In this project, we aim to perform customer segmentation using RFM modelling and K-Means Clustering. Customer segmentation is the process of dividing customers into groups based on their behavior, demographics, or other characteristics. The purpose of this is to create targeted marketing campaigns and improve customer engagement.
We will be using a dataset containing transactional data of customers from an online retail store. E-commerce database that lists purchases made by ∼ 4000 customers over a period of one year (from 2010/12/01 to 2011/12/09)
-We will first perform RFM analysis to determine the recency, frequency, and monetary value of each customer's purchases. RFM is a customer segmentation technique that allows us to categorize customers based on these three dimensions.
-Next, we will use K-Means clustering to group customers based on their RFM scores. K-Means is an unsupervised learning algorithm that partitions a dataset into K clusters. We will use this algorithm to group customers into clusters based on their RFM scores.
We will be using Python for this project, along with the following libraries:
-Pandas: for data manipulation and analysis -NumPy: for numerical computing -Matplotlib and Seaborn: for data visualization -Scikit-learn: for implementing the K-Means clustering algorithm
The deliverables for this project will include:
- A Jupyter Notebook with the code and analysis
- A written report summarizing the findings and insights gained from the analysis
- Data visualizations to illustrate the results of the analysis
Through this project, we aim to gain insights into customer behavior and preferences, and identify opportunities for targeted marketing campaigns. The results of this analysis will help the online retail store to improve customer engagement and increase sales.