Inspiration
The traditional process for planning podcast marketing campaigns is slow and labor-intensive. Analysts spend days gathering market stats, building personas, and manually researching and shortlisting shows—often across dozens of tabs, spreadsheets, and dashboards. We built AudienceIQ to automate these “hidden” manual steps, letting teams focus on strategy and creative, not grunt work.
What it does
AudienceIQ transforms the early phases of podcast campaign planning (Phases 1–4) by:
- Instantly generating intake templates and auto-pulling audience personas from live data sources
- Using AI-powered discovery to match brands to relevant podcasts, replacing hours of manual list building and data entry
- Scoring and ranking shows on reach, relevance, and safety, so analysts don’t have to sift through hundreds of titles manually
- Providing scenario modeling tools with live CPM benchmarks, eliminating the need for complex spreadsheets
- Exporting all research, insights, and recommendations directly to PowerPoint decks—no more manual copy-paste or slide building
With AudienceIQ, what used to take 2–3 days of hands-on analyst work can now be done in a few hours, with client-ready PPTs generated in one click.
How we built it
- Frontend: React + TypeScript + Vite
- Backend: Node.js + Express + TypeScript
- Database: IndexedDB (with migration support)
- API Integrations: QLOO, YouTube, OpenRouter LLM
- Deployment: Docker, Docker Compose, Nginx
Key features are mapped directly to the most effort-intensive workflow phases:
- Multi-category audience selection
- Automated persona and market insight generation
- AI-driven podcast discovery and shortlisting
- Scenario modeling with real CPM data
- Automated export of all findings and recommendations to PowerPoint (PPT) for easy client reporting
Challenges we ran into
- Rapid Prototyping: Building a robust, full-stack product under tight hackathon time constraints
- API Integration: Connecting to multiple third-party APIs (QLOO, YouTube, OpenRouter) with inconsistent data formats and rate limits
- Data Modeling: Structuring data to support dynamic audience types, show rankings, and scenario modeling
- User Experience: Designing an intuitive, streamlined UI for complex research and planning workflows
- PPT Export Reliability: Ensuring all research and recommendations export cleanly and consistently to PowerPoint, even as features evolved
Accomplishments that we're proud of
- Reduced the time required for campaign research and shortlisting from days to hours
- Automated persona generation and scenario building, eliminating repetitive spreadsheet work
- Enabled rapid, data-driven decision making for campaign planning teams
- Built a tool that fits seamlessly into the real-world marketing project lifecycle
What we learned
- Deep understanding of the pain points in marketing research and campaign planning
- The importance of mapping software features directly to user workflow phases
- Advanced API integration and state management in a full-stack TypeScript environment
- How automation and smart defaults can unlock significant productivity gains
What's next for AudienceIQ
- Podcast Persona: Personalized brand recommendations for podcasters based on their unique audience demographics and interests
- Trends Analytics: Real-time analysis of sponsorship and advertising trends to surface emerging opportunities
- Opportunity Analysis: Import and evaluate your own leads with AI-powered compatibility and success scoring
- Cross-Team Collaboration: Real-time project sharing, teamwork, and unified campaign management
- Brand Analysis: Deep-dive analytics on brand audience segments and engagement
- Competitor Analysis: Track competitor ad placements and partnerships for strategic market insights
- Enhanced Dashboards: Advanced analytics to track campaign performance from planning through wrap-up
Built With
- ai
- docker
- gemini
- llm
- node.js
- qloo
- react
- typescript
- vite
Log in or sign up for Devpost to join the conversation.