customer-segment-eng

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

Security Scan

The skill's files, instructions, and behavior are coherent with its stated purpose (customer segmentation) and do not request unrelated credentials, installations, or network access; however it will process sensitive financial/customer data so privacy and compliance checks are advised before use.

Purpose & Capability

Name/description match the provided SKILL.md and the included scripts. The Python script implements feature engineering, K-Means clustering, profiling, and chart generation — exactly what the skill says it will do. There are no unrelated environment variables, binaries, or config paths requested.

Instruction Scope

Instructions and code focus on reading user-uploaded CSV/Excel files, cleaning, feature engineering, clustering, and producing CSV/PNG/MD outputs. The skill will therefore handle sensitive bank/customer data (PII/financial). There are no instructions to read other system files, environment variables, or to transmit data externally, but the operator should confirm the runtime environment prevents unintended exfiltration.

Install Mechanism

No install spec is provided (instruction-only plus included script). Nothing is downloaded or extracted; the skill relies on standard Python libraries (pandas, scikit-learn, matplotlib, seaborn) which are expected for this task.

Credentials

The skill requests no credentials, env vars, or config paths. Its resource needs (CPU/memory when clustering large datasets) are proportional to the task. No broad or unrelated secrets are requested.

Persistence & Privilege

always is false and the skill does not request permanent system presence or attempt to modify other skills. It writes output files to a provided output directory (normal for this use case).

Assessment

This skill appears coherent and implements customer segmentation as advertised, but it processes sensitive financial and personally identifiable data. Before installing/running: (1) Verify where outputs are written and ensure they stay on trusted storage; (2) Confirm the runtime environment has no network egress or is monitored if data must remain private; (3) Review and test the script on synthetic or anonymized data first (there are minor logic issues such as a simplistic 'optimal_k' selection in the SKILL.md example); (4) Ensure compliance with data protection rules (masking/desensitization, retention policies, and non-discriminatory use); (5) Pin or vet Python package versions you install to avoid dependency supply-chain risks. If those controls are acceptable, the skill is internally consistent with its stated purpose.

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