CRM-Analysis 0.1.0
A package for Customer Relationship Management (CRM) analytics
Requires Python: >=3.6


```markdown
# CRM_Analysis

CRM_Analysis is a Python package for Customer Relationship Management (CRM) analytics. It provides tools for analyzing customer data, segmenting customers, calculating customer lifetime value, and more.

## Installation

You can install CRM_Analysis using pip:

```
pip install CRM_Analysis
```

## Features

- Calculate Customer Lifetime Value
- Segment customers based on purchase behavior
- Calculate retention rates
- Identify top customers
- Generate personalized product recommendations
- Analyze customer behavior
- Identify cross-selling opportunities
- Perform behavioral segmentation

## Usage Example

Here's an example of how to use the CRM_Analysis package:

```python
import numpy as np
from datetime import datetime, timedelta
from CRM_Analysis import CRMAnalytics

# Generate sample data (in a real scenario, you'd load this from your database)
np.random.seed(42)
num_customers = 10000
num_transactions = 50000

customer_ids = np.random.randint(1, num_customers + 1, num_transactions)
purchase_amounts = np.random.uniform(10, 1000, num_transactions)
start_date = datetime(2023, 1, 1)
purchase_dates = [start_date + timedelta(days=int(x)) for x in np.random.uniform(0, 365, num_transactions)]
acquisition_costs = np.random.uniform(50, 200, num_transactions)
product_categories = np.random.choice(['Smartphones', 'Laptops', 'Accessories', 'Smart Home', 'Wearables'], num_transactions)
interaction_channels = np.random.choice(['Website', 'Mobile App', 'Phone', 'In-store'], num_transactions)

customer_data = np.column_stack((customer_ids, purchase_amounts, purchase_dates, 
                                 acquisition_costs, product_categories, interaction_channels))

# Create CRMAnalytics instance
crm = CRMAnalytics(customer_data)

# 1. Identify top customers
top_customers = crm.identify_top_customers(10)
print("Top 10 Customer IDs:", top_customers)

# 2. Calculate Customer Lifetime Value
clv = crm.calculate_customer_lifetime_value()
avg_clv = np.mean(list(clv.values()))
print(f"Average Customer Lifetime Value: ${avg_clv:.2f}")

# 3. Segment customers
segments = crm.segment_customers()
segment_counts = {i: list(segments.values()).count(i) for i in range(1, 4)}
for segment, count in segment_counts.items():
    print(f"Segment {segment}: {count} customers ({count/len(segments)*100:.2f}%)")

# 4. Analyze retention rate
retention_rate = crm.calculate_retention_rate(np.datetime64('2023-01-01'), np.datetime64('2023-12-31'))
print(f"Annual Retention Rate: {retention_rate:.2f}%")

# 5. Get personalized recommendations for a specific customer
customer_id = top_customers  # Let's use the top customer as an example
recommendations = crm.personalize_recommendations(customer_id)
print(f"Recommended categories for Customer {customer_id}: {recommendations}")

# 6. Analyze customer behavior
behavior = crm.analyze_customer_behavior(customer_id)
print(f"\nBehavior analysis for Customer {customer_id}:")
for key, value in behavior.items():
    print(f"{key}: {value}")

# 7. Identify cross-selling opportunities
cross_sell = crm.identify_cross_selling_opportunities(customer_id)
print(f"\nCross-selling opportunities for Customer {customer_id}: {cross_sell}")

# 8. Perform behavioral segmentation
behavioral_segments = crm.segment_by_behavior()
segment_counts = {i: list(behavioral_segments.values()).count(i) for i in range(3)}
print("\nBehavioral Segmentation:")
for segment, count in segment_counts.items():
    print(f"Segment {segment}: {count} customers ({count/len(behavioral_segments)*100:.2f}%)")
```

This example demonstrates how to use various methods of the CRMAnalytics class to analyze customer data and derive insights.

## Documentation

For detailed documentation on each method and its parameters, please refer to the docstrings in the source code.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.