Building RFM segments

Customer Segmentation in Python

Karolis Urbonas

Head of Data Science, Amazon

Data

Dataset we created previously
Will calculate quartile value for each column and name then R, F, M

rfm_data

Recency quartile

r_labels = range(4, 0, -1)

r_quartiles = pd.qcut(datamart['Recency'], 4, labels = r_labels)

datamart = datamart.assign(R = r_quartiles.values)

r-value

Frequency and monetary quartiles

f_labels = range(1,5)
m_labels = range(1,5)

f_quartiles = pd.qcut(datamart['Frequency'], 4, labels = f_labels)
m_quartiles = pd.qcut(datamart['MonetaryValue'], 4, labels = m_labels)

datamart = datamart.assign(F = f_quartiles.values)
datamart = datamart.assign(M = m_quartiles.values)

Build RFM segment and RFM score

Concatenate RFM quartile values to RFM_Segment
Sum RFM quartiles values to RFM_Score

def join_rfm(x): return str(x['R']) + str(x['F']) + str(x['M'])

datamart['RFM_Segment'] = datamart.apply(join_rfm, axis=1)

datamart['RFM_Score'] = datamart[['R','F','M']].sum(axis=1)

Final result

rfm_table

Let's practice building RFM segments

Customer Segmentation in Python