Welcome to the "Customer Segmentation with RFM" project! In this project, we aim to demonstrate the process of customer segmentation using RFM analysis, a valuable technique for marketing and business strategy.
- Segment the e-commerce company's customers into meaningful groups.
- Develop targeted marketing strategies based on these customer segments.
- Gain insights into customer behavior and preferences.
This project will guide you through the steps of data understanding, preparation, RFM metrics calculation, and the creation of RFM segments. It will provide you with valuable insights into the dataset and demonstrate the power of RFM analysis in improving marketing efforts.
Let's get started with the project steps!
- Business Problem
- Data Understanding
- Data Preparation
- Calculating RFM Metrics
- Calculating RFM Scores
- Creating & Analysing RFM Segments
- Functionality of the Whole Process
An e-commerce company wants to segment its customers and determine marketing strategies based on these segments.
The dataset, named Online Retail II, includes the sales of an online retail store based in the UK between 01/12/2009 and 09/12/2011.
- InvoiceNo: Invoice number. A unique number for each transaction or invoice. If it starts with 'C', it's a canceled transaction.
- StockCode: Product code. A unique number for each product.
- Description: Product name.
- Quantity: Product quantity. Indicates how many of each product were sold in invoices.
- InvoiceDate: Invoice date and time.
- UnitPrice: Product price (in GBP).
- CustomerID: Unique customer number.
- Country: Country name. The country where the customer resides.
- Clone the project to your local machine.
- Use
pip install -r requirements.txtto install the necessary dependencies. - Run the main analysis file:
python main.py.
If you would like to contribute, please submit a "Pull Request" or open an "Issue." Contributions are welcome!
This project is open-source and licensed under the MIT License.