---
license: apache-2.0
task_categories:
- tabular-classification
- tabular-regression
tags:
- nigeria
- banking
- churn-prediction
- customer-analytics
- customer-360
- fintech
- customer-segmentation
language:
- en
size_categories:
- 1M<n<10M
---

# Nigerian Banking Customer 360

**Dataset Type**: Banking & Finance - Customer Analytics  
**Version**: 1.0  
**License**: Apache 2.0  
**Language**: English  
**Geography**: Nigeria

---

## Dataset Description

Synthetic Nigerian banking customer 360-degree profiles with churn prediction labels. Comprehensive view of customer demographics, product holdings, balances, transaction activity, and digital engagement.

**Use Cases**:
- Customer churn prediction
- Lifetime value (LTV) modeling
- Next-best-offer recommendations
- Customer segmentation
- Product cross-sell/up-sell
- Retention campaign targeting

---

## Dataset Statistics

- **Rows**: 2,000,000 customers (pilot: 10,000)
- **Columns**: 24
- **Snapshot Date**: 2024-12-31 (monthly snapshots possible)
- **Churn 30d Prevalence**: 5%
- **Churn 90d Prevalence**: 12%

---

## Schema

| Column | Type | Description |
|--------|------|-------------|
| `customer_id` | string | Unique customer identifier (CUS-########) |
| `snapshot_date` | date | Data snapshot date |
| `age` | int | Customer age (18-70) |
| `gender` | category | `male`, `female` |
| `monthly_income_ngn` | float | Monthly income in Naira |
| `state` | category | Nigerian state (37 states) |
| `kyc_tier` | category | `tier1`, `tier2`, `tier3` |
| `account_age_days` | int | Days since account opening |
| `products_count` | int | Number of products held (1-7) |
| `has_savings_account` | bool | Has savings account |
| `has_current_account` | bool | Has current account |
| `has_loan` | bool | Has active loan |
| `has_debit_card` | bool | Has debit card |
| `has_credit_card` | bool | Has credit card |
| `has_wallet` | bool | Has digital wallet |
| `has_investment` | bool | Has investment product |
| `total_balance_ngn` | float | Total balance across products |
| `transaction_count_30d` | int | Transaction count (last 30 days) |
| `transaction_volume_ngn_30d` | float | Transaction volume (last 30 days) |
| `days_since_last_transaction` | int | Days since last transaction |
| `dormant_flag` | bool | Dormant if no activity in 90+ days |
| `digital_engagement_score` | int | Digital engagement score (0-100) |
| `churn_30d` | bool | **LABEL**: True if churned in 30 days |
| `churn_90d` | bool | **LABEL**: True if churned in 90 days |

---

## Label Distribution

### Churn 30d
- **Positive**: 5%
- **Negative**: 95%

### Churn 90d
- **Positive**: 12%
- **Negative**: 88%

**Note**: `churn_90d` is a superset of `churn_30d`

---

## Churn Drivers

### High Risk Factors
- **Dormant** (90+ days no activity): 15x risk
- **Single Product**: 5x risk
- **Low Engagement** (<30 score): 6x risk
- **Low Balance** (<₦10k): 4x risk
- **Failed Transactions** (>5/month): 3x risk
- **No Direct Deposit**: 3.5x risk

### Protective Factors
- **Multi-Product** (3+): 0.2x risk
- **High Engagement** (>70 score): 0.3x risk
- **Recent Activity** (<7 days): 0.25x risk
- **High Balance** (>₦500k): 0.4x risk

---

## Data Distributions

### Demographics
| Metric | Value |
|--------|-------|
| **Avg Age** | 35 years |
| **Gender Split** | 52% M / 48% F |
| **Urban Rate** | 65% |

### Product Holdings
| Product | Prevalence | Avg Balance |
|---------|------------|-------------|
| Savings Account | 80% | ₦150,000 |
| Current Account | 35% | ₦300,000 |
| Debit Card | 40% | - |
| Digital Wallet | 30% | ₦50,000 |
| Loan | 20% | ₦500,000 |
| Investment | 12% | ₦1,000,000 |
| Credit Card | 8% | - |

**Average Products per Customer**: 2.0

### Balance Distribution
- **Distribution**: Lognormal
- **Median**: ₦50,000
- **Mean**: ₦160,000
- **Top 10%**: >₦800,000

### Transaction Activity (30 days)
| Metric | Value |
|--------|-------|
| **Avg Transactions** | 25 |
| **Zero Transactions** | 15% |
| **High Activity (50+)** | 10% |

### Digital Engagement Score
- **Distribution**: Normal (μ=55, σ=20)
- **Low (<30)**: 20%
- **Medium (30-70)**: 60%
- **High (>70)**: 20%

---

## Customer Segments

| Segment | Prevalence | Avg Balance | Products | Churn Rate |
|---------|------------|-------------|----------|------------|
| **Mass Market** | 60% | ₦50k | 1.5 | 8% |
| **Emerging Affluent** | 25% | ₦300k | 2.5 | 4% |
| **Affluent** | 10% | ₦2M | 3.5 | 2% |
| **Private Banking** | 5% | ₦10M | 5.0 | 1% |

---

## KYC Tiers

| Tier | Daily Limit | Prevalence | Churn Rate |
|------|-------------|------------|------------|
| **Tier 1** | ₦300k | 45% | 8% |
| **Tier 2** | ₦5M | 45% | 4% |
| **Tier 3** | Unlimited | 10% | 2% |

---

## Nigerian Context

### Geographic Distribution
**Top 5 States**:
1. Lagos - 25%
2. Abuja (FCT) - 12%
3. Rivers - 8%
4. Kano - 7%
5. Oyo - 5%

### Income Patterns
- **Median Income**: ₦120,000/month
- **Lagos Multiplier**: 1.5x national average
- **Rural Areas**: 0.6-0.7x national average

### Banking Behavior
✅ **Salary Detection**: 55% have direct deposit  
✅ **Multi-Banking**: 40% hold accounts at 2+ banks  
✅ **Mobile First**: 70% primarily use mobile app  
✅ **Dormancy**: 12.5% dormant (90+ days no activity)

### Seasonal Patterns
- **Detty December**: +50% balances in December
- **January Broke**: -30% balances in January
- **Mid-year Lull**: -10% activity in June-July

---

## Files

```
customer_360/
├── README.md
├── nigerian_customer_360_pilot.parquet (10k, 0.45 MB)
├── nigerian_customer_360.parquet (2M, ~160 MB) - Coming Soon
├── nigerian_customer_360.csv (2M, ~320 MB) - Coming Soon
└── customer_360_sample.csv (100 rows)
```

---

## Usage Example

```python
import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split

# Load data
df = pd.read_parquet('nigerian_customer_360.parquet')

# Feature engineering
df['balance_per_product'] = df['total_balance_ngn'] / df['products_count']
df['txn_per_day'] = df['transaction_count_30d'] / 30
df['avg_txn_size'] = df['transaction_volume_ngn_30d'] / df['transaction_count_30d']

# Churn prediction features
features = [
    'products_count', 'total_balance_ngn', 'transaction_count_30d',
    'days_since_last_transaction', 'digital_engagement_score',
    'dormant_flag', 'account_age_days', 'balance_per_product'
]

X = df[features].fillna(0)
y = df['churn_30d']

# Split and train
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
model.fit(X_train, y_train)

# Feature importance
importance_df = pd.DataFrame({
    'feature': features,
    'importance': model.feature_importances_
}).sort_values('importance', ascending=False)

print(importance_df)
```

---

## Retention Strategies

### High-Risk Segments
1. **Dormant Customers**: Re-engagement campaigns, special offers
2. **Single-Product**: Cross-sell campaigns
3. **Low Balance**: Savings incentives
4. **Low Engagement**: Digital onboarding support

### Recommended Actions
- **Churn Score >0.7**: Immediate intervention (personal call)
- **Churn Score 0.5-0.7**: Retention offer (fee waiver, bonus)
- **Churn Score 0.3-0.5**: Engagement campaign (push notifications)
- **Churn Score <0.3**: Monitor only

---

## Limitations

1. **Snapshot Data**: Single point-in-time, not time-series
2. **Simplified Engagement**: Real engagement includes many more touchpoints
3. **External Factors**: Macroeconomic conditions not included
4. **Competitive Actions**: Other banks' offers not modeled

---

## Citation

```bibtex
@dataset{nigerian_customer_360_2025,
  author = {Electric Sheep Africa},
  title = {Nigerian Banking Customer 360 Dataset},
  year = {2025},
  publisher = {Hugging Face}
}
```

---

**Last Updated**: 2025-10-19  
**Status**: ✅ Pilot Validated