Exploratory Data Analysis

With

SQL | Python

Exploratory Data Analysis

With

SQL | Python

Exploratory Data Analysis

With

SQL | Python

Exploratory Data Analysis

With

SQL | Python

Exploratory Data Analysis

With

SQL | Python

Exploratory Data Analysis

With

SQL | Python

Exploratory Data Analysis

With

SQL | Python

Exploratory Data Analysis

With

SQL | Python

Exploratory Data Analysis

With

SQL | Python

Uncovering Patterns in Paying User Behavior to Drive Growth Strategies

This project was focused on analyzing transaction data to understand the trends in paying users for an educational platform. The objective was to determine the number of new paying users, the cumulative number of paying users, and the recurring paying users on a monthly basis. The insights derived from this analysis aimed to support strategic decisions regarding user acquisition and retention.

Data Details

The project utilized two main datasets: 1) Platform Registration Data: Contains information on 10,000 users, including their ID, registration date, and platform. 2) Platform Transaction Data: Contains 30,448 transactions, with details such as transaction ID, user ID, status, amount, and transaction date. Key variables include user ID, transaction status (success/failure), amount, and date.

The Problem

The challenge was to analyze user behavior on the platform, specifically identifying: The number of new paying users each month. The cumulative number of paying users over time. The number of recurring paying users who made at least two successful transactions, and their growth over time.

The Solution

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.

Showcase

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Insight

Peaks in new paying users during October 2019 and July 2020, likely due to external factors such as promotions or events. A steady increase in cumulative paying users, reaching 4,081 by May 2022. Recurring paying users grew slowly after 2021, indicating a need for strategies to boost retention. A slowdown in user growth was evident in 2022, signaling a shift in user engagement.

Recommendation

1) Enhance User Acquisition: Investigate peak periods to replicate successful marketing campaigns. Use targeted marketing and optimize referral programs. 2) Boost Recurring User Engagement: Introduce loyalty programs, improve user experience, and use re-engagement strategies to reduce churn. 3) Address 2022 Slowdown: Explore market expansion, product innovations, and seasonal promotions to revive user interest. 4) Continuous Data-Driven Monitoring: Implement real-time analytics and A/B testing to adapt strategies dynamically based on user behavior.

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Analyzing Trends in Paying Users on Educational Platform

Education

Private

December 27, 2024

Project details

Uncovering Patterns in Paying User Behavior to Drive Growth Strategies

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