Recency, Frequency, Monetary Model with Python

The last time we analyzed our online shopper date set using the cohort analysis method. We discovered some interesting observations around our cohort data set. While cohort analysis provides us with customer behavior overtime and understand retention rates, we also want to be able to segment our data by their behavior as well.

In this article, we will be exploring the popular RFM model used by retailers blending in-store and online purchases to segment their customers for better personalized ad content.

What is RFM?

Behavioral segmentation by 3 important features:

1. Recency - numbeer of days since the last purchase
2. Frequency number of transactions madee over a given period
3. Monetary - amount spent over a given period of time


    # Import libraries
    from datetime import timedelta
    import pandas as pd


    # Read data using pandas
    data = pd.read_csv('../data.csv')
    # Convert date format
    data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'])


    # Create TotalSum column for dataset
    data['TotalSum'] = data['Quantity'] * data['UnitPrice']
    
    # Create snapshot date
    snapshot_date = data['InvoiceDate'].max() + timedelta(days=1)

    # Grouping by CustomerID
    data_agg = (
      data
      .groupby(['CustomerID'])
      .agg(
        {
          'InvoiceDate': lambda x: (snapshot_date - x.max()).days,
          'InvoiceNo': 'count',
          'TotalSum': 'sum'
        }
      )
    )
    
    # Clean up column names
    data_process.rename(
      columns = {
        'InvoiceDate': 'Recency',
        'InvoiceNo': 'Frequency',
        'TotalSum': 'MonetaryValue'
      }, inplace=True
    )


              Recency  Frequency  MonetaryValue
  CustomerID                                   
  12346.0         326          2           0.00
  12347.0           2        182        4310.00
  12348.0          75         31        1797.24
  12349.0          19         73        1757.55
  12350.0         310         17         334.40
  4,372 rows; 3 columns