Effective Work Samples for Evaluating AI Skills in Influencer Marketing

The intersection of artificial intelligence and influencer marketing represents one of the most dynamic and high-impact areas in modern digital marketing. Companies that effectively leverage AI for influencer selection and ROI measurement gain significant competitive advantages through more precise targeting, better campaign outcomes, and clearer attribution of marketing spend. However, identifying professionals who truly possess both the technical AI capabilities and the marketing acumen to drive these initiatives can be challenging.

Traditional interviews often fail to reveal a candidate's actual ability to apply AI concepts to real-world influencer marketing challenges. Resumes may list impressive technical skills or marketing experience, but they rarely demonstrate how effectively a candidate can bridge these domains. This gap between stated qualifications and practical application can lead to costly hiring mistakes and underperforming marketing initiatives.

Work samples and practical exercises provide a window into how candidates approach complex problems at this intersection of technology and marketing. By observing candidates as they analyze data, build models, and develop measurement frameworks, hiring managers can gain invaluable insights into their problem-solving processes, technical capabilities, and marketing intuition.

The following four exercises are designed to evaluate a candidate's ability to apply AI concepts specifically to influencer selection and ROI measurement challenges. These activities simulate real-world scenarios that professionals in this field encounter, allowing you to assess not just what candidates know, but how they apply that knowledge to drive business results through more effective influencer marketing strategies.

Activity #1: Influencer Performance Data Analysis

This exercise evaluates a candidate's ability to extract meaningful insights from influencer campaign data using AI techniques. It tests their analytical skills, understanding of influencer marketing metrics, and ability to translate data findings into actionable recommendations. This skill is fundamental for anyone responsible for using AI to optimize influencer selection and measure campaign effectiveness.

Directions for the Company:

• Prepare a sanitized dataset of past influencer campaigns that includes metrics such as engagement rates, conversion data, audience demographics, content types, and ROI figures.
• Include some "noise" in the data to test the candidate's ability to identify relevant patterns versus coincidental correlations.
• Provide access to a data analysis environment (could be Excel, Python notebook, or your company's preferred analytics platform).
• Allow 60-90 minutes for this exercise.
• Have a marketing team member and a data science team member available to evaluate the results.

Directions for the Candidate:

• Analyze the provided influencer campaign dataset to identify patterns that could inform future influencer selection.
• Use appropriate statistical methods or machine learning techniques to identify which influencer characteristics and content strategies correlate most strongly with desired outcomes.
• Prepare a brief presentation (5-7 slides) that outlines:

1. Key patterns discovered in the data
2. Recommendations for influencer selection criteria based on these patterns
3. Suggestions for how AI could be used to automate or enhance this selection process
4. Limitations of the current dataset and what additional data would strengthen your analysis

Feedback Mechanism:

• After the presentation, provide feedback on one analytical approach the candidate executed well and one area where their analysis could be improved or expanded.
• Give the candidate 15 minutes to refine one aspect of their analysis or recommendations based on this feedback.
• Observe how they incorporate the feedback and whether they can quickly adapt their approach.

Activity #2: AI Model Evaluation for Influencer Selection

This exercise assesses a candidate's ability to critically evaluate AI models for influencer selection. It tests their understanding of machine learning concepts, model evaluation metrics, and how these technical elements connect to business objectives in influencer marketing.

Directions for the Company:

• Prepare documentation for two different AI approaches to influencer selection:

1. A collaborative filtering model that recommends influencers based on similar successful past campaigns
2. A content-based model that matches influencer content style and audience with brand requirements

• Include model performance metrics, training data descriptions, and sample outputs for each.
• Prepare a brief on a fictional upcoming campaign with specific goals and constraints.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the documentation for both AI models for influencer selection.
• Evaluate the strengths and weaknesses of each approach for the specific campaign described.
• Recommend which model would be more appropriate for this campaign, or suggest a hybrid approach.
• Identify potential biases or limitations in the models and how they might be addressed.
• Prepare a 10-minute verbal presentation explaining your evaluation and recommendations, including:

1. Key evaluation criteria you used
2. How the recommended approach aligns with campaign objectives
3. Potential implementation challenges
4. How you would measure the model's success once deployed

Feedback Mechanism:

• Provide feedback on one aspect of the candidate's model evaluation that demonstrated strong technical understanding and one area where their evaluation could be more comprehensive.
• Ask the candidate to spend 10 minutes addressing how they would modify their recommendation based on a new constraint (e.g., "What if we learned the campaign needed to launch in 2 weeks instead of 2 months?").
• Evaluate their ability to adapt their technical recommendation to changing business requirements.

Activity #3: AI-Driven Influencer Selection System Planning

This exercise evaluates a candidate's ability to plan and architect an AI system for influencer selection. It tests their strategic thinking, technical planning capabilities, and understanding of how to integrate AI into existing marketing workflows.

Directions for the Company:

• Prepare a brief describing your company's current influencer selection process, available data sources, and key challenges.
• Include information about existing marketing technology stack and team structure.
• Provide a whiteboard or digital collaboration tool.
• Allow 60-75 minutes for this exercise.
• Have a technical lead and marketing stakeholder available to participate.

Directions for the Candidate:

• Review the information about the company's current influencer selection process and challenges.
• Develop a high-level plan for implementing an AI-driven influencer selection system that addresses these challenges.
• Create a system architecture diagram showing:

1. Data sources and data flow
2. Key AI/ML components
3. Integration points with existing systems
4. User interfaces for marketing team interaction

• Outline an implementation roadmap with key milestones and dependencies.
• Identify potential technical and organizational challenges and propose mitigation strategies.
• Present your plan to the interview team, explaining your rationale for key decisions.

Feedback Mechanism:

• Provide feedback on one aspect of the system design that effectively addresses business needs and one area where the design could be improved for practical implementation.
• Ask the candidate to spend 15 minutes revising one component of their plan based on a specific constraint or requirement not previously mentioned (e.g., "Our legal team requires all influencer data processing to happen on-premises rather than in the cloud").
• Evaluate their ability to adapt their technical solution while maintaining the core business value.

Activity #4: ROI Measurement Framework Development

This exercise assesses a candidate's ability to develop a comprehensive framework for measuring influencer marketing ROI using AI. It tests their understanding of marketing attribution, AI-based measurement techniques, and ability to connect technical capabilities to business metrics.

Directions for the Company:

• Prepare a case study of a complex influencer marketing campaign with multiple touchpoints, influencer types, and conversion paths.
• Include information about available data sources, tracking capabilities, and business KPIs.
• Provide examples of current ROI measurement challenges.
• Allow 60 minutes for this exercise.

Directions for the Candidate:

• Review the case study materials on the complex influencer campaign.
• Develop a framework for measuring the ROI of this campaign using AI-enhanced approaches.
• Your framework should address:

1. Attribution modeling approaches that could be implemented using AI
2. Methods for isolating influencer impact from other marketing channels
3. Techniques for measuring both direct conversions and brand lift
4. Approaches for optimizing measurement in real-time during campaigns

• Create a one-page visual representation of your measurement framework.
• Prepare a 15-minute presentation explaining how your framework works, what AI techniques it employs, and how it improves upon traditional measurement approaches.

Feedback Mechanism:

• Provide feedback on one innovative aspect of the candidate's measurement framework and one area where the approach might face practical implementation challenges.
• Ask the candidate to spend 10 minutes addressing how they would modify their framework to account for a specific measurement challenge (e.g., "How would your approach change if 40% of conversions happen offline?").
• Evaluate their ability to adapt their technical solution to real-world constraints while maintaining measurement integrity.

Frequently Asked Questions

How technical should candidates be to complete these exercises?

Candidates should have a working knowledge of AI/ML concepts and techniques, but they don't necessarily need to be able to code complex algorithms from scratch. The focus is on their ability to apply AI concepts to influencer marketing challenges, evaluate appropriate approaches, and design systems that deliver business value. Data scientists may go deeper on technical implementation, while marketing technologists might focus more on application and integration.

Should we expect candidates to complete all four exercises?

No, these exercises are designed to be used selectively based on the specific role and the candidate's background. Choose 1-2 exercises that best align with the core responsibilities of your position. For more senior roles, you might use Activity #3 (System Planning) to evaluate strategic thinking, while for more analytical roles, Activities #1 or #4 might be more appropriate.

How should we adapt these exercises for candidates with stronger marketing backgrounds versus those with stronger technical backgrounds?

For candidates with stronger marketing backgrounds, you might provide more structure in the technical aspects of the exercises and evaluate them more on their ability to connect AI capabilities to marketing outcomes. For technically stronger candidates, you might challenge them more on understanding the nuances of influencer marketing and how technical solutions translate to business impact.

What if we don't have real influencer data to use for these exercises?

You can create synthetic data that mimics the patterns and challenges of real influencer campaigns. Alternatively, you can use publicly available case studies or anonymized industry benchmarks. The key is to create realistic scenarios that test the candidate's ability to apply AI concepts to influencer marketing challenges, even if the specific data points are simulated.

How do we evaluate candidates who propose approaches different from what we currently use?

This can actually be valuable! Evaluate candidates on the soundness of their reasoning, not on whether they arrived at a predetermined "correct" answer. A candidate who proposes a well-reasoned alternative approach might bring fresh perspectives that could improve your current processes. Focus on whether their solution addresses the core business needs and demonstrates an understanding of both the technical and marketing aspects of the challenge.

Should we share our current AI approaches with candidates during these exercises?

It's generally better to evaluate candidates' independent thinking first, then discuss your current approaches during the feedback phase. This allows you to see how they approach problems without being influenced by existing solutions, while still giving you an opportunity to assess how they might work within your established frameworks.

The ability to effectively apply AI to influencer selection and ROI measurement represents a specialized skill set that can dramatically improve marketing outcomes. By using these practical work samples, you can identify candidates who not only understand the technical aspects of AI but can also apply these technologies to solve real business challenges in influencer marketing.

These exercises go beyond theoretical knowledge to reveal how candidates approach complex problems, adapt to feedback, and balance technical capabilities with business objectives. The insights gained from observing candidates work through these scenarios will help you identify professionals who can truly drive innovation and results at this critical intersection of technology and marketing.

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.

Build a complete interview guide for AI in Influencer Selection and ROI Measurement by signing up for a free Yardstick account