Food Social Media User Research
Conducted UX research for DineOneOne, conducting interviews, surveys, and competitive analysis, aligning more closely with users' needs
Overview
Originally envisioned as a food delivery price-comparison platform, this project evolved into DineOneOne, a personalized foodie app that blends dining with social interaction. Through deep user research—including over 25 interviews and 400+ survey responses—we discovered that users cared more about community-based food discovery than pricing tools. This key insight led to a strategic pivot that redefined the product’s core value.

✅ Result
After the designer joined, key influences included establishing a consistent design system and implementing user-friendly error correction prompts. Following the launch, we continuously gathered user feedback through usability testing and SUS questionnaires, resulting in an SUS improvement from 47.27 to 83.25 in the final outcome.
Successfully launched on Google Play, DineOneOne now serves as a dynamic food community. It supports user sharing, friend-based recommendations, and social interaction, offering a more engaging experience than traditional delivery apps.
🎯 Objective
To identify core user needs and preferences around food delivery and community sharing, and build a platform that aligns with these needs using UX research, usability testing, and design thinking.
⚠️ Challenge
To shift away from a utility-driven price comparison model and reimagine the platform as a socially engaging food-sharing community — while ensuring usability, discoverability, and sustained user engagement.
💭 Picture this: in the vast landscape of food delivery platforms, visitors embarked on a quest, pondering questions such as "How do you navigate the multitude of choices on a food delivery platform?" and "Which platform steals your heart, and why?" The journey unfolded as users explored their food delivery needs, each decision a story waiting to be told.
Research & Discovery 🔍
To create an experience that truly resonates with users, the team conducted an in-depth exploration of their gastronomic habits and preferences. This involved:
Conducting over 25 in-depth interviews
Distributing more than 400 surveys
Interview
First, we began with 25+ in-depth interviews with Taiwanese users across different age groups and lifestyles. Early assumptions pointed toward users prioritizing discounts and delivery speeds—but qualitative conversations revealed something deeper: social motivation and emotional fulfillment.
Many users viewed food delivery as a social extension—sharing what they eat, seeing what others recommend, and using food as a conversational spark. This insight shifted the foundation of our product. Through affinity diagramming, we identified themes like:
"I want my friends’ opinions, not strangers."
"I keep a photo record of what I eat."
"Even bad meals are fun to talk about."
These insights led to the design of a friend-powered recommendation model, moving beyond public reviews and toward tight social circles and shared stories.

Survey
With 400+ survey responses, we mapped the quantitative scale of our qualitative findings. Using trait analysis and persona mapping, we segmented users into seven personas, focusing on two dominant types:
The Social Foodie (age 25–34, shares frequently, values novelty and friend opinions)
The Practical Planner (age 35–44, prefers efficiency, orders for family)
Key behavioral stats:
Users aged 25–34 share the most (avg. 2–3 times bi-weekly)
Motivation to share wasn’t only based on good food, but also "venting" bad experiences
Name preference: 40% preferred nicknames, 35% real names, 25% anonymous
We also tested the question: “How do users decide where to eat?” The top three influences were:
Friend recommendations
Visual appeal (photos)
Mood & convenience


Our research led to a bold shift in direction:
🔄 Pivot & Product Strategy
❌ Original Idea:
A utility-focused food delivery price comparison tool.
✅ New Vision:
A social dining app that builds food communities around friends and shared experiences.
Key Transformations:
From pricing to people-powered discovery
Emphasis on friend recommendations over public reviews
Introduced friend categorization to improve social organization
Focused on moments, memories, and meals—not just logistics
Information Architecture
