RFM Analytics with Python
In this project, I segmented customers using RFM Analysis.
RFM stands for Recency,Frequency and Monetary
Recency: How much time has elapsed since a customer’s last activity or transaction with the brand? Activity is usually a purchase, although variations are sometimes used, e.g., the last visit to a website or use of a mobile app. In most cases, the more recently a customer has interacted or transacted with a brand, the more likely that customer will be responsive to communications from the brand.
Frequency: How often has a customer transacted or interacted with the brand during a particular period of time? Clearly, customers with frequent activities are more engaged, and probably more loyal, than customers who rarely do so. And one-time-only customers are in a class of their own.
Monetary: Also referred to as “monetary value,” this factor reflects how much a customer has spent with the brand during a particular period of time. Big spenders should usually be treated differently than customers who spend little. Looking at monetary divided by frequency indicates the average purchase amount – an important secondary factor to consider when segmenting customers.
An e-commerce company which sells souvenirs wants to segment its customers and determine marketing strategies according to these segments.
For this purpose, we will define the behavior of customers and we will put the customers into same groups who exhibit common behaviors and then we will try to develop sales and marketing techniques specific to these groups.
The data set “online_retail_II” includes the sales of this online shop between 01/12/2009 – 09/12/2011. For this study, the data of the year 2010–2011 is chosen.
Variables: InvoiceNo: Invoice number. It is a unique value. If this code starts with C, it means refund. StockCode: Product code. Unique number for each product Description: Product name Quantity: Number of products. It means how many of the products in the invoices are sold. Those who start with C get negative value InvoiceDate: Invoice date and time UnitPrice: Product price (in pounds) CustomerID: Customer number. Unique number for each customer Country: Country name. Refers to the country where the customer lives
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