1. Performance Benchmark: A quantitative comparison of AI vs. Human influencers on core KPIs (trust, engagement, purchase intent).
2. Audience Targeting Guide: A detailed profile of the demographic and psychographic segments most (and least) receptive to AI influencers.
3. Disclosure Best Practices: A clear "how-to" guide on disclosing an influencer's AI nature to maximize audience trust and minimize negative sentiment.
4. Strategic Go/No-Go Recommendation: A clear recommendation on when, why, and how to deploy AI influencers as part of a balanced marketing mix.

Recommended Frameworks: A/B Testing & STP

• Framework Teaching:
  • A/B Testing: This is a controlled experimental method used to compare two versions of a single variable to see which performs better. You create a control group (Version A) and a treatment group (Version B) and measure which one more effectively achieves a specific goal (e.g., higher engagement). It's a powerful way to get definitive, quantitative answers to "what if" questions.
  • STP (Segmentation, Targeting, Positioning): This is a foundational strategic marketing framework.
    1. Segmentation: You divide a broad market into smaller, distinct groups of consumers based on shared characteristics (e.g., demographics, needs, behaviors).
    2. Targeting: You evaluate the attractiveness of each segment and decide which one(s) to focus your efforts on.
    3. Positioning: You design your product, message, and marketing to occupy a clear and desirable place in the minds of your target consumers.
• Applicability Explanation:
  • A/B testing is perfectly suited to directly answer your core questions. It will allow you to isolate the impact of the influencer type (Human vs. AI) and the disclosure practice (Disclosed vs. Undisclosed) on audience response.
  • The STP framework provides a structure to move from raw demographic data to actionable strategy. It ensures you don't just find out that different groups react differently, but it guides you on which groups to prioritize and how to communicate with them effectively.

Part 1: Information Collection

A. Web Search

• Search Queries:
  • "AI influencer vs human influencer engagement statistics 2025"
  • "Consumer trust in AI influencers research 2025"
  • "Impact of AI disclosure on brand trust statistics"
  • "Case studies of brand campaigns with AI influencers"
• Purpose of this Step:
  • Establish Baselines: This search will provide industry benchmarks for engagement, trust, and costs. For example, knowing that human influencers currently generate about 2.7 times more engagement helps contextualize the results of our own A/B test.
  • Refine Hypotheses: Insights from existing research—such as Gen Z being more receptive to AI influencers or that 84% of experts favor mandatory disclosure—help us form sharper, more nuanced hypotheses for our experiment.
  • Identify Best Practices: Reviewing case studies from brands like Samsung and Maybelline can reveal successful strategies and common pitfalls, particularly regarding creative execution and disclosure.

B. User Research: An Experimental Approach

• Research Setup (A/B Testing):
  1. Create Stimuli: Design a simple, neutral social media post (e.g., an Instagram post for a fictional lifestyle brand). Create two identical versions of this post: one featuring a real human influencer and one featuring a photorealistic AI influencer.
  2. Form Test Groups: Recruit a statistically significant and demographically diverse pool of social media users. Divide them randomly into three equal groups:
     • Group A (Control): Sees the post from the human influencer.
     • Group B (AI Undisclosed): Sees the post from the AI influencer with no disclosure.
     • Group C (AI Disclosed): Sees the post from the AI influencer with a clear, upfront disclosure (e.g., "#AIinfluencer" or "Meet our virtual partner...").
• Interview/Survey Subjects: Social media users from a range of demographics (specifically focusing on age, gender, and frequency of social media use).
• Purpose of Interviews/Surveys: To collect quantitative and qualitative data on trust, perception, and engagement immediately after participants view their assigned post.
• Core Survey Questions to Ask Post-Exposure:
  • Demographics: "What is your age range?", "What gender do you identify with?", etc.
    • Analysis Purpose: This data is crucial for the Segmentation step of the STP framework.
  • Trust & Authenticity (1-5 Scale): "How authentic did this influencer feel?", "How trustworthy was the product recommendation?", "How relatable did you find this creator?"
    • Analysis Purpose: These scores will be the primary metrics for comparing trust between Groups A, B, and C.
  • Engagement & Purchase Intent (1-5 Scale): "How likely would you be to 'like' or 'follow' this creator?", "How likely would you be to consider purchasing this product?"
    • Analysis Purpose: These scores will measure the direct impact on business-relevant actions.
  • Open-Ended Feedback: "What were your first impressions of this post?", "Is there anything else you noticed?"
    • Analysis Purpose: This qualitative data will add color and context, helping to explain why we are seeing certain quantitative results.

Part 2: Information Analysis

A. Step 1: Comparative Analysis (A/B Test Results)

• Human vs. AI Effectiveness: Compare the average scores (Trust, Authenticity, Engagement Intent) of Group A (Human) with Group B (AI Undisclosed). This will give you a stark, quantitative measure of performance differences when all else is equal.
• Impact of Disclosure: Compare the average scores of Group B (AI Undisclosed) with Group C (AI Disclosed). This will reveal the "cost" or "benefit" of transparency. Does disclosure increase trust, or does it hurt engagement by breaking the illusion? This is a critical strategic question.

B. Step 2: Segmentation

• How-to: Take all your survey responses and filter them by demographic criteria. For instance, create cohorts like "Women, 18-24," "Men, 35-44," etc.
• Analysis Goal: Look for patterns. Does the trust gap between human and AI influencers shrink with younger audiences, as some reports suggest? Is the negative reaction to non-disclosure stronger among older users?
• Output: You will create 2-4 key audience segments, giving them descriptive names like "The AI-Native Enthusiast" (e.g., younger, tech-savvy users who care more about aesthetics than authenticity) and "The Authenticity Seeker" (e.g., users who value genuine connection and are highly skeptical of undisclosed AI).

C. Step 3: Targeting and Positioning

• Targeting: Evaluate the segments you created. Which segment is the largest? Which one shows the highest purchase intent? Which one aligns best with the brand's goals? You will select one or two "priority segments" to focus on.
• Positioning: For each priority segment, you will develop a specific positioning strategy.
  • For "AI-Native Enthusiasts": The analysis might suggest positioning an AI influencer as a cutting-edge, creative, and visually perfect partner. Transparency might be a footnote.
  • For "Authenticity Seekers": The data might show that the only way to win their trust is with radical transparency. The positioning here would be to frame the AI influencer as an innovative and helpful tool, not a replacement for a human, and to lead with that disclosure.