The objective of our project is to categorize customers into various clusters to identify and predict which customers are likely to respond to promotions and for future personalization services using RFM Analysis. The RFM model is derived from three quantitative factors: -
1.Receny: How recently a customer made a purchase
2.Frequency: How frequently a customer makes a purchase
3.Monetary: How much money a customer spends on purchases
We have taken the Online_Retail dataset from UCI Machine Learning (Link: https://archive.ics.uci.edu/ml/datasets/online+retail) to perform RFM analysis and Kmeans Clustering.
It is a transnational data set which contains transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company sells unique all-occasion gifts. Many customers of the company are wholesalers.
The following is the attribute information:
1.InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.
2.StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.
3.Description: Product (item) name. Nominal.
4.Quantity: The quantities of each product (item) per transaction. Numeric.
5.InvoiceDate: Invice Date and time. Numeric, the day and time when each transaction was generated.
6.UnitPrice: Unit price. Numeric, Product price per unit in sterling.
7.CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.
8.Country: Country name. Nominal, the name of the country where each customer resides.