Product-Case-Studies

Product case studies showcase real-world examples of how a product solved specific customer challenges, highlighting the problem, solution, and measurable impact. They provide actionable insights into product performance, user experience, and business outcomes.

This repository is a collection of learnings from my NextLeap Product Management classes. It covers a wide range of topics including product thinking, user research, problem framing, and competitive analysis. I have also explored execution-focused skills such as roadmapping, prioritization, and metrics tracking, along with practical applications through case studies, onboarding flow design, referral strategies, and go-to-market planning. Each section reflects both theoretical concepts and hands-on exercises, making this a consolidated space for my product management journey.

Product Case Studies & Analysis

AI Dating App – Post-Match Planning Assistant

• Identified post-match drop-offs as a key conversion and retention problem in dating platforms
• Defined an AI-driven post-match planning solution to reduce friction and increase real-world meetups
• Conducted user segmentation, competitive analysis, and market sizing (TAM–SAM–SOM)
• Mapped product features to metrics such as match-to-meet conversion and user follow-through

GameOn – Referral Engine Revamp

• Diagnosed low referral adoption as a product design and incentive alignment issue
• Redesigned the referral system using gamification, tiered rewards, and social motivation loops
• Applied RICE prioritization to balance impact, effort, and scalability
• Linked feature outcomes to referral conversion rate, participation, and long-term LTV

Zepto – Driving Scheduled Delivery Adoption

• Framed scheduled delivery as a strategic lever for improving profitability and delivery efficiency
• Analyzed user behavior, operational constraints, and quick-commerce market dynamics
• Proposed product changes to increase adoption without weakening the speed-first brand promise
• Estimated impact on basket size, delivery costs, and customer retention

BeReal – Onboarding and First-Time User Experience

• Evaluated onboarding as a behavior-shaping mechanism rather than a setup flow
• Identified friction points preventing users from completing their first post
• Redesigned flows aligned with BeReal’s authenticity-focused product philosophy
• Connected UX improvements to activation rate, Day-1 retention, and posting compliance

Data Analysis Using SQL

• Structured SQL analysis around real product and business decision questions
• Analyzed performance, value contribution, and underperforming segments
• Demonstrated strong command of joins, aggregations, and filtering techniques
• Translated analytical outputs into decision-ready product insights

Swiggy – Reducing Customer Support Load

• Treated high support volume as a product and UX design problem
• Identified root causes of frequent customer issues across core user flows
• Proposed self-serve and clarity-driven features to reduce support dependency
• Linked solutions to CSAT improvement, cost reduction, and operational efficiency

MoodMate – Mental Wellness Product

• Identified a product gap between clinical therapy tools and generic wellness apps
• Designed a preventive, low-friction mental wellness experience
• Prioritized features using user empathy, ethics, and RICE scoring
• Focused on trust, privacy, and sustained engagement as core outcomes

Enterprise AI Market Analysis

• Analyzed the enterprise AI ecosystem through segmentation and value creation lenses
• Identified high-impact AI use cases across functions and industries
• Assessed competitive positioning and monetization potential
• Synthesized insights to inform product strategy and roadmap decisions

Zepto – User Research & Scheduled Delivery Adoption

• Conducted deep user research to identify low trust, rigidity, and habit-driven preference for instant delivery over scheduled delivery.
• Segmented users by urgency, lifestyle, and frequency, highlighting working professionals (25–34) as the highest-impact target group.
• Performed market sizing (TAM) to quantify a ~₹30B annual GMV opportunity from high-frequency Tier-1 users.
• Synthesized survey insights showing predominantly spontaneous purchase behavior and preference for early next-day slots.
• Proposed ETA transparency for scheduled deliveries, evaluated using RICE, to improve trust and adoption with moderate implementation effort.

Spotify – UX / UI Experience Analysis

• Analyzed Spotify’s UX through behavioral psychology and usability laws to understand engagement, retention, and conversion drivers.
• Identified key user needs around personalization, discovery, and uninterrupted listening across free and premium segments.
• Evaluated core user flows to surface strengths in navigation, recommendation, and familiarity-driven design.
• Diagnosed UX gaps such as cognitive overload, weak error-state CTAs, and limited podcast continuity.
• Recommended targeted UX improvements to enhance activation, premium conversion, and long-term engagement.

NLLearnVista – AI-Guided Upskilling Platform for Young Professionals

• Identified decision fatigue, low follow-through, and unclear career alignment as core barriers in upskilling for young professionals.
• Conducted market sizing (TAM–SAM–SOM) to quantify a $1B+ opportunity in guidance-focused learning solutions.
• Analyzed competitors (Coursera, Udemy, YouTube, bootcamps) to uncover gaps in personalized decision-making and actionable first steps.
• Designed an AI-driven MVP offering personalized skill recommendations, curated learning roadmaps, and progress nudges using RICE and MoSCoW prioritization.
• Defined success metrics and monetization strategy linking skill completion, retention, and subscription revenue to long-term platform growth.