings in a 15-minute presentation.

Feedback Mechanism:

• After the presentation, provide specific feedback on one strength of the candidate's analysis approach or findings.
• Offer one constructive suggestion for improvement, such as additional factors they could have considered or alternative ways to interpret the data.
• Give the candidate 15 minutes to refine one aspect of their analysis based on this feedback, then have them explain how this new perspective changes their conclusions.

Activity #2: Stakeholder Interview Design and Role Play

This exercise assesses the candidate's ability to design effective win/loss interview protocols and conduct insightful conversations with stakeholders. It evaluates their understanding of what questions yield valuable insights, their interviewing skills, and their ability to adapt their approach based on the conversation flow.

Directions for the Company:

• Create a fictional but realistic customer profile for a recently lost deal, including:
• Company background and needs
• The solution they were considering
• Key stakeholders involved in the decision
• Timeline of the sales process
• Known factors that influenced their decision
• Assign a team member to role-play as the customer's decision-maker.
• Provide the candidate with basic information about the deal but withhold some key insights that they should uncover during the interview.
• Allow 30 minutes for preparation and 30 minutes for the interview role play.

Directions for the Candidate:

• Review the information provided about the lost deal.
• Develop an interview protocol with 10-15 questions designed to uncover:
• The customer's decision-making process
• Key factors that influenced their decision
• How they evaluated competing solutions
• What could have been done differently to win the business
• Conduct a 30-minute interview with the customer stakeholder (role-played by a team member).
• Take notes during the interview to capture key insights.
• After the interview, spend 15 minutes preparing a brief summary of the most important findings and how they might inform future sales approaches.

Feedback Mechanism:

• Provide feedback on one aspect of the interview that was particularly effective at uncovering valuable insights.
• Suggest one area where the questioning approach could be improved to gather more useful information.
• Ask the candidate to revise 2-3 of their questions based on this feedback and explain how these improved questions would yield better insights.

Activity #3: AI-Powered Pattern Recognition and Insight Generation

This exercise tests the candidate's ability to use AI tools to identify non-obvious patterns in win/loss data and generate actionable insights. It evaluates their creativity in prompt engineering, their critical thinking about AI-generated insights, and their ability to distinguish correlation from causation.

Directions for the Company:

• Prepare a more complex dataset of 50+ deals with multiple variables including:
• Standard deal attributes (size, industry, etc.)
• Communication data (number of emails, calls, meetings)
• Stakeholder information (roles involved, decision-making structure)
• Competitive information (alternatives considered)
• Timeline data (time spent in each sales stage)
• Provide access to an AI analysis tool or large language model (like ChatGPT, Claude, or a specialized win/loss analysis platform).
• Include some deliberate "red herring" correlations in the data that don't represent causal relationships.
• Allow 2 hours for this exercise.

Directions for the Candidate:

• Analyze the provided dataset using AI tools to identify patterns that distinguish won from lost deals.
• Document your approach to prompt engineering—how you structured queries to the AI system to extract meaningful insights.
• Identify at least 3 non-obvious patterns in the data that might influence deal outcomes.
• For each pattern:
• Describe the evidence supporting this pattern
• Assess whether it likely represents correlation or causation
• Explain how you would validate this insight further
• Outline potential business implications if this pattern is valid
• Create a one-page executive summary of your most significant findings and their potential impact on win rates.

Feedback Mechanism:

• Highlight one particularly insightful pattern the candidate identified or an effective prompt engineering technique they used.
• Suggest one area where they could have been more critical of AI-generated insights or where they might have missed an important consideration.
• Give the candidate 20 minutes to refine their analysis based on this feedback, focusing specifically on better distinguishing correlation from causation in one of their identified patterns.

Activity #4: Strategic Recommendation Development

This exercise evaluates the candidate's ability to translate win/loss insights into actionable strategic recommendations. It tests their business acumen, their understanding of how insights connect to business processes, and their ability to prioritize recommendations for maximum impact.

Directions for the Company:

• Create a comprehensive win/loss analysis report with multiple findings across different areas:
• Product features and capabilities
• Sales process and methodology
• Competitive positioning
• Pricing and packaging
• Customer success and implementation
• Include supporting data and quotes for each finding.
• Provide context about the company's current strategic priorities and resource constraints.
• Allow 90 minutes for this exercise.

Directions for the Candidate:

• Review the provided win/loss analysis report.
• Develop a strategic recommendation plan that includes:
• 3-5 high-priority recommendations based on the win/loss insights
• Clear rationale for why each recommendation would improve win rates
• Specific metrics to track for measuring the impact of each recommendation
• Estimated level of effort and potential ROI for each recommendation
• Suggested implementation timeline and key stakeholders to involve
• Create a compelling executive presentation (5-7 slides) that communicates these recommendations effectively.
• Be prepared to present and defend your recommendations in a 20-minute presentation.

Feedback Mechanism:

• Provide feedback on one recommendation that was particularly well-supported by the data and likely to have significant impact.
• Suggest one area where the recommendation could be more specific, actionable, or better aligned with business constraints.
• Ask the candidate to revise this recommendation based on the feedback and explain how the revised approach would be more effective or implementable.

Frequently Asked Questions

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

Each exercise requires different time commitments. Activity #1 needs 60-90 minutes, Activity #2 about 75 minutes, Activity #3 approximately 2 hours, and Activity #4 around 2 hours including presentation time. Consider spreading these across different interview stages or selecting the 1-2 most relevant to your specific needs rather than using all four.

Should we use real company data for these exercises?

While using real data provides the most authentic assessment, it often raises confidentiality concerns. We recommend creating realistic synthetic data based on your actual win/loss patterns but with identifying information removed. This gives candidates a representative experience without exposing sensitive information.

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

Focus on evaluating their analytical approach rather than tool-specific knowledge. Provide a brief orientation to your tools or allow them to use familiar alternatives. The key is assessing their ability to leverage AI for insight generation, not their proficiency with particular software.

How should we evaluate candidates who take different approaches to these exercises?

Establish evaluation criteria focused on outcomes rather than specific methods. For example, assess the quality and actionability of insights rather than whether they used the exact analytical technique you expected. Different approaches often reveal diverse strengths that could benefit your team.

What if we don't currently have a formal win/loss analysis program?

These exercises can still be valuable. Focus on Activities #1 and #2, which test fundamental skills in data analysis and stakeholder interviewing. You can simplify the exercises to match your current data availability while still evaluating candidates' potential to build a more sophisticated program.

Should we expect candidates to have both AI expertise and sales/business knowledge?

The ideal candidate will have strengths in both areas, but this combination is rare. Consider which is more important for your specific role and evaluate accordingly. Technical skills can be developed more easily than business acumen in some cases, or vice versa depending on your team's current composition.

AI-powered win/loss analysis represents a significant competitive advantage for organizations that implement it effectively. By using these work samples in your hiring process, you'll identify candidates who can truly leverage AI to transform your understanding of market dynamics and customer decision-making. The right hire will not only analyze past performance but provide actionable insights that measurably improve future win rates.

For more resources to optimize 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 organization's needs.

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