Project Summary: Customer Segmentation Using K-Means Clustering

Project Goal:

To segment customers of an online retail store using K-Means clustering to better understand their behavior and identify potential target markets.

Data:

• Online Retail Dataset: Contains transaction data from a UK-based online retail store from 2009 to 2011.
  • Dataset URL
• Key Features: Recency (time since last purchase), Frequency (total purchases), Monetary Value (total spending).

Methodology:

1. Data Exploration and Cleaning:

   • Analyzed data for missing values, outliers, and inconsistencies.
   • Identified and addressed issues such as negative quantities, invalid stock codes, and cancelled orders.
   • Dropped unnecessary or irrelevant data.

2. Feature Engineering:

   • Calculated RFM scores for each customer based on recency, frequency, and monetary value.

3. K-Means Clustering:

   • Applied K-Means clustering to the RFM scores to identify distinct customer segments.
   • Determined the optimal number of clusters using techniques like the elbow method.

4. Cluster Analysis:

   • Analyzed the characteristics of each cluster to understand customer behavior patterns.
   • Identified key differences between clusters in terms of purchase frequency, spending habits, and engagement levels.

Results:

• Successfully segmented customers into distinct groups based on their purchasing behavior.
• Identified high-value customers, frequent buyers, and customers with lapsed engagement.
• Provided insights for targeted marketing campaigns and customer retention strategies.

Conclusion:

K-Means clustering proved to be a valuable tool for customer segmentation in this project. The results can be used to tailor marketing efforts, improve customer satisfaction, and drive business growth.