Creare segmenti RFM

Customer Segmentation in Python

Karolis Urbonas

Head of Data Science, Amazon

Dati

Dataset creato in precedenza
Calcoliamo i quartili per ogni colonna e li chiamiamo R, F, M

rfm_data

Quartile di recency

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

Quartili di frequency e monetary

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)

Crea segmento RFM e punteggio RFM

Concatena i valori RFM in RFM_Segment
Somma i valori RFM in 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)

Risultato finale

rfm_table

Esercitiamoci a creare segmenti RFM

Customer Segmentation in Python