Interview Data
Overview
Synthesized findings from a longitudinal behavioral study on regional culinary exploration.
Participant Profile & Methodology
| Participants | 8 (5 female, 3 male) |
|---|---|
| Age Range | 22-50 |
| User Type | High-frequency delivery users seeking authentic regional cuisine |
| Method | 3-day diary study tracking meal cravings, followed by remote contextual inquiries |
Full Research Report
Interview Data
References & Sources
- Participant 1 notes — I struggle to find authentic regional food; most apps just push generic fast food.
- Participant 2 notes — To me, quality is defined by proper ingredient sourcing.
- Participant 3 notes — For me, ingredient transparency justifies a premium service.
- Participant 4 notes — The onboarding was too generic to reflect my culinary background.
- Participant 5 notes — Without smart pairings, I usually just order less.
Key Insights
Insight 1: Implement a tiered filtering system Region > Style > Ingredient to transition users from "browsing" to "deciding" faster.
Insight 2: Integrate a Transparency Data view highlighting ingredient sourcing and quality metrics.
Insight 3: Build a dynamic Flavor Profile engine that learns from user ratings to prioritize regional dishes that match their specific taste preferences.
Notes
Following the diary studies and contextual inquiries, 85% of participants confirmed that integrated ingredient transparency and regional personalization effectively solved their primary browsing barriers, validating the core architectural insights for Oisoshi.