RFM-segmenten analyseren

Klantsegmentatie in Python

Karolis Urbonas

Head of Data Science, Amazon

Grootste RFM-segmenten

datamart.groupby('RFM_Segment').size().sort_values(ascending=False)[:10]

top_rfm_segments

Filteren op RFM-segmenten

Selecteer het laagste RFM-segment "111" en bekijk de top 5 rijen

datamart[datamart['RFM_Segment']=='111'][:5]

top-111-segments

Samenvattende metrics per RFM-score

datamart.groupby('RFM_Score').agg({
    'Recency': 'mean',
    'Frequency': 'mean',
    'MonetaryValue': ['mean', 'count'] })/
    .round(1)

Groeperen in benoemde segmenten

Gebruik de RFM-score om klanten in de segmenten Gold, Silver en Bronze te groeperen.

def segment_me(df):
    if df['RFM_Score'] >= 9:
        return 'Gold'
    elif (df['RFM_Score'] >= 5) and (df['RFM_Score'] < 9):
        return 'Silver'
    else:
        return 'Bronze'

datamart['General_Segment'] = datamart.apply(segment_me, axis=1)

datamart.groupby('General_Segment').agg({
    'Recency': 'mean',
    'Frequency': 'mean',
    'MonetaryValue': ['mean', 'count']}).round(1)

Nieuwe segmenten en hun waarde

Oefen met eigen segmenten bouwen

Klantsegmentatie in Python