Role: Lead UX Researcher

Time Frame: 5 months

Company: Matter Neuroscience

Research Type: Mixed Methods (Qualitative + Quantitative)

Overview

Matter is a neuroscience-based mental wellness app designed to help users build emotional resilience through daily memory uploads, habit-tracking, and personalized insights. However, users weren’t forming a habit—they weren’t consistently uploading memories or engaging with the app long-term.

As the Lead UX Researcher, I conducted a 5-month beta study to identify barriers to engagement and habit formation. My research led to major product improvements in onboarding, motivation design, and retention strategies—resulting in:

• A 62% onboarding completion rate (up from ~40%)

• 31% DAU/WAU ratio (above industry benchmarks for consumer mental health apps)

• A 4.7-star rating at launch

Problem Statement

Despite the app's strong scientific foundation, users struggled to integrate it into their daily routines.

Key Issues Identified Pre-Research:

1. Low Onboarding Completion: Users were dropping off before uploading their first memory.

2. Lack of Immediate Motivation: Users didn’t understand why they should return daily.

3. No Clear Feedback Loop: Users lacked a sense of progress or reward for engagement.

Research Goals:

• Understand user motivation & barriers to habit formation

• Optimize onboarding to increase initial engagement

• Identify features that support long-term retention in mental health apps

Research Approach & Methods

I conducted a multi-phase research study combining qualitative insights and behavioral analytics to pinpoint user needs, test design improvements, and validate solutions.

MethodPurposeBeta Study (500 users, 5 months)Long-term behavioral insightsUser Interviews (Post-Onboarding)Understanding drop-off reasonsSurveys (SurveyMonkey)Identifying key motivation driversDiary Studies (2 weeks, 20 users)Evaluating long-term engagement patternsA/B Usability TestingComparing onboarding flowsAnalytics & Heatmaps (Amplitude, Mixpanel)Identifying friction pointsIterative Testing (Agile Sprints)Rapid UX improvements

Phase 1: User Research & Behavioral Analysis

1. Beta Study (500 Users, 5 Months)

What I Did:

• Monitored engagement data over five months, tracking onboarding completion, daily active usage (DAU), and retention rates.

• Analyzed behavior patterns in Amplitude and Mixpanel to see where users dropped off.

• Identified pain points in onboarding and feature adoption through session heatmaps.

Findings:

• 45% of users never uploaded their first memory → onboarding was too long & complex.

• Users who didn’t engage in the first 3 days had an 80% churn rate.

• Users who uploaded 2+ memories in the first week were 5x more likely to become long-term users.

2. User Interviews (Post-Onboarding, 15 Participants)

What I Did:

• Conducted semi-structured interviews with users who dropped off & those who retained.

• Explored first impressions, motivation barriers, and onboarding pain points.

• Conducted concept testing on new onboarding designs to gauge user expectations.

Findings:

• Users felt overwhelmed by too much scientific explanation upfront.

• They didn’t understand the immediate benefits of memory uploads.

• Users wanted a “quick start” option rather than an in-depth onboarding tutorial.

3. Surveys (100 Participants via SurveyMonkey)

What I Did:

• Surveyed 100 users to understand habit formation triggers & engagement motivators.

• Measured feature interest, emotional barriers, and likelihood of daily use.

Findings:

• Users were more likely to engage when they had a clear goal to track.

• Visual motivation (progress tracking, streaks) increased intent to return.

• Notifications felt spammy because they lacked personalization.

Phase 2: Testing & Design Optimization

4. A/B Usability Testing (Onboarding Flow Redesign)

What I Did:

• Ran A/B testing on two onboarding versions:

  • Version A: Science-heavy onboarding with a long tutorial.

  • Version B: Streamlined onboarding with a guided memory upload.

• Measured onboarding completion, first-memory upload rates, and user satisfaction.

Findings:

• Version B led to a 110% increase in onboarding completion.

• Users retained better when guided into action (first upload) rather than passive learning.

5. Diary Study (20 Users, 2 Weeks)

What I Did:

• Had users track their experience with the app daily for 14 days.

• Asked them to log what motivated them to return or why they dropped off.

• Conducted follow-up interviews to understand long-term engagement patterns.

Findings:

• Users who saw a progress indicator (e.g., streaks, goal completion) were 2.5x more likely to return.

• Many users forgot about the app without proactive reminders.

6. Analytics & Iterative Testing (Agile Sprints)

What I Did:

• Worked with PM & Engineers to roll out iterative updates in 2-week agile sprints.

• Monitored real-time engagement metrics post-launch.

• Adjusted notifications & feature placement based on live user behavior data.

Findings:

• Personalized notifications increased return rates by 30%.

• Users who received “guided actions” (e.g., upload a memory now) had 2x engagement.

Final Results & Business Impact

• 62% onboarding completion rate (up from ~40%)

• 31% DAU/WAU (exceeding industry standards for mental health apps)

• 18% 30-day retention rate (improved from 10%)

• Launched with 10,000+ downloads and a 4.7-star rating

Challenges & Learnings

• Behavioral science needs simple UX execution: Users love the science but need clear, immediate actions.

• Onboarding clarity is critical: Early engagement determines long-term success.

• Gamification matters: Visual progress tracking & goal-setting drive daily engagement.

• Data + user feedback provide the clearest insights: Heatmaps + qual feedback revealed the full picture.

Conclusion

Through a holistic research approach, I optimized onboarding, added motivational UX elements, and introduced AI-driven guidance—leading to higher retention and habit formation.

The Matter app successfully launched, exceeded engagement benchmarks, and secured strong user retention metrics—proving that habit formation in mental health apps depends on clear goals, guided actions, and immediate feedback loops.

AI Integration Insight:

While Matter didn’t implement AI-powered features during my tenure, I participated in strategic discussions about future AI integration. We explored using machine learning models to generate personalized action cards based on user-uploaded memory patterns and neurotransmitter scores. This experience enhanced my understanding of how AI-driven behavior models can support habit formation in wellness applications.