Amazon Engagement Optimization Dashboard
Category
Data Analytics · BI
Date
March 2025
Client
Self-Initiated Project (Portfolio)
This project analyzes Amazon’s user engagement trends, cart abandonment rates, and conversion performance using Power BI and data analytics. The insights help in optimizing user retention, improving conversions, and understanding customer behavior across different traffic sources.
PROBLEM STATEMENT
With millions of users visiting Amazon daily, only a small fraction make it to the final purchase stage. I wanted to answer one key question: Where are we losing users, and why?
This project began with a curiosity to uncover bottlenecks in the user journey — from first click to final conversion — and offer actionable, data-backed insights to boost performance across devices, traffic sources, and campaign channels.
MY APPROACH
Using Power BI, MySQL, and Excel, I built an interactive dashboard that visualizes the full engagement funnel — from click-through to purchase — and exposes high-risk drop-off points with precision. Here’s how I tackled it:
User Journey Mapping: Analyzed a complete funnel — from 1M+ clicks to final purchases — to identify where users drop off most.
Cart Abandonment Deep Dive: Tracked abandonment rates across traffic sources (Organic, Social, Email, Direct).
Device Performance Comparison: Compared engagement and conversion by Mobile, Desktop, and Tablet to discover underperforming platforms.
Visual Storytelling: Used intuitive tables, charts, and color cues to narrate the flow — not just numbers.
KEY INSIGHTS DISCOVERED
Total Active Users: 1,024,000
- Average Session Duration: 5.4 minutes
- Cart Abandonment Rate: 68%
- Conversion Rate: 3.2%
📱 User Engagement by Device
Mobile: 58% users, 2.1% conversion
Desktop: 32% users, 5.8% conversion ← 🔥 Top performer
Tablet: 10% users, 3.9% conversion
📊 Clicks to Purchases
Clicks: 1,000,000
Added to Cart: 450,000 → 📉 55%
Drop-off Purchases: 144,000 → 📉 68% Drop-off from cart
🌐 Cart Abandonment Rate
Social Media → 76% ❌
Organic Search → 61%
Email Marketing → 54%
Direct Website Visits → 47% ✅
LESSONS LEARNED
Learned to apply funnel analysis to identify user leakage points in large-scale e-commerce data.
Leveraged DAX and Power BI visual storytelling to present data not just as charts, but as clear, business-focused narratives.
Integrated multiple data sources (MySQL + Excel) to build a cohesive picture of performance and behavior.
Understood the value of segmenting insights by device, channel, and stage for sharper decision-making.
Recommendations & Business Impact
✅ Optimize Mobile Experience Mobile drives engagement but not conversions. UX changes and streamlined checkout can close that gap.
✅ Reduce Cart Abandonment Use discounts, email reminders, and simplified flows to re-engage users stuck at “Added to Cart.”
✅ Improve Social Media Conversions Social had the highest drop-off. Implement retargeting and A/B tested landing pages to boost returns.
✅ Maximize Desktop ROI Capitalize on desktop’s higher conversion rate with premium offers and campaigns optimized for wider screens.
✅ Leverage Peak Hours Analyze session timing data to run email & ad campaigns during peak user activity.
TECH STACK
GIT LINK
GitHub Repository
LINK
From clicks to conversions — this dashboard decodes user behavior to turn drop-offs into data-driven wins.
