The objective was to analyze user behavior on the platform, focusing on new and recurring paying users and their growth over time. The analysis used two datasets: registration data for 10,000 users, including ID, registration date, and platform, and transaction data with 30,448 records detailing transaction ID, user ID, status, amount, and date. Key variables included user ID, transaction status, amount, and date.

New paying users peaked in October 2019 and July 2020, likely driven by promotions or events, while cumulative paying users steadily grew to 4,081 by May 2022. Recurring users increased slowly after 2021, highlighting a need to improve retention, and overall user growth slowed in 2022, showing reduced engagement. Strategies should focus on replicating successful acquisition campaigns, boosting recurring engagement through loyalty programs and better experiences, addressing the 2022 slowdown with market expansion and product updates, and using real-time analytics and A/B testing to adjust tactics based on user behavior.

To solve the problem, I prepared and formatted transaction and registration data, ensuring proper datetime conversion. I then wrote SQL queries to identify new, cumulative, and recurring paying users, aggregating them monthly. Using Python and SQL, I visualized trends, comparing growth rates, identifying peak periods, and analyzing cumulative user behavior to gain insights into user acquisition and retention.