Business Analyst
| Data Analytics

Business Challenge

Retrospective analysis of 100K+ OLA bookings (10,000+ records deep-dived) surfaced a marketplace under strain — completed rides converting only ~62% of demand:

• 37% overall ride-failure rate: 22% customer cancellations, 15% driver cancellations — over 55% of customer cancellations traced back to driver behaviour (stationary drivers, forced cancels)
• 53% of riders were one-time users — a major LTV leak against rising customer acquisition cost
• Cash still processed ₹19.3M vs UPI's ₹14.2M — slower reconciliation, higher dispute-handling cost
• Premium tiers (Prime Sedan, SUV) leaked ~37% of gross booking value to cancellations despite carrying the highest fares

BA Process & Approach

• Ran Agile Scrum delivery across 4 two-week sprints — elicitation → design → build → UAT
• Elicited requirements via stakeholder interviews (Ops Director, Finance Lead) and cross-functional workshops with Engineering, Product & Marketing
• Mapped AS-IS vs TO-BE process flows and ran formal gap analysis to isolate root causes
• Authored BRD, FRD, 3 UML diagrams, and prioritised user stories with acceptance criteria & story points
• Queried 10,000+ bookings in MySQL to quantify cancellation drivers, vehicle utilisation, and payment mix
• Owned backlog across 3 epics in Jira; ran 4 UAT scenarios to sign-off before production release

Business Impact

BA Deliverables

Business Challenge

A U.S.-based multi-region superstore generating $2.29M across ~10K transactions had plateaued on profitability despite strong top-line revenue. The mandate: find where value was leaking and convert insight into action.

• 21.1% of orders were loss-making — driven by deep discounts above 30%
• 42.8% of all orders included discounts, often without margin guardrails
• Tables, Bookcases, and Supplies sub-categories consistently destroyed value
• South region underperformed on margin despite healthy sales volume
• 40%+ of customers were one-time buyers while a small cohort drove outsized revenue

Analytical Approach

• Analysed 9,994 order-level transactions across regions, segments, sub-categories, and shipping modes
• Applied SQL-based structured analysis to validate profit calculations and operational assumptions
• Diagnosed discounting, logistics efficiency, and customer retention as primary margin drivers
• Identified pricing, bundling, and portfolio rationalisation candidates by sub-category
• Mapped shipping strategy to margin impact across fulfilment modes

Business Impact

The Challenge

Instead of repeating the same type of project, I picked a new industry every day and solved a real business problem — building analytical thinking across domains, not just technical skills.

• Day 1 — E-commerce: best customers & sales drivers
• Day 2 — SaaS: churn despite growing signups
• Day 3 — HR: why employees were actually quitting
• Day 4 — Retail: sales up, profit down — why?
• Day 5 — Marketing: which campaigns were worth the spend
• Day 6 — Banking: loan default prediction & risk profiling
• Day 7 — Food Delivery: root-cause of late deliveries
• Capstone — Airbnb NYC: what makes a listing succeed

Analytical Approach

• Started each day by framing the business problem, not the data
• Applied SQL for querying, Excel for cleaning, Power BI for dashboards
• Developed KPIs tailored to each industry's decision context
• Delivered stakeholder-ready recommendations for each domain
• Practiced problem framing, business storytelling, and churn/risk/ops analysis

Key Outcomes