Project Summary
Client: Drop Bio Health
Role: UX Designer & Researcher
Project Duration: 4 weeks (contract)
Focus: Customer discovery, persona validation, pricing insights
I was engaged during Drop Bio’s early product development phase to clarify the ideal customer profile, understand trust drivers in preventative health, and inform pricing strategy for a subscription-based service. Over 4 weeks, I conducted 45 interviews across diverse demographics, synthesized insights using Dovetail, and validated or refined four key personas. The work informed marketing strategy, pricing models, and laid the foundation for future journey mapping and feature ideation.
Team & Collaboration
- Worked alongside the Product Manager, Marketing Lead, and UX Designer.
- Responsible for interviewing 45 out of 90 users via Google Meet.
- Used Dovetail for transcription, tagging, and theme synthesis.
- Delivered insights through a written report and stakeholder presentation.
Approach & Constraints
- Timeframe: 4 weeks
- Method: Semi-structured interviews (2–3 per day)
- Analysis Time: 1 hour between each session for real-time synthesis
- Tools used: Dovetail for qualitative coding and pattern recognition
- Sensitivity of health data, trust-building in early-stage brand, limited time for iteration
Key Insights
Motivation Patterns by Gender
- Men often cited family health as their motivator—prioritizing vitality and long-term presence for their children over data metrics.
- Women also cited family but often included children in daily wellness routines (e.g., shared workouts).
Health App Spending Behavior
- Pricing sensitivity revealed that users viewed monthly subscriptions as trials, while annual or one-off payments signalled trust and long-term commitment, directly influencing pricing recommendations.
- Many use apps daily for 5–10 years once habits are formed.
- Trust and habit loops are essential to long-term engagement.
Persona Discovery
The research validated two high-value behavioural segments and surfaced two additional groups with distinct motivations and spending capacity.
Segmentation proved more predictive when based on relationship to health data, family responsibility, and habit formation rather than age, directly informing marketing focus and onboarding strategy.
Impact & Recommendations
- Aligned product and marketing teams around realistic customer expectations and pricing psychology
- Identified two priority segments with both strong engagement patterns and disposable income
- Informed early marketing messaging and pricing direction
Learnings
This project sharpened my approach to live qualitative research and reinforced the importance of fast, decision-oriented synthesis in early-stage health products, where teams need clarity rather than exhaustive documentation.