Built a SaaS analytics project using SQL and Python where I reconstructed customer lifecycle from snapshot data and analyzed key metrics like MRR, churn, cohort retention, ARPU, and LTV. I found that revenue growth was driven by customer acquisition rather than pricing, as ARPU declined over time while MRR increased

Jupyter notebook analyzing subscription revenue performance based on user transaction data (conversion rate, ARPU, revenue forecast).

A complete ETL solution with a PostgreSQL database, enabling calculation of key business metrics (KPI) such as revenue, ARPU, LTV, churn, retention, and cohort analysis.

Analizing customer purchase behaviour per cohorts using Online Retail II Dataset from Kaggle.

Python in use of pandas, numpy, sklearn, statsmodels, gensim, matplotlib, plotly, seaborn, bokeh

The case study is based on how a subscription-based e-commerce business employed customer-centric strategies to reduce churn and increase customer lifetime value. How companies are Maximizing customer spending and loyalty while minimizing subscription cancellations to enhance profits and long-term business sustainability in an e-commerce model.