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Ronit Chawla
Ronit Chawla

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InfluenceIQ: The Ultimate AI Tool for Smarter Influencer Marketing

This is a submission for the Agent.ai Challenge: Full-Stack Agent (See Details)

InfluenceIQ is an AI-driven analytics platform designed to streamline influencer marketing decisions. It combines data scraping, machine learning, and LLM-powered insights to evaluate influencers' suitability for brands. By analyzing audience alignment, content relevance, and risk factors, it replaces guesswork with actionable recommendations, enabling businesses to optimize campaign ROI.

What I Built

Problems Addressed:

  1. Inefficiency: Manual vetting of influencers is time-consuming and error-prone.
  2. Misalignment: Brands often prioritize follower counts over meaningful metrics (e.g., audience demographics).
  3. Risk Exposure: Fake followers, controversies, and mismatched audiences lead to wasted budgets.
  4. Accessibility Gap: Small businesses lack affordable tools for data-driven influencer analysis.

Solution:

  • Automated Analysis: Scrapes social platforms for metrics like engagement rate, audience demographics, and content trends.
  • LLM Contextualization: Interprets unstructured data (tone, brand affinity) to generate plain-language insights.
  • Risk Mitigation: Flags fake followers, audience mismatch, and past controversies.

🚀 Envisioned Use Cases

For Brands:

  • Vet influencers using criteria like "Find eco-friendly micro-influencers with Gen-Z audiences."
  • Simulate campaign ROI and compare influencers side-by-side.
  • Avoid partnerships with misaligned influencers.

For Agencies:

  • Bulk-analyze influencers for multiple clients.
  • Generate client-ready reports with visual dashboards.
  • Track performance trends to renegotiate contracts.

For Influencers:

  • Audit profiles to improve brand appeal (e.g., "Focus on Instagram Reels over static posts").
  • Benchmark against competitors in their niche.

🔍 Unique Value Proposition

Feature Description
AI Contextual Analysis Evaluates tone, aesthetic alignment, and audience sentiment (e.g., "@FoodieQueen’s audience engages 2x more with vegan recipes").
Ethical Compliance GDPR/CCPA-compliant data handling and adherence to platform scraping policies.

🌟 Vision for Impact

  • Smarter Budgets: Redirect spend from mismatched mega-influencers to high-conversion niche creators.
  • Long-Term Growth: Identify influencers who align with a brand’s evolving identity.
  • Global Reach: Expand to regional platforms (e.g., Douyin, KakaoTalk) for cross-border campaigns.

📋 Example Scenario

A skincare startup wants to promote a new acne treatment:

  1. Input: Brand values = "clean beauty, Gen-Z focus".
  2. Analysis:
    • @HonestGlow: 45k followers, 8% engagement, authentic reviews.
    • 🚩 @GlamSkin: 500k followers, 20% fake followers detected.
  3. Outcome: Startup partners with @HonestGlow, avoiding a $10k mistake.

Demo

Visit InfluenceIQ

Demo Video

Agent.ai Experience

Delightful: Seeing complex data (like influencer metrics) transform into actionable insights through LLM magic – it’s like watching raw data become strategy! 🎯
Challenging: Fine-tuning prompts to balance specificity and flexibility (e.g., avoiding "analysis paralysis" in outputs). But every hurdle taught me to "think like the LLM"! 💡

Solo-Hack :@ronit_chawla_88d33416a2cd

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