Clustering para segmentación de clientes: ejemplo de principio a fin
Preprocesará un conjunto de datos de comercio electrónico, agrupará a los clientes según su gasto y frecuencia, y analizará cada segmento para obtener información empresarial.
Clustering para segmentación de clientes: ejemplo de principio a fin es una lección gratuita de Machine Learning Academy en CoddyKit. Esta es la lección 4 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de Machine Learning Academy, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Machine Learning Academy incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
The Business Goal: Segment Customers
Customer segmentation groups buyers by behaviour so that marketing, product, and customer-success teams can tailor their actions to each group. Typical signals include recency (days since last purchase), frequency (number of purchases), and monetary value (total spend) — the RFM framework. Clustering discovers these segments from data without needing predefined categories.
Loading and Inspecting the Dataset
We use the classic Online Retail dataset (UCI ML Repository). It contains ~500k transactions with invoice date, customer ID, quantity, and unit price. Our first task is to load the data, drop rows with missing customer IDs, filter out returns (negative quantity), and compute the RFM features for each customer.
import pandas as pd
df = pd.read_csv('online_retail.csv', encoding='latin1')
# Drop missing customers and returns
df = df.dropna(subset=['CustomerID'])
df = df[df['Quantity'] > 0]
df['Revenue'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])
print(df.shape)
print(df.dtypes)Engineering RFM Features
Recency: days since the customer's last purchase (smaller = more recent = better). Frequency: number of unique invoices. Monetary: total revenue generated. We compute these relative to a snapshot date (one day after the last transaction in the dataset) so recency increases with inactivity.
snapshot_date = df['InvoiceDate'].max() + pd.Timedelta(days=1)
rfm = df.groupby('CustomerID').agg(
Recency=('InvoiceDate', lambda x: (snapshot_date - x.max()).days),
Frequency=('InvoiceNo', 'nunique'),
Monetary=('Revenue', 'sum')
).reset_index()
print(rfm.describe())Treating Outliers and Skewness
RFM features are often highly right-skewed: a handful of VIP customers dominate the monetary axis. Before scaling, apply a log transform (np.log1p) to compress the long tail. Clip extreme outliers beyond the 99th percentile to prevent a single whale customer from distorting all centroids.
import numpy as np
for col in ['Recency', 'Frequency', 'Monetary']:
cap = rfm[col].quantile(0.99)
rfm[col] = rfm[col].clip(upper=cap)
rfm[col + '_log'] = np.log1p(rfm[col])
print(rfm[['Recency_log', 'Frequency_log', 'Monetary_log']].describe())Scaling Features for K-Means
K-Means uses Euclidean distance, so features must be on the same scale. After log-transforming, apply StandardScaler to centre each feature at zero with unit variance. Always fit the scaler on training data only — here the full RFM table since there is no separate test set for unsupervised learning.
from sklearn.preprocessing import StandardScaler
features = ['Recency_log', 'Frequency_log', 'Monetary_log']
X = rfm[features].values
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print('Mean after scaling:', X_scaled.mean(axis=0).round(4))
print('Std after scaling:', X_scaled.std(axis=0).round(4))Selecting k with Elbow and Silhouette
Run the elbow and silhouette diagnostics on the RFM dataset to select k. For a typical e-commerce dataset you might see the elbow around k=4 or k=5, which corresponds to intuitive segments: champions, loyal customers, at-risk customers, and churned customers.
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
results = []
for k in range(2, 9):
km = KMeans(n_clusters=k, n_init=10, random_state=42)
labels = km.fit_predict(X_scaled)
results.append({'k': k, 'inertia': km.inertia_,
'silhouette': silhouette_score(X_scaled, labels)})
import pandas as pd
print(pd.DataFrame(results))Fitting the Final Clustering Model
After selecting k, fit the final K-Means model and add the cluster labels back to the RFM DataFrame. This makes it easy to compute segment profiles and build customer-facing reports. The fit_predict method fits and returns labels in one call.
from sklearn.cluster import KMeans
k = 4
km = KMeans(n_clusters=k, n_init=20, random_state=42)
rfm['Segment'] = km.fit_predict(X_scaled)
print('Cluster sizes:')
print(rfm['Segment'].value_counts())Profiling Each Segment
Compute the mean of the original (untransformed) RFM values for each cluster. This gives interpretable business profiles: Champions have low recency, high frequency, high monetary; Churned have high recency, low frequency, low monetary. Naming segments based on their profiles makes reports actionable.
profile = rfm.groupby('Segment')[['Recency', 'Frequency', 'Monetary']].mean()
print(profile.round(1))
# Optional: label segments by profile
segment_names = {
0: 'Champions',
1: 'At-Risk',
2: 'Loyal',
3: 'Churned'
}
rfm['SegmentName'] = rfm['Segment'].map(segment_names)
print(rfm['SegmentName'].value_counts())Visualising Segments with Scatter Plots
Plot Frequency vs Monetary with colour coding for each segment. Add recency as point size to encode the third dimension visually. This chart is the deliverable that a marketing team can use to identify which customers to target for reactivation campaigns vs upselling campaigns.
import matplotlib.pyplot as plt
plt.figure(figsize=(8, 5))
for seg in rfm['Segment'].unique():
mask = rfm['Segment'] == seg
plt.scatter(rfm.loc[mask, 'Frequency'],
rfm.loc[mask, 'Monetary'],
s=rfm.loc[mask, 'Recency'] + 5,
label=f'Segment {seg}', alpha=0.5)
plt.xlabel('Frequency')
plt.ylabel('Monetary')
plt.legend()
plt.title('RFM Customer Segments')
plt.show()Assigning New Customers to Segments
After deploying the model, new customers get assigned by passing their scaled RFM vector through the same scaler and then calling km.predict. Never refit the scaler on new data — use the scaler fitted on the training RFM table to avoid shifting the feature space. The centroid positions remain fixed after fitting.
import numpy as np
# Simulate a new customer: recency=10, frequency=15, monetary=600
new_customer = np.array([[10, 15, 600]])
new_log = np.log1p(new_customer)
new_scaled = scaler.transform(new_log)
segment = km.predict(new_scaled)[0]
print('New customer segment:', segment)Business Insights and Next Steps
Clustering is a starting point, not an end. After profiling segments, the team should design targeted actions: send re-engagement emails to At-Risk customers, offer loyalty rewards to Champions, present upsell offers to Loyal customers. Track conversion rates per segment to measure the ROI of segmentation. Periodically retrain the model as customer behaviour evolves over time.
Quick Check
Test your understanding of customer segmentation with clustering from this lesson.
Lesson Recap
In this lesson you learned: RFM (Recency, Frequency, Monetary) features are the standard building blocks for customer segmentation, log transformation and StandardScaler make skewed RFM features suitable for K-Means, and segment profiling translates cluster numbers into actionable business labels like Champions and At-Risk. Next up we explore PCA — a technique for reducing high-dimensional data to its most informative components.
Preguntas frecuentes
¿La lección «Clustering para segmentación de clientes: ejemplo de principio a fin» es gratis?
Sí — el texto completo de «Clustering para segmentación de clientes: ejemplo de principio a fin» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de Machine Learning Academy, actualiza a CoddyKit PRO. El curso de Machine Learning Academy incluye 4 lecciones en total.
¿Qué aprenderé en «Clustering para segmentación de clientes: ejemplo de principio a fin»?
Preprocesará un conjunto de datos de comercio electrónico, agrupará a los clientes según su gasto y frecuencia, y analizará cada segmento para obtener información empresarial. Practicas Machine Learning Academy con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.
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No se requiere experiencia previa. Machine Learning Academy en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 4 de 4.
¿Cuánto tiempo toma la lección «Clustering para segmentación de clientes: ejemplo de principio a fin»?
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¿Puedo escribir y ejecutar código en esta lección de Machine Learning Academy?
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Todas las lecciones de este curso
- K-Means: centroides, asignación y actualización
- Elección de K: método del codo y puntuación de silueta
- DBSCAN: puntos núcleo, puntos frontera y ruido
- Clustering para segmentación de clientes: ejemplo de principio a fin