en2uition
Data-Driven Romance: Navigating Love's Complexities
Project Overview
en2uition Psychology Labs LLC is a research company applying AI to the study of human relationships. Using algorithms based on Robert Sternberg's Triangular Theory of Love (intimacy, passion, and commitment), en2uition analyzes relationship behavior patterns to transform psychology into a data-driven science.
Problem Space
Many people struggle with romantic relationships, facing barriers like cost, stigma, and lack of access to professional psychological consultations.
Goal
Design an app offering AI-driven insights and human counseling to foster healthier relationships.
My Responsibilities
As Lead Product Designer, I led the design lifecycle, including research, validation, documentation, prototyping, and a design system, collaborating with a cross-functional team.
Pain Points
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When I started, the product was in its initial phase, with only an idea on the table. en2tuition's psychologists created a romantic relationships questionnaire, and I was tasked with designing a chatbot. However, the team questioned whether a chatbot was the best approach.
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The 360-question survey required responses for participants and their partners, encouraging analysis of multiple relationships. However, surveys over 15 questions often see low completion rates.
Years
2021 - 2024
Role
Lead Product Designer
Industry
Mental Health & Wellness
Location
Washington D.C. Metro Area
services
Product Discovery
Design Strategy
Stakeholder Alignment
UX Research & Testing
Interactive Prototyping
Design System
Branding Design
Presentation
Process Deep Dive
Interview Quotes and Research Findings
To shape the product's architecture and functionality, I interviewed stakeholders to capture their vision, expectations, and key metrics (e.g., user retention and satisfaction). I also conducted 16 user interviews with ten pre-defined questions to understand user needs and optimize questionnaire completion strategies.
Solutions
After a series of brainstorming sessions, we ditched the chatbot for a traditional card-based questionnaire design, letting users preview questions, work at their own pace, and edit answers.
We split the questionnaire into 6 levels, giving users feedback after every 15 questions and detailed feedback at the end.
We created a dashboard where users can share relationship issues, hire coaches (volunteers or professionals), and become coaches themselves after completing two questionnaire levels. Coach rates depend on completed levels and platform reputation.
We added an option for verified professional psychologists to offer their services without completing the questionnaire.
Customer Journey Map
To provide broader context on user interactions, identify critical features, and guide navigation flow to meet user expectations, I created and shared a Customer Journey Map with the team. At this point we had direction on what we needed to improve and what the customers needed.
Mapping the Information Architecture
Once completed, I mapped the Information Architecture, outlining the entire user journey, including all scenarios, edge cases, and error states. Deliverables included a sitemap, user flow diagrams, wireframes, and documentation for navigation paths, edge cases, and error-handling strategies.
Define Scope & Features for MVP
In the stakeholders meetings we reviewed the entire information architecture for the MVP and decided on must-have, should-have, could-have, and won’t-have, focusing on user engagement (the questionnaire and AI feedback) and user retention features (community board and coaching).
From Vision to Execution
This process guided us in creating the initial batch of high-fidelity prototypes. Experimenting with a multitude of different styles, I created wizard-type flows, and much more. Finally, after a couple weeks of iterations, we landed on a style that we were comfortable with. We thought that this format could grow with en2uition as we add more features and functionality.
Design System
Simultaneously, I developed a design system to define and standardize the visual elements that encapsulate the essence of our brand, forming a cohesive visual language. Implementing the design system allowed the team to streamline the process of fixing, maintaining, and evolving the product efficiently.
Finally, I worked closely with four engineers during the implementation phase to hand off our designs for development, ensure their integration for alpha and beta testing, and establish a foundation for future feature expansions.
We conducted an initial round of quantitative usability testing, refined the designs based on the feedback, and proceeded with a second round of usability testing, this time focusing on qualitative insights.
Full Feature Rollout
As the product became more popular and investors emerge the rest of the planned features were deployed:
Full-fledged mobile application
Advanced filtering messaging and matchmaking for coaches and users.
In-Service Payment System.
Complete six-level questionnaire with tailored full feedback.
Outcomes
This constant use of design thinking and thorough testing helped us to reach market while reducing costs by using consistent approach and iterations as well as reducing strain due to badly targeted products.
Seventy-five percent of users stated that the proposed solution effectively addressed their needs. Both qualitative and quantitative data indicate that the solution will help users build healthier relationships, reduce stress and anxiety before consulting a psychologist, and make psychology education more accessible to the general public.
This growth has accelerated further in 2024, with en2uition becoming part of the state program Improving Mental Health Access for Low-Income Citizens.
