Self-Initiated Analytics Project | January 2026
This page demonstrates how I approach business problems end-to-end — from defining the question, analyzing data, making judgment-based decisions, and translating insights into recommendations.
📄 Note:
Detailed methodology, assumptions, calculations, and documentation are maintained separately in Confluence ****and available on request. This portfolio focuses on what I did, how I thought, and what I delivered.
Analyzed one year of retail sales data (₹4.84L revenue, 1,994 transactions) to identify performance drivers and build a 6-month revenue forecast with scenario planning.
Key Result: Identified ₹2.38L upside opportunity with 2.2:1 favorable risk-reward profile as the primary value driver.
A retail business needed to plan for the next 6 months but faced a problem: historical growth rates were all over the place.
Monthly growth ranged from -68% to +1,132%, averaging 117%. While that average is mathematically correct, it's operationally meaningless—no business sustains 117% monthly growth.
The core question: How do you build a reliable forecast when historical data is this volatile?
Analyzed 12 months of transactional data to identify: