Mocha

An application designed for consulting job seekers to practice, refine, and track their case preparation skills, helping them secure a consulting job.

Project Type: Web

Role: Product Strategy & Design

Partnered with Jonathan Kaufman

Background

Many consulting applicants face challenges in preparing for case interviews, especially in finding reliable practice partners and mastering frameworks. Without structured support or accessible resources, candidates can struggle to efficiently prepare within limited timeframes, leading to anxiety and gaps in readiness.

Product Overview

Mocha is designed to enhance and streamline the interview preparation journey by offering:

  • AI-Powered Mock Interviews: Experience realistic interview simulations with voice input and two modes, "Learning" and "Interview," to adapt to your preparation needs.

  • Personalized Readiness Tracking: Receive an auto-generated Readiness Score to monitor your progress, track improvement over time, and get tailored recommendations for your next steps.

  • Comprehensive Case Library and Drills: Access a well-organized library of cases sorted by industry and type. Practice targeted drills focused on specific skills, ensuring well-rounded preparation for case interviews.

Readiness Dashboard

  • Offers a comprehensive Readiness Score that reflects overall performance and proficiency in key areas like Frameworks, Charts, Math, and Communication.

  • Provides personalized feedback on strengths and weaknesses, enabling users to focus on targeted improvements.

  • Includes tailored practice drills for specific skill areas, such as structuring frameworks, interpreting data, and refining communication techniques.

Case Library

  • An extensive repository of consulting cases across multiple types (Profitability, Market Entry, Growth, etc.) and industries (Technology, Healthcare, Retail, and more).

  • Enables users to choose cases that align with their specific goals, skill levels, and areas of interest.

Interactive, Audio-Powered AI

  • Mocha provides an AI-driven virtual partner that users can interact with through real-time audio input, simulating natural consulting interview dynamics. The AI adapts responses based on user input to mimic a real interviewer’s flow.

Customized Case Selection

  • Users can tailor their practice by selecting:

    • Case Type: Profitability, Market Entry, Growth, M&A, and more.

    • Industry: Technology, Healthcare, Retail, and others.

    • Difficulty Level: Adjustable from Easy to Hard, ensuring challenges appropriate to the user’s skill level.

Ideations

Why Targeting Consulting Applicants?

1. Real-World Experience with Consulting Recruitment

As former consulting applicants ourselves, we’ve navigated the intense, demanding process of preparing for case interviews. Our personal experiences, coupled with recent conversations with students currently recruiting, highlighted that the pain points and challenges candidates face today are strikingly similar to those we encountered just last year, such as:

  • Limited Access to Case Partners: Consulting candidates often struggle to find reliable peers or mentors to practice cases with.

  • Structured Practice Gaps: While there are resources like casebooks, these don't provide personalized feedback or real-time interaction, which are crucial for improvement.

  • Feedback and Scoring: Many candidates expressed the need for immediate, objective feedback on their case-solving skills, which can be difficult to obtain through traditional methods.

2. High ROI for MBA Students in Consulting Paths

The consulting track attracts MBA students because of its high career ROI and lucrative starting salaries. This makes consulting candidates highly committed to their interview preparation, as even a small competitive advantage could result in landing a coveted role. By catering to this target audience, we ensure a product-market fit with users who are both motivated and willing to invest in high-quality preparation tools.

3. Expansion Potential

While consulting applicants are our primary focus, the platform’s functionality can be expanded to serve adjacent industries with similar interview styles, such as investment banking and tech. However, consulting remains the ideal starting point due to the unique challenges and high-stakes nature of the case interview process.

User Research

User Interviews:

We conducted 9 interviews on MBA students’ consulting interview experiences. Here are some key findings from our conversation:

“The hardest part of prepping for case interviews is finding good partners to case with. It’s exhausting, and it takes time away from actually doing the prep work needed in the short amount of time we have”

-Teddy

“The framework is the toughest part of the case interview, and I’m never sure how to tailor the framework to the case at hand. Practicing the whole case is great, but I would love dedicated drills to practicing just the framework.”

-Emily

“This idea of AI preping would be best for entry-level casing, focused on supporting users in the first leg of their journey. Real interviews are very specific, so it’s important to make sure the AI isn’t just spitting out highly general cases.”

-Raphael

User Surveys:

We conducted a survey (N = 39) to further understand student preferences and needs.

From user interview and survey, we learned:

1. Top Pain Points in Case Interview Preparation

  • Difficulty Scheduling Live Cases / Finding Partners (51.3%): The majority of respondents struggle to find consistent practice partners or schedule live cases, which suggests a strong need for tools or features that make connecting with partners easier or provide structured, solo practice options.

  • Insufficient Feedback / Unclear How to Improve (33.3%): A significant portion of respondents feels they lack actionable feedback, indicating a demand for tools that offer clear, personalized feedback to guide their improvement.

  • Overwhelm with Resources (5.1%) and Developing Own Relationships (2.6%) are less prominent pain points, suggesting these areas might be secondary focus areas for product development.

Key Insight: There’s a clear need for easier access to partners and structured feedback, which can be addressed by integrating AI-based solutions for solo practice and actionable feedback loops.

2. Most Desired Features

  • Personalized Feedback (43.6%): Almost half of the respondents want feedback tailored to their performance, reinforcing the need for an AI-driven tool that can analyze and provide customized insights.

  • Instant Clarification on Frameworks (20.5%) and Customized Quantitative Exercises (20.5%): These results show that many users need quick help with frameworks and quantitative exercises, highlighting the importance of including features like framework guides, real-time AI coaching, and focused math practice modules.

  • Over-Time Performance Tracking (15.4%): Tracking performance over time is a less prominent, but still notable, feature. This could add value for users who want to see their progress and pinpoint areas for continuous improvement.

Key Insight: Prioritize developing features that provide immediate, personalized feedback and guidance on frameworks, as these are high-demand areas that align well with the core value of an AI-driven prep tool.

3. Trust in AI Tools

  • 87.2% Trust AI for Case Prep: A high percentage of users are open to using AI tools like ChatGPT for their case prep, indicating a strong market receptivity to AI-driven solutions. This high trust factor provides a solid foundation to introduce AI-powered mock interviews, feedback mechanisms, and personalized coaching.

  • 12.5% Do Not Trust AI: A small segment remains skeptical of AI for case prep, suggesting a need to build credibility through testimonials, demo videos, and trial access to demonstrate AI’s effectiveness in this context.

Key Insight: The overwhelming trust in AI indicates that users are comfortable with and open to AI-driven case prep solutions, making it easier to position the product as a trusted and innovative tool.

4. Usage Frequency

  • 61.5% Would Use a Few Times a Week: The majority would use the tool multiple times weekly, showing that there’s demand for a tool that supports consistent practice rather than daily use. This frequency suggests features that focus on skill-building over time, rather than intensive daily drills.

  • Daily Usage (20.5%) and Once a Week (7.7%): Some users might use it daily, while others might need it less frequently. The product should be flexible, allowing users to dive in at their own pace, whether that’s daily practice or less regular sessions.

Key Insight: Design the product to support a flexible practice schedule, allowing users to engage on a semi-regular basis (a few times a week) while also catering to those who might use it more or less often.

Persona

Based on extensive research, interviews, and surveys, we created a typical user persona to represent the consulting candidates using our platform.

Understanding the Consulting Prep User Journey

We mapped out the typical journey that students go through as they prepare for case interviews. This journey highlights the main stages, actions, and challenges candidates face, from initial exploration to reflecting on feedback after practice sessions.

Our platform is designed to address these pain points by offering AI-driven feedback, easy access to practice resources, and personalized insights to guide candidates at each step. This way, you don’t just prepare for your interview—you prepare confidently, with the right tools and support.

Defining Product Vision & Key Features

Prototyping

Wireframing

High-Fidelity Prototype

Mock Up Flow:

User Dashboard & Library

Project Reflection

What’s Next?
The next step is to collaborate with engineers and launch the MVP to deliver immediate value and test the market:

  • Collaborate with Engineers: Work closely with the development team to finalize and integrate core features, including AI-powered audio interaction, customizable case selection, and readiness tracking.

  • Launch MVP: Deploy the Minimum Viable Product, ensuring that essential functionalities meet user needs and offer a seamless experience.

  • Collect Feedback Post-Launch: Actively gather user feedback to identify gaps, improve features, and refine the product for future iterations.

What I Could Have Done Better?
Reflecting on the process, there are areas where improvements could have been made:

  • Accelerated Development: Developing and delivering faster would have allowed us to test the market sooner and adapt based on real-world feedback.

  • More Intensive User Research: Conducting deeper, more targeted user research early in the process could have provided additional insights to better inform product decisions.

Project Takeaways:
Through this project, I gained valuable insights into product development and user engagement:

  • Agile Development: Speed is critical when testing a new concept in the market. Quick iterations allow for faster learning and adaptation.

  • User-Centric Design: Investing in understanding user needs ensures the product aligns with expectations and provides value.

  • Cross-Team Collaboration: Effective communication with engineers and designers is key to delivering a well-rounded product.

  • Iterative Refinement: Continuous learning and refinement, both pre- and post-launch, are essential for long-term success.