In this project, your task is to identify major customer segments on a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.
- Data Cleaning
- Exploratory Data Analysis (EDA)
- RFM
- Clustering
- Cluster Profiling
- Conclusion
The RFM model is quite useful model in retail customer segmentation where only the data of customer transaction is available. RFM stands for the three dimensions:
Recency – How recently did the customer purchase? - Number of days since last purchase
Frequency – How often do they purchase? - The total number of purchases
Monetary Value – How much do they spend? - The total money customer spent
A combination of these three attributes can be defined to assign a quantitative value to customers. e.g. A customer who recently bought high value products and transacts regularly is a high value customer.
- K-Means Clustering
- Silhouette Analysis
- Elbow Method
- DBSCAN Clustering
- Hierarchical Clustering
- Agglomerative Clustering (Bottom-up approach using tree like diagram called Dendogram)
- RFM(Recency, Frequency and Monetary) dataframe ease our problem to solve in a particular order, it makes easy to recommend and display new launched products to few customers.
- And total number clusters for each of the used models came 2.
Sonica Sinha
Mohd Taufique
