This project focuses on analyzing customer churn within a business, aiming to understand and predict customer attrition. Churn analysis is crucial for businesses to identify factors contributing to customer churn and take proactive measures to retain valuable customers.
- Programming Language: Python
- Data Analysis Libraries: Pandas, NumPy
- Machine Learning Libraries: Scikit-learn
- Data Visualization: Matplotlib, Seaborn
- Jupyter Notebooks: Used for data exploration and analysis.
The project leverages a combination of exploratory data analysis (EDA) and machine learning to identify patterns and build predictive models for customer churn.
In today’s global scenario post-pandemic, shopping has become more indoor leisure activity. The pandemic represented a humanitarian crisis pushing billions of people towards being more technologically adept. E-Commerce stores contribute highly to the consumer base traffic and increase individuals’ marginal benefit without compromising budgets. And thus, are placed with a wide variety of choices to pick from increasing customer churn incidence owing to the large e-commerce stores. Spending is based on individual wants and preferences thus demand of a particular product defines the price paid. We will explore the different consumer behaviour while making purchases and its relevance on pricing structures.
Customer churn, often referred to as customer attrition or turnover, is a critical metric for businesses, indicating the rate at which customers cease their association with a service or product. In the context of this project, understanding and addressing customer churn is pivotal for businesses aiming to retain their customer base.
Churn analysis involves identifying patterns and factors contributing to customer attrition, enabling proactive measures to be taken for customer retention. This project focuses on analyzing customer churn, exploring various features influencing it, and implementing machine learning models to classify customers into churn and non-churn categories.
- Determine the percentage of churned customers and those actively using services.
- Analyze data to identify key features influencing customer churn.
- Select an optimal machine learning model for accurate churn classification.
In today's competitive E-Commerce landscape, customer retention is challenging due to global competition and technological advancements. Analyzing factors behind churn behavior is crucial for devising targeted offers and minimizing attrition. With an annual churn rate potentially reaching 25% in the E-Commerce sector, my aim is to align it with industry standards, as reducing it to 10% could yield a significant revenue increase of around 5%. Retaining existing customers is cost-effective, with the potential for substantial profit spikes. My focus is on effective customer segmentation to tailor promotional offers, fostering loyalty and optimizing purchases.
Yes, this project can be applied in real-life scenarios, providing valuable insights for businesses looking to reduce customer churn. For instance, here an e-commerce company might use this analysis to identify customers at risk of leaving and implement targeted retention strategies.