This repository contains an in-depth analysis of Walmart's retail dataset. The analysis delves into various factors, including customer segments, order numbers, order priorities, and shipment methods. The primary goal is to gain insights into sales and customer behavior. The analysis is conducted in several stages, starting with RFM analysis and followed by K-means clustering.
The dataset used in this analysis contains valuable information from Walmart's retail operations. It includes details on customer transactions, order priorities, shipment methods, and more. The data serves as the foundation for understanding Walmart's retail performance and customer dynamics.
The initial phase of the analysis focuses on Recency, Frequency, and Monetary (RFM) analysis. RFM is a powerful method for segmenting customers based on their transaction behaviors. It provides insights into customer value and helps identify high-value segments.
Following the RFM analysis, the dataset is subjected to K-means clustering. Two clustering approaches are explored: one with three clusters and another with four clusters. K-means clustering enables us to group similar customers together, uncover hidden patterns, and understand customer segments more effectively.
The analysis yields valuable findings about customer behavior, sales trends, and customer segments. These findings form the basis for recommendations to improve Walmart's retail performance. Recommendations may include strategies to target specific customer segments, optimize order priorities, or enhance shipment methods.