ui/ux
Overview
Project process
Project process
To design an experience that resonates with users, we first need to understand who they are and how they interact with voice assistants. Our research identified our primary audience as older Gen Z (ages 18-24), who lead a trendy lifestyle and use a mix of operating systems.
-Older Gen Z – Early Adulthood: Young adults who are familiar with technology but seek convenience and efficiency.
-Trendy & Digital-First Lifestyle: They value personalization and seamless interactions in their digital experiences.
-Mixed Operating System Users: They use both Android and iOS, requiring cross-platform compatibility.
To further refine our understanding, we conducted a survey with 74 participants who use voice assistants regularly. The findings provided key insights into their behavior, preferences, and pain points:
Key Findings:
More than 50% of participants believe Google Assistant is easy to use, but they want more engaging voice interactions.
67% of users use their voice assistant after 12 PM, indicating a preference for productivity in the afternoon.
Users want Google Assistant to improve their daily efficiency, especially in scheduling and task management.
82% of users have been using voice assistants for over a year, yet many still underutilize its advanced features.
Our research utilized the AEIOU framework to analyze the existing experience of Google Assistant across different devices. This helped us understand how users interact with the product in real-life scenarios and identify opportunities for improvement.
By analyzing the current product landscape through this lens, we identified key usability gaps and areas where the customer journey could be optimized for a more seamless and engaging experience.
To better understand Google Assistant’s position in the market, we analyzed competing products and identified key areas where users face challenges. Our secondary research focused on how Google Assistant compares to other voice assistants in terms of personalization, localization, usability, and user engagement.
Our competitive analysis highlights several areas where Google Assistant can improve:
✔ Enhancing personalization to make interactions feel more tailored
✔ Improving localization to better support bilingual and accented users
✔ Reducing the learning curve by making feature discovery easier
✔ Creating engaging onboarding experiences to increase user retention
✔ Encouraging deeper feature exploration beyond simple tasks
These insights shaped our design decisions, focusing on improving user engagement, usability, and overall experience.
To better understand the user experience with Google Assistant, we analyzed how users feel across three key touchpoints: Introduction, Onboarding, and Habits. Using ethnographic research, we mapped their emotional highs and lows throughout their journey.
While users generally have a positive perception of Google Assistant, their engagement fluctuates across different phases. Trust and usability are strong in the beginning, but personalization and habit reinforcement are key areas that need improvement. These insights guided our design decisions in refining the customer journey and experience flow.
To address the challenges identified in our research, we designed a structured Customer Journey Map that helps guide users through:
Initial discovery and onboarding
Daily engagement and task completion
Long-term habit formation
-Users need a more guided onboarding experience to understand the Assistant’s full capabilities.
-Engagement drops due to limited user motivation—gamification could help.
-Personalization must be more intuitive and proactive to encourage habit formation.
Concept Exploration: 10 Potential Ideas
After analyzing the customer journey, we generated 10 potential concepts aimed at improving engagement, onboarding, and personalization. These ideas ranged from gamification strategies to AI-driven habit formation techniques.
Emotional
Intelligence
Personalized
Daily Interactions
Personalization
Gamification
of Learning Functions
Intuitive
Scheduling
Optimization
of Usage History
Gamification
Recommendations
Based on Schedules
Product
Promotion
Information
Architecture
of Routine Maker
Evaluating and refining Gamification & Personalization based on user feedback
After selecting Gamification & Personalization, we conducted usability testing with 13 participants to validate these concepts. The goal was to evaluate how these features impact engagement, ease of use, and long-term retention.
Gamification
of Learning Functions
Gamification
of Learning Functions
-Gamification: Encouraging user engagement through progress tracking, rewards, and interactive challenges.
-Personalization: Creating a tailored Assistant experience by adapting responses based on user behavior & preferences.
Users struggled with onboarding due to a lack of engagement, making the learning curve steep and discouraging early adoption.
We designed a personalized onboarding experience that allows users to choose custom names and hotwords, making the interaction with Google Assistant more welcoming and user-friendly.
quick Grasp：Interactive tutorials that simplify learning.
We created an interactive onboarding experience that guides users step-by-step through key features using quick tasks and personalized suggestions, making it easier to explore Google Assistant's capabilities.
Learning by Doing: Hands-on tasks that encourage users to explore functionality naturally.
We designed scenario-based guides that use real-life situations to help users explore Google Assistant's features naturally, making the learning process more relatable and engaging.
Users found scheduling rigid and not tailored to their needs, leading to poor adoption.
Customize Scheduling: Users can organize schedules with more flexibility.
We designed a personalized scheduling system that allows users to add, edit, and manage deadlines effortlessly through natural language input, making task management more intuitive and efficient.
Receive Smart Suggestions: Assistant provides proactive recommendations based on user habits.
We designed a smart suggestion system that proactively recommends actions based on user habits, such as muting notifications during focus times or suggesting relevant places and tasks, making the experience more seamless and personalized.