An England-based retail company aims to segment its customers and develop marketing strategies based on these segments. They believe that conducting marketing campaigns targeting customer segments exhibiting common behaviors will lead to increased revenue. RFM analysis will be utilized for customer segmentation.
The dataset named "Online Retail II" contains the online sales transactions of an England-based retail company between 01/12/2009 and 09/12/2011. The company's product catalog consists of gift items, and it is known that most of its customers are wholesalers.
P.S.: If the invoice number (InvoiceNo) starts with the code "C," it indicates that the transaction has been canceled.
RFM stands for Recency, Frequency, and Monetary. It is a popular marketing and customer segmentation technique used to analyze and categorize customers based on their past behavior and transaction data. The RFM model is widely used in customer relationship management (CRM) and marketing strategies to identify valuable customer segments and tailor personalized marketing efforts.
Here are the components of RFM:
Recency (R): Recency measures the time elapsed since a customer's last purchase or interaction with the business. Customers who have made recent purchases are often considered more engaged and likely to be responsive to marketing efforts.
Frequency (F): Frequency measures the number of times a customer has made a purchase or interacted with the business over a specific period. Customers with higher purchase frequency are typically more loyal and valuable to the business.
Monetary (M): Monetary represents the total monetary value of a customer's transactions within a specific timeframe. Customers with higher monetary value are those who have spent more money on their purchases.
By combining these three components, businesses can segment their customers into different groups or categories, such as "High-Value Customers," "Loyal Customers," "Churn Risk Customers," and "Inactive Customers." Each group can then be targeted with specific marketing strategies to optimize customer retention, cross-selling, and customer satisfaction. RFM analysis provides valuable insights to improve customer targeting, increase customer engagement, and drive revenue growth.