Customer-Segmentation-with-RFM-Analysis

This project is created to segment customer based on their buying pattern using RFM Analysis. Further creating K-Mean Clustering model for more precise segmentation for optimize result Dataset Link: https://www.kaggle.com/datasets/tunguz/online-retail

Description

This project is constructed to segment customers based on their buying patters.

• Conduct RFM Analysis to divide customers on differnt groups.
• Produce K-means clustering unsupervised machine learning model to form clusters of customer with similar recency, frequency, monetary
• Observe each clusters and then make conclusion

RFM Analysis

RFM Analysis is a concept used by Data Science professionals, especially in the marketing domain for understanding and segmenting customers based on their buying behaviour. Using RFM Analysis, a business can assess customers’:

• Recency (the date they made their last purchase)
• Frequency (how often they make purchases)
• Monetary (the amount spent on purchases) Recency, Frequency, and Monetary value of a customer are three key metrics that provide information about customer engagement, loyalty, and value to a business.

K-Mean Clustering

Based on Recency, Frequency, Monetary K-mean clustering model is created.Before model the data is preprocessed using log transformation, scaling and outliers treatment using Windsorization. Using Flatten Clustering Plot and silhouette score, 4 cluster for optimized clustering is concluded

K-Mean Clustering Interpretation

Here are the interpretation of each cluster

• Cluster0: Most Recent customer with average frequency and monetary, mostly "Look out buyers".
• Cluster1: "Best Customers" with recent puchases, most frequent buyers with higher monetary investment
• Cluster2: "Lost Cheap Customer" with last purchase long ago, purchased very few times with little spend
• Cluster3: "Almost Lost Customer" , who haven’t purchased for some time(R=3), but used to purchase frequently and spent a lot