customer-segment-eng

Analyze uploaded bank customer data to segment and profile customers by assets, transactions, and behavior, outputting clusters, statistics, and visual charts.

Customer Segmentation Skill

Financial customer segmentation analysis: Stratify customers based on assets, transaction behaviors, activity levels, and other dimensions, outputting actionable segmentation results and visualizations.

Workflow

Step 1 — Data Loading and Cleaning

Read user-uploaded CSV or Excel files, automatically identifying column names.

Priority fields to retain:

• customer_id / 客户ID — Unique customer identifier
• age / 年龄
• gender / 性别
• balance / 资产余额
• txn_amount / 交易金额
• txn_count / 交易次数
• last_date / 最近交易日期
• product_count / 持有产品数
• branch / 网点

Missing value handling:

• Numeric: Fill with median
• Categorical: Fill with mode
• Columns with >30% missing: Delete and notify user

import pandas as pd

df = pd.read_csv(file_path)
df.columns = df.columns.str.strip().str.lower()

Step 2 — Feature Engineering

Build RFM + extended features:

Feature	Description
Recency	Days since last transaction (smaller = more active)
Frequency	Transaction frequency (number of transactions in specified period)
Monetary	Transaction amount (total amount in specified period)
Tenure	Customer duration (months)
Product_Depth	Number of products held
Age	Customer age

Data standardization: Use StandardScaler (Z-score) to normalize all numeric features.

Step 3 — Clustering Analysis

Use K-Means algorithm, automatically determine K value (Elbow Method, SSE inflection point).

from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(features)

# Elbow method to find optimal K
sse = {}
for k in range(2, 10):
    km = KMeans(n_clusters=k, random_state=42, n_init=10)
    km.fit(X_scaled)
    sse[k] = km.inertia_
optimal_k = min(sse, key=sse.get)  # Simply take k with minimum SSE

K=5 can also be fixed based on business needs (high/medium-high/medium/medium-low/low value customers).

Step 4 — Segment Profiling

Output core statistics for each cluster:

Cluster 0 (High-Value Customers): Avg. assets 850k, Avg. transaction frequency 28/month, Gender distribution 62% male
Cluster 1 (Potential Customers): Avg. assets 320k,明显 younger trend
...

Recommended label system (five categories):

• 🌟 High-Value Customers (VIP)
• ⬆️ Potential Customers
• 🟢 Stable Customers
• 🔄 Active Transaction Customers
• ⚠️ Dormant/Churn Warning Customers

Step 5 — Visualization

Generate the following charts (saved as PNG):

1. Customer Asset Distribution Histogram — Asset distribution comparison across levels
2. Radar Chart — Feature comparison across segments
3. Heatmap — Cluster feature mean matrix
4. Scatter Plot — Customer distribution with assets × transaction frequency as coordinates

import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')
plt.rcParams['font.sans-serif'] = ['WenQuanYi Micro Hei', 'SimHei']

fig, axes = plt.subplots(1, 2, figsize=(14, 5))
# Asset distribution
axes[0].hist([g['balance'] for _, g in df.groupby('cluster')], bins=30, label=[f'C{i}' for i in range(k)])
axes[0].set_title('Customer Balance Distribution by Cluster')
# Heatmap
import seaborn as sns
sns.heatmap(cluster_means.T, annot=True, fmt='.1f', ax=axes[1])
axes[1].set_title('Cluster Feature Heatmap')
plt.tight_layout()
plt.savefig(output_path, dpi=150)

Step 6 — Output Results

Output content:

1. Segmentation result table (including customer ID, cluster, segmentation label) → segmentation_results.csv
2. Cluster feature statistics → cluster_summary.csv
3. Visualization charts → segmentation_charts.png
4. Analysis summary (Markdown format) → segmentation_report.md

For detailed clustering and parameter documentation:

• RFM model explanation: Refer to references/rfm-guide.md
• Clustering parameter explanation: Refer to references/clustering-guide.md