Overview This project is an in-depth analysis of user engagement, acquisition, retention, and monetization strategies for a digital platform. It leverages various datasets including user registrations, client additions, audio file uploads, note signings, and Stripe charges to uncover insights into user behavior and platform performance. The primary goal is to enhance user engagement, improve content quality, optimize monetization, and reduce churn through data-driven recommendations.
Datasets The analysis uses several datasets including:
adds_new_client.csv - Data on new clients added. exports_note.csv - Notes exported by users. new_registration.csv - User registration information. signs_notes.csv - Information on notes signed by users. stripe_charges.csv - Stripe payment data. uploads_audio_file.csv - Data on audio files uploaded by users. Features The project includes:
Data Preparation: Cleaning and preprocessing steps such as timestamp conversion, handling null values, and renaming columns for clarity. User Acquisition and Engagement Analysis: Insights into yearly, monthly, and weekly new registrations, along with engagement patterns like adding new clients, uploading audio files, and signing notes. Content Quality and User Feedback: Analysis of engagement with signed notes, average note length distribution, and correlation matrix for note properties. Monetization and User Conversion: Insights into Stripe charges distribution, upgrade plan distribution, and upgrade propensity by user. User Retention and Churn: Calculations of churned users, churn rates over different observation periods, and analysis related to payments. Cohort Analysis: Examining user activity by cohort, average time spent by cohort, and trends in monthly active users (MAU). Data-driven Recommendations: Suggestions for improving Week 1 user retention rate, MAU growth rate, and implementing these recommendations. Potential Improvement and Recommendations: Proposals for collecting additional user information, implementing granular event tracking, soliciting qualitative feedback, and enhancing data infrastructure. Installation Instructions for setting up the project environment:
bash
git clone https://github.com/AyatoShirayouki/Product-Analytics-Project/tree/master
Usage The analysis is performed through a series of Jupyter notebooks which can be run to replicate the analysis and visualize the findings: jupyter notebook solution.ipynb