Kaushik Yeddanapudi Growth & Product Analyst | BCom Business Analytics Graduate - 2025 Focus: End-to-End SaaS Analytics System — from raw relational data to business intelligence.

Built an end-to-end SaaS analytics system to unify product usage, subscription lifecycle, and revenue data into a single analytical layer.

The system is designed to answer key business questions: • Where are users dropping off in the lifecycle? • Which customers are likely to churn? • What drives revenue growth and retention?

It combines SQL-based analysis, cohort modeling, and structured data design to transform raw data into decision-ready insights.

The system is built on 5 relational tables

— Users, Subscriptions, Payments, Products, Subscription Items, and User Activities

Designed to capture the complete customer lifecycle. Foreign key relationships enable cross-table analysis: linking payment behavior to subscription tier, product adoption to churn risk, and user demographics to revenue segmentation. This schema was the foundation for every SQL query in the project.

The project started with a clear problem:

SaaS companies operate on fragmented data. Product, finance, and retention teams each have partial visibility but no unified view.

This system was built to solve that — tracking 1M+ records across user behavior, subscription lifecycle, payment flows, and product usage to generate CLV, RFM segments, and churn signals in a single analytical framework.

Five core analyses were run against the dataset.

Customer Lifetime Value revealed that the top 5 users averaged $19,900+ each — disproportionate concentration that signals high dependency risk.

RFM segmentation identified 590 At-Risk users who haven't been addressed.

Revenue spiked 207% in May but trended downward, indicating acquisition without retention. Most critically — churn grew 5.6× in 5 months, from 814 to 4,575 users.