Destination marketing teams spend limited public money on origin-market campaigns, but most visitor-intelligence tools only show who already came. TasteBound was inspired by the blind spot between cultural taste fit and actual arrivals: a destination may have strong latent affinity in origin markets that are not visiting yet, while the DMO’s budget keeps going to already-visited markets.
Shifting public funding pressure — median DMO budget around $1.77M with 92% public funding, and Brand USA federal matching funds falling from $100M to $20M — made this decision problem more urgent.
TasteBound turns that urgency into a Qloo-grounded, deterministic campaign-planning workflow. It builds destination taste profiles, scores origin markets by taste fit, compares taste against real arrivals, and produces audiences, campaign angles, media partners, budget splits and exportable briefs. Every number is tied to Qloo and validated, so the tool helps DMOs act on latent cultural demand without pretending to forecast visitors.
The core idea: taste fit can identify origin markets that are culturally aligned but under-visited, while arrivals data explains the gap; together they support a targeted campaign plan for limited public money.
Built With
- agentic
- ai
- arrivals
- backend
- campaign-planning
- data-science
- demo
- fastapi
- frontend
- gemma
- hackathon
- llm
- machine-learning
- nextjs
- ollama
- postgresql
- python
- qloo
- react
- sqlite
- supabase
- tailwindcss
- taste-fit
- typescript
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