Quest AI
Revolutionizing Market Research with AI-Powered Insights
Role
Product Designer
Industry
Marketing
Team
1 Product Designer, 4 Developers & 2 Product Managers
Duration
3 Months
Project Scope ๐
Quest AI is a platform designed to streamline market research by automating complex data analysis. Market researchers often grapple with overwhelming amounts of qualitative and quantitative data from diverse sources, such as multimedia focus groups and open-ended survey responses. This manual process is both time-consuming and error-prone. The goal was to create a tool that empowers researchers to efficiently manage diverse data formats and extract actionable insights in real-time.
Problem โ ๏ธ
Market researchers regularly struggle with:
Data Overload: Juggling vast quantities of multimedia and survey data.
Limited Tool Capabilities: Traditional tools donโt accommodate diverse formats like video, audio, and open-ended responses.
Slow, Error-Prone Processes: Manual data analysis is slow, leading to delayed insights.
Goal ๐งญ
Speed, Simplicity, and Scalability.
The primary goal of Quest AI was to create a tool that would:
Automate data processing for speed and efficiency.
Support diverse data formats like video, audio, and text.
Provide real-time insights to enhance decision-making processes.
My Role ๐
Owned UX strategy, user research, wireframing, prototyping, and usability testing for QUEST AI
Collaborated with cross-functional teams to create intuitive workflows and impactful solutions.By collaborating with product managers and developers, I ensured the design was intuitive and optimized for speed and usability.
Achieved a 22% improvement in user completion rates and a 25% reduction in time on task through optimized design.
How might we help market researchers process complex data formats and deliver real-time insights effectively?
โ
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Design Methodology
A User-Centered Approach
Target Users ๐ฏ
Quest AI is primarily targeted at market researchers, but it is also beneficial for other industries, including UX research, journalism, and any field reliant on big data.
The Challenge ๐ง๐ปโโ๏ธ
Handling Complex Data and Limited Tools
Market researchers are constantly overwhelmed with:
Vast amounts of multimedia data (e.g., video/audio from focus groups).
Limited tools that can analyze diverse data formats, such as video & survey questions.
Slow and error-prone manual data analysis processes that delay decision-making.
The Solution: Quest AI
Focus Group Platform
Focus Group analyzes qualitative and quantitative data from focus group sessions, workshops, or team discussions in formats like text, audio, and video, using AI to identify themes, patterns, and sentiments from participant input.
Open End Platform
Open End in QuestAI processes open-ended survey responses, interpreting text feedback to uncover meaningful insights and support decision-making.
Understanding The User ๐ญ ๐ฌ
I conducted 12 Semi-Structured interviews with market researchers, UX researchers, and journalists.
Key insights:
Need for speed in data analysis and real-time insights, using AI solutions to accelerate decision-making.
Support for multiple formats like video, audio, and text for efficient data handling.
Ease of use, with a user-friendly platform that doesnโt require technical expertise.
AI-driven speed, especially with chatbots for faster data processing and real-time insights.
Data visualization tools like word maps for clearer data interpretation.
Diverse insights, including sentiment analysis, main topics, and actionable comments.
Persona ๐จ
With these insights, I developed User Personas to ensure the design focused on real user challenges and goals. These personas shaped every design decision moving forward.
Competitive Analysis ๐ & Feature Prioritization ๐ข
We conducted a thorough competitive analysis, evaluating tools like:
Otter.ai,
Fireflies.ai,
Read AI,
Chorus.ai
And Gong.io.
The analysis revealed gaps in integrating diverse data formats, assessing companies on transcription, collaboration, and real-time summaries.
Prioritized Main Features ๐งฉ
Theme Generation
Transcription
Data Visualization
AI powered Insight
Advanced data analysis
Collaboration tools
Ideation, Sketching & Wireframing ๐ญ โแฐ
Using research insights and competitive analysis, I facilitated ideation workshops with the entire team to collaboratively define key features. Starting with sketches, I transitioned to low-fidelity wireframes in Figma, mapping workflows for data uploading, AI insights, and tool navigation. These wireframes enabled testing and rapid iterations based on feedback.
Designing for Clarity ๐
Once the wireframes were validated, I moved on to high-fidelity prototypes in Figma. The design prioritized clarity and usability.
From
Sketching
To
Low-Fi
Prototype
From
First HI-FI
Prototype
To Final
Design After
Testing
Constant Collaboration ๐ ๐ ๐
To ensure seamless implementation, I worked closely with the Software engineers and Project Managers throughout the design process.
Using Zeplin, I provided detailed specifications and assets for an efficient handoff. Regular meetings allowed us to address technical challenges. This collaborative approach ensured that the designs were not only user-friendly but also feasible from a technical standpoint.
Testing and Refining ๐งช ๐
Problem: The audio bar was hard to access.
Solution: Repositioned it to the bottom, reducing task time by 15%.Problem: Confusing terminology caused errors.
Solution: Replaced unclear terms, reducing errors by 10% and improving satisfaction by 18%.Problem: Breadcrumb-only navigation slowed workflows.
Solution: Added a side bar, cutting navigation time by 25%.Problem: Overloaded workflows with unnecessary features.
Solution: Removed redundant features like chat history, improving efficiency by 20%.
The Final Product ๐
Figma Prototype for Focus Group Tool:
The Impact ๐
The impact of Quest AI was clear:
35% faster data analysis, freeing researchers to focus on strategic insights.
40% improvement in usability after multiple design iterations.
Adoption by major clients like Ford and Zyrtec, who praised the platformโs efficiency and ease of use.
Lessons Learned ๐
UI Element Repositioning: Moving key UI elements like the audio bar resulted in significant task efficiency improvements.
Simplifying Workflows: Removing redundant features reduced cognitive load and increased completion rates.
Clear Terminology: Replacing unclear terms improved usability and reduced errors.
Collaborative Design: Iterative testing and ongoing collaboration were crucial for refining the product.
โจ ๐ญ Final Thought ๐ญ โจ
Quest AI wasnโt just another toolโit was a game-changer. Watching clients like Ford and Zyrtec use the platform to seamlessly analyze data and uncover actionable insights demonstrated the power of thoughtful, user-centered design.
Quest AI simplified data analysis for market researchers, enabling strategic decision-making and showcasing the power of design to solve real-world problems.