ARCHIT RAJ

Problem Statement

Spotify users struggle to discover new music beyond algorithm recommendations, leading to listening fatigue and reduced engagement.

Analysis Approach

• Conducted RICE prioritization framework analysis
• Performed MoSCoW and Kano model classification
• Identified $120M market opportunity through bottom-up sizing
• Defined "Weekly Unique Genre Exploration Rate" as key metric

Key Features Proposed

• 🎵 Genre Deep Dive: Weekly curated niche sub-genre collections
• 🎯 Taste Expander: AI recommendations pushing beyond comfort zones
• 👥 Social Music Explorer: See what friends are discovering

Prioritization Results

MVP Definition

• Weekly curated "Genre Deep Dive" playlists
• Basic recommendation algorithm with 20% "stretch" content
• Limited beta for 5% of users
• 6-week development timeline

Problem Statement

Online shoppers struggle to find niche products matching their personal style due to generic filters and irrelevant search results.

Solution Overview

Developed an AI-powered visual discovery engine using computer vision to analyze user style preferences and recommend matching products with compatibility scores.

Key Features

• 🎨 Visual style recognition from uploaded images
• 🔍 Smart filters based on style analysis results
• 🎯 Compatibility scoring for products (98% match)
• 📊 Personalized recommendations across sessions

Implementation Strategy

• Computer vision for color palette extraction
• Pattern recognition for textures and shapes
• Style classification using CNN models
• Similarity scoring algorithms
• TensorFlow/PyTorch technology stack

Impact & Results

Problem Statement

Zomato users face decision fatigue when choosing restaurants due to overwhelming options and inconsistent quality information.

Opportunity Sizing

• 70M active Zomato users
• 50% experience decision fatigue
• $907M annual revenue opportunity
• 20% potential increase in order frequency

Prioritized Features

• 🚀 Quick Decision Mode (RICE: 33.1)
• 🎯 Taste Profile Creator (RICE: 19.6)
• 📱 Personalized Discovery Feed (RICE: 18.7)

Kano Framework Classification

MVP Definition

• Simplified onboarding with taste preference capture
• Prominent "Quick Decision" button for instant recommendations
• Three personalized restaurant suggestions based on context

Key Metric

Decision-to-Order Conversion Rate: Target 40% (vs. current 15%)

Problem Statement

Instagram users experience content fatigue and reduced authentic connection due to algorithmic content and promotional material.

Market Sizing

• TAM: $10B (2B users)
• SAM: $4B (800M users)
• SOM: $800M (160M target users)

Prioritized Features

• 👥 Close Circle Feed (RICE: 864)
• ⚙️ Content Filter Controls (RICE: 266.7)
• 📊 Connection Insights (RICE: 129.2)

MVP Implementation

• Designate up to 30 accounts as "Close Circle"
• Dedicated tab for chronological Close Circle content
• Basic filters to adjust friend vs. sponsored content ratio
• Notification prioritization for Close Circle activity

Key Metric

Meaningful Interaction Rate (MIR) = (Comments + Saves + Direct Shares) ÷ Content Views
Target: Increase from 2.3% to 4%

Problem Statement

Businesses struggle to create engaging reels efficiently due to fragmented tools and complex editing workflows.

User Flow

• Start → Onboarding → Profile Setup
• Business Verification → Create Reel
• Enter Product Details → Generate Script/Audio
• Edit Script → Media Upload → AI Auto-Editing
• Preview → Schedule/Publish → Performance Dashboard

Key Features

• 🤖 AI-powered script generation
• 🎬 Automated editing with smart templates
• 📆 Scheduling across platforms
• 📊 Performance analytics dashboard

Technical Implementation

• Natural Language Processing for script generation
• Computer vision for automatic scene composition
• Cloud-based rendering for fast video processing
• API integrations with social platforms

Impact

Failure Analysis

• ⏱️ Poor timing (5 years late to market)
• 🚫 Lack of meaningful differentiation
• 🔒 Limited ecosystem compatibility
• 📢 Ineffective marketing and branding
• 🌍 Geographical limitations

User Insights

"I already had music in iTunes and none of my friends had Zunes, so the sharing feature seemed pointless." - Alex Rivera, IT Professional

JTBD Statements

• Portable access to entire music collection
• Easy sharing of new music discoveries

Turnaround Strategy

• 🔀 Pivot to cross-platform music ecosystem
• 🎧 Focus on high-fidelity audio positioning
• 👥 Enhanced social discovery features
• 📱 Tiered hardware strategy (premium to budget)

Testing Plan

• Prototype testing sessions
• In-depth interviews with audiophiles
• Competitor user interviews
• A/B testing of marketing messages
• Conjoint analysis for feature pricing

Key Lesson

Entering a mature market requires a fundamentally different approach that solves user problems in new ways while minimizing switching costs.

Problem Statement

Fashion retailer was experiencing 72% cart abandonment rate due to complex checkout process, resulting in significant revenue loss.

Analysis Approach

• Analyzed user journey using heatmaps & session recordings
• Conducted exit surveys with 500+ abandoning users
• Identified key friction points using funnel analysis
• Benchmarked against industry best practices

Key Insights

• 42% abandonment at account creation step
• 28% drop-off at shipping options page
• Unexpected fees shown late caused 18% of exits
• Mobile users had 35% higher abandonment rate

Solutions Implemented

• ✅ Guest checkout option with simplified flow
• ✅ Progress indicator showing checkout steps
• ✅ Shipping cost calculator earlier in flow
• ✅ Mobile-optimized form fields with auto-fill
• ✅ Saved payment options for returning users

A/B Test Results

Problem Statement

CRM platform had only 38% of new users completing core activation events within first week, limiting conversion to paid plans.

Research Methods

• Analyzed cohort retention data
• Conducted user interviews with churned users
• Performed usability testing of onboarding flow
• Mapped user journey with pain points

Key Findings

• Overwhelming setup process with 12 steps
• Lack of clear "aha moment" demonstration
• No guidance on initial setup best practices
• Technical jargon confusing new users

Solutions Implemented

• ✅ Progressive onboarding with 4-step setup
• ✅ Interactive product tour with tooltips
• ✅ Template library for quick setup
• ✅ Personalized success metrics dashboard
• ✅ In-app guidance for key features

Impact