Customer Segmentation - KMeans Clustering based on RFM

Overview:

Recency-Frequency-Monetary Value (RFM) is a quantitative technique used to analyze & group customers based on their purchase history by assigning numerical values (1 to K) for customer groups. In this study, I computed RFM for each customer based on their purchases (using definitions below), then applied the K-Means clustering algorithm, which aims to partition N-observations into K-clusters by assigning each observation a random cluster number (0 to K), based on nearest 'Means'.

Using this randomly generated cluster numbers (0 to 4) as scores, I re-structured it to a scale of 1 to 5 and assigned the customer group that is recent, frequent, and generates high monetary value a higher score.

• Recency: Days since the last transcation.
• Frequency: Transactions Count.
• Monetary Value: Average Monetary Value Generated.

Applications:

• This approach can be utilized to quantitatively understand Customer base.
• Implement Personalized Marketing strategies to customer groups.

Recency, Frequency and Monetary Value Clusters

Data Source:

To perform this study I used Online Retail dataset.

Final Report & Package Walk-Through

To reproduce this study, use modules in 'src' directory of this repo. (setup instructions below) and walk-through of the package is presented in the final report

Setup instructions

Creating Python environment

This repository has been tested on Python 3.7.6.

1. Cloning the repository:

git clone https://github.com/abhijitpai000/customer_segmentation_rfm.git

2. Navigate to the git clone repository.

cd customer_segmentation_rfm

3. Install virtualenv

pip install virtualenv

virtualenv rfm

4. Activate it by running:

rfm/Scripts/activate

5. Install project requirements by using:

pip install -r requirements.txt

Note Please ensure the raw .csv file is placed in 'datasets' directory.