Predict Quarterly Sales with the RFM Model
Explore how to predict quarterly customer sales with the RFM model by analyzing transaction data from the CDNOW dataset. Learn to preprocess data, calculate recency, frequency, and monetary values, and apply linear regression to forecast future sales. This lesson equips you to build and evaluate predictive models using historical purchase behavior, helping you understand customer value and improve marketing strategies.
In this lesson, we’ll predict a customer’s quarterly spending from historical transactions. We’ll use the CDNOW dataset, which contains the historical online purchases of compact disks and applies the RFM (recency, frequency, and monetary) model to predict quarterly sales numbers.
Here are the details of the dataset.
Feature Details
Feature | Data Type | Details |
customer_id | Integer | Unique identifier of a customer |
date | String | Date and time of the transaction |
quantity | String | Quantity sold in a transaction |
price | Integer | Total sale amount |
RFM model
RFM model is a method used in marketing to analyze and segment customers based on their historical purchasing behavior. The three elements of the RFM model are:
Recency: How recently a customer made a purchase.
Frequency: The number of purchases made by a customer in a given time period.
Monetary: The total amount spent by a customer.
Data processing
First, let’s load the dataset and change the data type of the column ...