Effective Work Samples to Evaluate AI-Powered A/B Test Hypothesis Generation Skills

In today's data-driven business landscape, A/B testing has become a fundamental practice for optimizing digital experiences. However, the traditional approach to generating test hypotheses often relies heavily on human intuition, which can be limited by cognitive biases and knowledge gaps. This is where artificial intelligence enters the picture, revolutionizing how organizations develop and prioritize A/B test hypotheses.

Professionals skilled in using AI for A/B test hypothesis generation bring tremendous value to organizations by accelerating the testing process, uncovering non-obvious patterns, and generating more innovative ideas than traditional methods alone. These individuals combine technical expertise in data analysis with strategic thinking and an understanding of how to effectively prompt and utilize AI tools.

When hiring for roles requiring this specialized skill set, traditional interviews often fall short in revealing a candidate's true capabilities. Theoretical questions may demonstrate knowledge, but they don't showcase the practical application of AI tools in real testing scenarios. This is why work samples are essential for evaluating candidates' proficiency in using AI for A/B test hypothesis generation.

The following four activities are designed to assess candidates' abilities to leverage AI tools for generating meaningful test hypotheses, analyze data patterns, communicate insights effectively, and apply strategic thinking to testing programs. These exercises simulate real-world scenarios that professionals in this field encounter regularly, providing a window into how candidates would perform on the job.

By incorporating these work samples into your hiring process, you'll gain deeper insights into candidates' capabilities beyond what resumes and traditional interviews reveal. You'll identify individuals who not only understand the principles of A/B testing and AI but can also apply these tools effectively to drive business results.

Activity #1: AI-Powered Hypothesis Generation for Conversion Rate Optimization

This activity evaluates a candidate's ability to use AI tools to generate meaningful A/B test hypotheses for improving conversion rates on a digital platform. It assesses their understanding of conversion principles, their skill in crafting effective AI prompts, and their ability to evaluate and prioritize AI-generated hypotheses based on potential impact and feasibility.

Directions for the Company:

• Provide the candidate with access to a sample website or app screenshot (preferably from your actual product or a similar one in your industry) along with basic analytics data showing conversion rates at different funnel stages.
• Include information about the target audience and business objectives.
• Allow the candidate to use an AI tool of their choice (e.g., ChatGPT, Claude, Bard) or provide access to your company's preferred AI platform.
• Allocate 30-45 minutes for this exercise.
• Prepare a list of common constraints or limitations that would affect implementation (e.g., technical limitations, brand guidelines, regulatory considerations).

Directions for the Candidate:

• Review the provided website/app and analytics data to identify potential conversion bottlenecks.
• Use an AI tool to generate at least 10 hypothesis ideas for A/B tests that could improve conversion rates.
• Document the prompts you used to generate these hypotheses and explain your prompt engineering approach.
• Evaluate the AI-generated hypotheses and select the top 3 you would recommend testing first.
• For each selected hypothesis, provide:
• The specific element to test
• The proposed change
• The expected impact on user behavior
• How you would measure success
• Any potential risks or considerations

Feedback Mechanism:

• After the candidate presents their top 3 hypotheses, provide feedback on one aspect they did well (e.g., creative prompt engineering, strategic prioritization) and one area for improvement (e.g., overlooking a key user motivation, not considering technical feasibility).
• Give the candidate 10 minutes to refine one of their hypotheses based on your feedback, demonstrating their ability to iterate and incorporate new information.

Activity #2: Pattern Recognition and Data-Informed AI Prompting

This exercise tests a candidate's ability to analyze past A/B test results and use those insights to inform more effective AI prompts for hypothesis generation. It evaluates their data analysis skills, pattern recognition abilities, and capacity to translate historical learnings into forward-looking test ideas.

Directions for the Company:

• Prepare a dataset of 5-10 previous A/B tests your company has run, including:
• The hypothesis tested
• Key metrics before and after
• Whether the test was successful or not
• Any notable insights gained
• If using actual company data isn't possible, create a realistic mock dataset that reflects common patterns in your industry.
• Provide access to an AI tool or platform.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the provided A/B test history to identify patterns in what has and hasn't worked.
• Analyze the data to determine:
• Common characteristics of successful tests
• Potential reasons why unsuccessful tests failed
• Untested areas that might present opportunities
• Based on your analysis, craft 3-5 sophisticated AI prompts designed to generate hypotheses that build on past successes and address gaps.
• Use these prompts with an AI tool to generate a new set of test hypotheses.
• Present your analysis of the historical data, explain your prompt engineering approach, and share the most promising AI-generated hypotheses.
• Explain how your approach to AI prompting was informed by the patterns you identified in the data.

Feedback Mechanism:

• Provide feedback on the candidate's data analysis approach and their ability to translate insights into effective AI prompts.
• Highlight one strength in their methodology and one area where their analysis could be more comprehensive or their prompts more effective.
• Ask the candidate to refine one of their AI prompts based on your feedback and generate a new set of hypotheses, explaining how the refined prompt addresses the feedback.

Activity #3: Cross-Channel Hypothesis Development with AI

This activity assesses a candidate's ability to use AI for developing coordinated A/B test hypotheses across multiple channels or touchpoints. It evaluates their understanding of the customer journey, their strategic thinking about cross-channel experiences, and their skill in using AI to identify testing opportunities that span different platforms.

Directions for the Company:

• Prepare a simplified customer journey map showing key touchpoints across 3-4 channels (e.g., website, email, mobile app, paid ads).
• Include basic performance metrics for each channel and touchpoint.
• Identify a specific business goal that spans multiple channels (e.g., increasing free trial conversions, reducing cart abandonment).
• Provide access to an AI tool.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the customer journey map and channel performance data.
• Use an AI tool to help identify potential friction points or opportunities across the journey.
• Develop a coordinated testing strategy that includes:
• 2-3 hypotheses for each channel that support the overall business goal
• How these tests would work together across channels
• How you would sequence or prioritize these tests
• Document the AI prompts you used and explain how you guided the AI to think across channels rather than in silos.
• Present your strategy, highlighting how AI helped you identify non-obvious connections or opportunities across the customer journey.

Feedback Mechanism:

• Provide feedback on the candidate's cross-channel thinking and their ability to use AI to connect dots across different touchpoints.
• Highlight one particularly strong hypothesis and one area where their approach could be more integrated or strategic.
• Ask the candidate to take 10 minutes to refine their testing strategy based on your feedback, focusing specifically on strengthening the connections between tests across different channels.

Activity #4: AI-Assisted Competitive Analysis for Testing Opportunities

This exercise evaluates a candidate's ability to use AI tools to analyze competitors' digital experiences and generate testing hypotheses based on competitive insights. It assesses their competitive analysis skills, their ability to identify transferable patterns, and their judgment in adapting competitive approaches to your specific context.

Directions for the Company:

• Identify 2-3 key competitors in your space.
• Provide screenshots or access to these competitors' digital properties (websites, apps, etc.).
• Include any publicly available information about their recent changes or optimizations.
• Provide access to an AI tool.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the competitors' digital experiences, noting key differences from your company's approach.
• Use an AI tool to help analyze these differences and identify potential testing opportunities based on competitor strategies.
• For each competitor, develop:
• 3-5 hypotheses inspired by their approach that could be tested on your platform
• An analysis of why these elements might be working for the competitor
• How you would adapt and test these approaches for your specific audience and brand
• Document the AI prompts you used to analyze the competitive landscape and generate hypotheses.
• Present your findings, explaining how you used AI to extract meaningful insights rather than simply suggesting direct copying of competitor approaches.

Feedback Mechanism:

• Provide feedback on the candidate's competitive analysis skills and their ability to use AI to extract actionable insights rather than surface-level observations.
• Highlight one particularly insightful adaptation and one area where their analysis could be deeper or their adaptation more thoughtful.
• Ask the candidate to spend 10 minutes refining one of their hypotheses based on your feedback, demonstrating how they would make it more tailored to your specific audience and business context.

Frequently Asked Questions

How long should we allocate for these work sample exercises?

Each exercise is designed to take 45-60 minutes, including time for the candidate to present their work and receive feedback. If you're incorporating multiple exercises into your interview process, consider spreading them across different interview stages or condensing the scope of each activity to focus on specific skills you want to evaluate.

Should we provide candidates with access to specific AI tools, or let them use their preferred platforms?

If your company uses specific AI tools that would be part of the role, provide access to those platforms. Otherwise, allowing candidates to use familiar tools like ChatGPT, Claude, or Bard gives them the best opportunity to demonstrate their skills without the learning curve of a new platform. The key is evaluating their prompt engineering and critical thinking, not their familiarity with a specific tool.

How technical should candidates be to complete these exercises successfully?

These exercises are designed for individuals with varying technical backgrounds. The focus is on strategic thinking, effective AI prompting, and testing methodology rather than coding or data science expertise. However, candidates should have a solid understanding of A/B testing principles and metrics. Adjust the technical depth based on the specific requirements of your role.

What if we don't have real A/B testing data to share with candidates?

If sharing actual company data isn't possible due to confidentiality concerns, create realistic mock data that reflects common patterns in your industry. The goal is to assess the candidate's analytical approach and AI utilization, which can be demonstrated with simulated data. Alternatively, you can use anonymized or aggregated data that doesn't reveal sensitive information.

How should we evaluate candidates who use different AI tools or approaches?

Focus on the quality of their thinking and results rather than their specific tool choice. Evaluate their prompt engineering creativity, their critical assessment of AI outputs, and their ability to translate AI-generated ideas into practical testing hypotheses. The best candidates will demonstrate thoughtful interaction with AI tools, not just acceptance of whatever the AI suggests.

Can these exercises be adapted for remote interviews?

Absolutely. These exercises work well in remote settings using screen sharing and collaborative documents. For remote interviews, consider providing materials in advance and using video conferencing tools that allow for screen sharing during the presentation portion. You might also extend the time slightly to account for potential technical issues.

The ability to effectively leverage AI for A/B test hypothesis generation represents a powerful competitive advantage in today's digital landscape. By incorporating these work samples into your hiring process, you'll identify candidates who can not only use AI tools but can do so strategically to drive meaningful business outcomes through more effective testing programs.

The most successful candidates will demonstrate a balance of technical understanding, strategic thinking, and practical application. They'll show how AI can augment human creativity and analytical capabilities rather than replace them, using these tools to overcome common limitations in traditional hypothesis generation approaches.

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

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