Business Challenge: Cohort Analysis for Ironhack Payments (Project 1)

Introduction

IronHack Payments, a cutting-edge financial services company, has been offering innovative cash advance solutions since its inception in 2020. With a commitment to providing free cash advances and transparent pricing, IronHack Payments has gained a substantial user base. As part of their ongoing effort to enhance services and understand user behavior, IronHack Payments has commissioned a cohort analysis project.

Project Overview

In this project, you will conduct a comprehensive cohort analysis based on data provided by IronHack Payments. The primary objective is to analyze user cohorts defined by the month of their first cash advance. You will track the monthly evolution of key metrics for these cohorts, providing IronHack Payments with valuable insights into user behavior and the performance of their financial services.

Metrics to Analyze

You will calculate and analyze the following metrics for each cohort:

1. Service Usage Frequency: Understand how frequently users in each cohort utilize IronHack Payments' cash advance services over time.
2. Incident Rate: Determine the incident rate, focusing specifically on payment incidents, for each cohort. Identify if there are variations in incident rates across different cohorts.
3. Revenue Generated by the Cohort: Calculate the total revenue generated by each cohort over the months to assess the financial impact of user behavior.
4. New Relevant Metric: Propose and calculate a new relevant metric that provides additional insights into user behavior or the performance of IronHack Payments' services.

Data Analysis Tools

You are expected to perform the cohort analysis using Python, primarily leveraging the Pandas library for data manipulation and analysis. However, the core analysis must be done using Python.

Exploratory Data Analysis (EDA)

Before diving into the cohort analysis, conduct an exploratory data analysis (EDA) to gain a complete understanding of the dataset. Explore key statistics, distributions, and visualizations to identify patterns and outliers. The EDA will help you make informed decisions about data preprocessing and analysis strategies.

Data Quality Analysis

Evaluate the quality of the dataset by identifying missing values, data inconsistencies, and potential errors. Implement data cleaning and preprocessing steps to ensure the reliability of your analysis. Document any data quality issues encountered and the steps taken to address them.

Deliverables

1. Python Code: Provide well-documented Python code that performs the cohort analysis, including data loading, preprocessing, cohort creation, metric calculation, and visualization.
2. Exploratory Data Analysis Report: Prepare a report summarizing the findings from your exploratory data analysis. Include visualizations and insights that help in understanding the dataset.
3. Data Quality Analysis Report: Document the results of your data quality analysis, highlighting any issues and the steps taken to resolve them.
4. Short Presentation: Create a concise presentation (maximum of 4 slides) that summarizes your findings from the cohort analysis and key insights obtained from the EDA and data quality analysis. This presentation should be suitable for sharing with the IronHack Payments team.