Ollama Customer Segmentation: AI-Powered Marketing Campaign Targeting Guide

# Download and install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull the Llama 2 model for customer analysis
ollama pull llama2

# Pull the Code Llama model for data processing
ollama pull codellama

import pandas as pd
import json
import requests

# Load customer data from your CRM or database
def prepare_customer_data():
    """
    Prepare customer data for Ollama analysis
    Include behavioral, demographic, and transactional data
    """
    customer_data = {
        'customer_id': [1001, 1002, 1003, 1004, 1005],
        'age': [28, 45, 32, 67, 24],
        'purchase_history': [
            ['organic_food', 'yoga_mat', 'protein_powder'],
            ['power_tools', 'work_boots', 'safety_equipment'],
            ['baby_clothes', 'stroller', 'organic_formula'],
            ['garden_tools', 'bird_feeder', 'outdoor_chair'],
            ['gaming_laptop', 'mechanical_keyboard', 'energy_drinks']
        ],
        'email_engagement': [0.8, 0.3, 0.9, 0.6, 0.4],
        'website_behavior': [
            'health_wellness_pages',
            'tools_equipment_pages', 
            'family_parenting_pages',
            'home_garden_pages',
            'technology_gaming_pages'
        ]
    }
    
    return pd.DataFrame(customer_data)

# Convert data to text format for Ollama processing
def format_for_ollama(customer_df):
    """
    Convert customer data into natural language for AI analysis
    """
    formatted_customers = []
    
    for _, customer in customer_df.iterrows():
        customer_profile = f"""
        Customer {customer['customer_id']}:
        - Age: {customer['age']} years old
        - Recent purchases: {', '.join(customer['purchase_history'])}
        - Email engagement rate: {customer['email_engagement']}
        - Primary website interests: {customer['website_behavior']}
        """
        formatted_customers.append(customer_profile)
    
    return formatted_customers

def analyze_customer_segments(customer_profiles):
    """
    Use Ollama to analyze customer data and create segments
    """
    
    # Combine all customer profiles for analysis
    all_profiles = "\n".join(customer_profiles)
    
    # Create prompt for customer segmentation
    segmentation_prompt = f"""
    Analyze these customer profiles and create distinct segments based on:
    1. Purchase behavior patterns
    2. Engagement levels
    3. Life stage indicators
    4. Interest categories
    
    Customer Data:
    {all_profiles}
    
    Create 3-5 customer segments with:
    - Segment name
    - Key characteristics
    - Marketing message recommendations
    - Preferred communication channels
    
    Format as JSON for easy processing.
    """
    
    # Send request to local Ollama instance
    response = requests.post(
        'http://localhost:11434/api/generate',
        json={
            'model': 'llama2',
            'prompt': segmentation_prompt,
            'stream': False
        }
    )
    
    return response.json()['response']

# Execute customer segmentation analysis
customer_df = prepare_customer_data()
customer_profiles = format_for_ollama(customer_df)
segments = analyze_customer_segments(customer_profiles)

print("AI-Generated Customer Segments:")
print(segments)

def generate_targeted_campaigns(segment_data):
    """
    Generate specific marketing campaigns for each customer segment
    """
    
    campaign_prompt = f"""
    Based on these customer segments, create targeted marketing campaigns:
    
    {segment_data}
    
    For each segment, provide:
    1. Email subject lines (3 variations)
    2. Ad copy for social media (Facebook/Instagram)
    3. Product recommendations
    4. Optimal timing for outreach
    5. Key messaging themes
    
    Make messages specific and actionable.
    """
    
    response = requests.post(
        'http://localhost:11434/api/generate',
        json={
            'model': 'llama2',
            'prompt': campaign_prompt,
            'stream': False
        }
    )
    
    return response.json()['response']

# Generate campaigns for identified segments
campaign_ideas = generate_targeted_campaigns(segments)
print("Targeted Marketing Campaigns:")
print(campaign_ideas)

def create_customer_scoring_system():
    """
    Implement AI-powered customer scoring for marketing prioritization
    """
    
    scoring_prompt = """
    Create a customer scoring algorithm that evaluates:
    1. Purchase frequency and recency
    2. Average order value trends
    3. Email and social media engagement
    4. Website behavior patterns
    5. Customer service interactions
    
    Assign scores from 1-100 where:
    - 90-100: High-value, highly engaged customers
    - 70-89: Regular customers with growth potential  
    - 50-69: Occasional customers needing nurturing
    - 30-49: At-risk customers requiring retention efforts
    - 1-29: Low-engagement customers for minimal outreach
    
    Provide Python code for implementation.
    """
    
    response = requests.post(
        'http://localhost:11434/api/generate',
        json={
            'model': 'codellama',
            'prompt': scoring_prompt,
            'stream': False
        }
    )
    
    return response.json()['response']

# Get AI-generated scoring system
scoring_code = create_customer_scoring_system()
print("Customer Scoring System Code:")
print(scoring_code)

def analyze_campaign_performance(campaign_metrics):
    """
    Use Ollama to analyze marketing campaign results and suggest improvements
    """
    
    performance_data = f"""
    Campaign Performance Data:
    - Email open rates: {campaign_metrics['email_open_rate']}%
    - Click-through rates: {campaign_metrics['click_through_rate']}%  
    - Conversion rates: {campaign_metrics['conversion_rate']}%
    - Revenue per customer: ${campaign_metrics['revenue_per_customer']}
    - Customer acquisition cost: ${campaign_metrics['acquisition_cost']}
    - Segment engagement breakdown: {campaign_metrics['segment_performance']}
    """
    
    optimization_prompt = f"""
    Analyze this marketing campaign performance and provide specific recommendations:
    
    {performance_data}
    
    Identify:
    1. Underperforming segments and reasons
    2. Optimization opportunities for each metric
    3. A/B testing suggestions
    4. Budget reallocation recommendations
    5. Message refinement ideas
    
    Provide actionable steps with expected impact estimates.
    """
    
    response = requests.post(
        'http://localhost:11434/api/generate',
        json={
            'model': 'llama2',
            'prompt': optimization_prompt,
            'stream': False
        }
    )
    
    return response.json()['response']

# Example campaign metrics
sample_metrics = {
    'email_open_rate': 24.5,
    'click_through_rate': 3.2,
    'conversion_rate': 1.8,
    'revenue_per_customer': 85.40,
    'acquisition_cost': 23.50,
    'segment_performance': {
        'health_conscious': {'conversion': 2.8, 'revenue': 95.20},
        'professionals': {'conversion': 1.2, 'revenue': 67.80},
        'new_parents': {'conversion': 3.1, 'revenue': 112.50}
    }
}

# Get optimization recommendations
optimization_tips = analyze_campaign_performance(sample_metrics)
print("Campaign Optimization Recommendations:")
print(optimization_tips)

def predict_customer_lifetime_value():
    """
    Use Ollama to create CLV prediction models based on early indicators
    """
    
    clv_prompt = """
    Design a customer lifetime value prediction system using these early indicators:
    
    1. First purchase behavior (product type, amount, timing)
    2. Initial engagement metrics (email opens, website visits)
    3. Customer service interactions in first 30 days
    4. Social media follow/engagement patterns
    5. Payment method and billing preferences
    
    Create a scoring system that predicts:
    - 6-month revenue potential
    - 12-month revenue potential  
    - Churn probability
    - Upsell/cross-sell opportunities
    
    Provide implementation approach and key metrics to track.
    """
    
    response = requests.post(
        'http://localhost:11434/api/generate',
        json={
            'model': 'llama2',
            'prompt': clv_prompt,
            'stream': False
        }
    )
    
    return response.json()['response']

# Generate CLV prediction framework
clv_framework = predict_customer_lifetime_value()
print("Customer Lifetime Value Prediction Framework:")
print(clv_framework)

def identify_behavioral_triggers():
    """
    Find optimal moments for marketing communication based on customer behavior
    """
    
    trigger_prompt = """
    Identify behavioral triggers that indicate high-intent moments for marketing:
    
    Analyze patterns like:
    - Product browsing without purchase
    - Cart abandonment scenarios
    - Support ticket types and timing
    - Email engagement spikes
    - Social media interaction patterns
    - Mobile app usage changes
    
    For each trigger, specify:
    1. The behavioral signal
    2. Optimal response timing (immediate, 1 hour, 24 hours, etc.)
    3. Recommended message type
    4. Expected conversion lift
    5. Frequency limits to avoid annoyance
    
    Create automated workflow suggestions.
    """
    
    response = requests.post(
        'http://localhost:11434/api/generate',
        json={
            'model': 'llama2',
            'prompt': trigger_prompt,
            'stream': False
        }
    )
    
    return response.json()['response']

# Get behavioral trigger analysis
trigger_analysis = identify_behavioral_triggers()
print("Behavioral Trigger Marketing Framework:")
print(trigger_analysis)

def create_performance_dashboard():
    """
    Generate automated reports for Ollama-powered marketing campaigns
    """
    
    dashboard_code = """
    import matplotlib.pyplot as plt
    import seaborn as sns
    from datetime import datetime, timedelta
    
    def generate_segment_performance_report(segment_data):
        # Create visualization for segment performance
        fig, axes = plt.subplots(2, 2, figsize=(15, 10))
        
        # Conversion rates by segment
        segments = list(segment_data.keys())
        conversion_rates = [segment_data[s]['conversion_rate'] for s in segments]
        
        axes[0,0].bar(segments, conversion_rates, color='steelblue')
        axes[0,0].set_title('Conversion Rates by Customer Segment')
        axes[0,0].set_ylabel('Conversion Rate (%)')
        
        # Revenue per segment
        revenues = [segment_data[s]['revenue'] for s in segments]
        axes[0,1].pie(revenues, labels=segments, autopct='%1.1f%%')
        axes[0,1].set_title('Revenue Distribution by Segment')
        
        # Engagement trends over time
        # Add your time-series data here
        
        plt.tight_layout()
        plt.savefig('segment_performance_report.png', dpi=300, bbox_inches='tight')
        return 'segment_performance_report.png'
    
    # Usage example
    sample_data = {
        'Health Enthusiasts': {'conversion_rate': 4.2, 'revenue': 125000},
        'Tech Professionals': {'conversion_rate': 2.8, 'revenue': 89000},
        'New Parents': {'conversion_rate': 5.1, 'revenue': 156000},
        'Retirees': {'conversion_rate': 3.5, 'revenue': 94000}
    }
    
    report_file = generate_segment_performance_report(sample_data)
    print(f"Performance report saved as: {report_file}")
    """
    
    return dashboard_code

# Get dashboard implementation code
dashboard_implementation = create_performance_dashboard()
print("Performance Dashboard Code:")
print(dashboard_implementation)

# Example integration with Salesforce
def integrate_with_salesforce(segments):
    """
    Push AI-generated segments to Salesforce for campaign execution
    """
    import salesforce_api  # Your Salesforce connector
    
    for segment_name, customers in segments.items():
        # Create custom field for segment assignment
        segment_field = f"AI_Segment_{segment_name.replace(' ', '_')}"
        
        # Update customer records
        for customer_id in customers:
            salesforce_api.update_contact(
                customer_id, 
                {segment_field: True}
            )
    
    return "Salesforce integration completed"

# Example integration with HubSpot
def integrate_with_hubspot(segments):
    """
    Sync customer segments with HubSpot lists
    """
    import hubspot_api  # Your HubSpot connector
    
    for segment_name, customers in segments.items():
        # Create dynamic list for segment
        list_id = hubspot_api.create_smart_list(
            name=f"AI Segment: {segment_name}",
            criteria=customers
        )
        
        print(f"Created HubSpot list {list_id} for {segment_name}")
    
    return "HubSpot integration completed"