Personas

An AI-driven tool that redefines venture building by simulating real user interactions to swiftly uncover pain points and test solutions.

My Responsibilities

Conducted user research, developed and designed low fidelity prototypes for testing, designed final high fidelity deliverables for final development.

About Mach49

At Mach49, our incubate process is intricately designed to foster new ventures through three distinct phases of interviews. The process begins with open-ended discovery, where we explore and identify critical pain points. This is followed by value proposition testing, where potential solutions are validated to ensure they address the identified pains effectively and test for desirability. The final phase, solution testing, involves placing prototypes in front of users to test whether or not the solution meets the value proposition proposed. As Mach49ers, our role extends beyond facilitating these interviews; we mentor teams on managing a startup-like process, from conducting interviews and synthesizing data to making strategic decisions that drive towards product-market fit and preparing them to pitch their innovative ideas to their boards.

The Challenge

The primary challenge our team faced was efficiently recruiting appropriate participants for validating our incubation processes. Traditional interview methods were not only slow but often failed to deliver the depth of insights needed promptly, creating a significant delay in iterating and also decreased time for deep thinking amongst the team.

The Solution

To address this, we implemented AI-powered personas that simulate detailed user interactions. This innovative solution was designed to drastically reduce the time required to gather user insights by automating the interview process, thus allowing for quicker open-ended interviews, rapid testing of multiple value propositions and quicker iteration on potential solutions.

Design Process

Conceptualization and Initial Testing

The initial phase involved brainstorming and prototyping. After exploring the potential of synthetic interviews I helped develop initial prototypes to evaluate how our team members approach the creation of personas:

  • Detail Orientation: Investigating the granularity of information our teams prefer when creating personas.

  • Interaction Needs: Determining whether our teams preferred to interact with personas through dialogue or analytical reports.

  • Comparison Metrics: Aligning AI persona outputs with existing real data to ensure consistency and reliability.

Persona Creation Interface

I developed an intuitive, chat-based interface for persona creation, focusing on user-friendly design elements to facilitate easy adoption:

  • Interactive Creation Process: Users could dynamically input characteristics and receive immediate feedback, creating a seamless back-and-forth interaction akin to real conversations.

  • Information Utilization: Clear indicators on how user inputs were being transformed into persona traits helped in setting the right expectations and building trust in the tool.

  • Customization and Iteration: Features were included to edit and refine personas over multiple iterations, providing flexibility and control to the users.

  • Personas were grouped by specific user segments, allowing targeted exploration of distinct stakeholder groups. This enabled focused analysis on the most critical pain points and opportunities.

Persona Improvements

  • Pain Report: For each persona segment, we provide an AI generated pain point report to help identify major pain points and their severity.

  • Script Generation: We incorporated functionality to automatically generate interview scripts based on the most severe pain points identified, enhancing the customer development process.

  • Solution Generation: The platform provided solution ideas to address these pain points, facilitating creative problem-solving.

  • Synthetic Interview Feedback: In addition to being able to speak directly to personas, we also provided automated synthetic feedback to potential solution ideas to help supplement real life value proposition testing.

Design Handoff

I utilized our existing Material UI design system components and created new components specific to the Persona tool while keeping consistency across all previously created components.

Flows were outlined and annotated for easy handoff for the engineering team, with regular check-ins to ensure alignment and feasibility.

Implementation

Training and Deployment

Comprehensive training sessions were provided to ensure all internal teams could effectively utilize the new tools. Following internal adoption, the platform was rolled out to clients, leading to significant engagement and positive feedback:

User Base: We achieved a substantial number of active users shortly after deployment, indicating strong acceptance and utility.

Efficiency Gains: The need for traditional interviews reduced from five to six per day to one or two, supplemented by insights from the AI personas.

Outcomes

The AI persona tool has proven to be a robust addition to our research toolkit:

Time Savings: Significant reductions in time spent on user research without compromising on the depth of insights.

Insight Quality: Comparative analysis between AI-generated and traditional research methods showed high levels of agreement, validating the effectiveness of the AI personas.

Continuous Validation: Emphasizing that while AI personas provide powerful preliminary insights, they are designed to augment rather than replace traditional research methods.