The Customer Lifetime Value (CLV) project focuses on a critical aspect of business strategy: understanding the long-term value of customers. In today's competitive market, acquiring new customers can be expensive and challenging. Hence, it becomes essential for businesses to not only attract new customers but also retain and maximize the value generated from existing ones. Customer Lifetime Value helps companies make informed decisions regarding marketing, customer engagement, and resource allocation, leading to sustainable growth and profitability.
The primary goal of the Customer Lifetime Value Project is to develop a robust framework for predicting the potential value that a customer will generate over the course of their relationship with the business. By analyzing historical customer data and behavior, the project aims to create models that can forecast the expected revenue or profitability associated with each customer.
The project involves creating predictive models to estimate the Customer Lifetime Value for each customer based on various factors such as purchase history, frequency of transactions, average transaction value, customer demographics, and more. These models will assist businesses in identifying high-value customers, tailoring marketing strategies, optimizing customer retention efforts, and making data-driven decisions to enhance customer satisfaction and business growth.
The dataset used for the Customer Lifetime Value Project includes historical customer data, transaction records, customer demographics, and relevant features that contribute to understanding customer behavior and value. The dataset serves as the foundation for training and validating the predictive models. Access to the dataset can be found in the project files.
The success of the project will be evaluated based on the accuracy of the Customer Lifetime Value predictions. Other metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared value will also be considered to assess the model's performance.
The project repository is organized as follows:
├── LICENSE
├── README.md <- README .
├── notebooks <- Folder containing the final reports/results of this project.
│ │
│ └── clv.py <- Final notebook for the project.
├── reports <- Folder containing the final reports/results of this project.
│ │
│ └── Report.pdf <- Final analysis report in PDF.
│
├── src <- Source for this project.
│ │
│ └── data <- Datasets used and collected for this project.
| └── model <- Model.
This project is licensed under the MIT License.
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