Calculating RFM metrics

Customer Segmentation in Python

Karolis Urbonas

Head of Data Science, Amazon

Definitions

Recency - days since last customer transaction
Frequency - number of transactions in the last 12 months
Monetary Value - total spend in the last 12 months

Dataset and preparations

Same online dataset like in the previous lessons
Need to do some data preparation
New TotalSum column = Quantity x UnitPrice.

top_5_rows

Data preparation steps

We're starting with a pre-processed online DataFrame with only the latest 12 months of data:

print('Min:{}; Max:{}'.format(min(online.InvoiceDate),
                              max(online.InvoiceDate)))

Min:2010-12-10; Max:2011-12-09

Let's create a hypothetical snapshot_day data as if we're doing analysis recently.

snapshot_date = max(online.InvoiceDate) + datetime.timedelta(days=1)

Calculate RFM metrics

# Aggregate data on a customer level
datamart = online.groupby(['CustomerID']).agg({
    'InvoiceDate': lambda x: (snapshot_date - x.max()).days,
    'InvoiceNo': 'count',
    'TotalSum': 'sum'})

# Rename columns for easier interpretation
datamart.rename(columns = {'InvoiceDate': 'Recency',
                           'InvoiceNo': 'Frequency',
                           'TotalSum': 'MonetaryValue'}, inplace=True)

# Check the first rows
datamart.head()

Final RFM values

Our table for RFM segmentation is completed!

rfm_top_5

Let's practice calculating RFM values!

Customer Segmentation in Python