ai-marketing-campaign-optimizer 1.0.0

AI Marketing Campaign Optimizer - Multi-language toolkit for optimizing AI-powered marketing campaigns with content analysis, strategy frameworks, and automation utilities. Inspired by https://ai-cmo.net/

AI Marketing Campaign Optimizer - Python Implementation

This Python implementation was inspired by ai-cmo. It provides a comprehensive toolkit for analyzing marketing campaigns, evaluating content strategies, and calculating performance metrics with clean, type-annotated code.

Features

• Campaign Strategy Evaluation - Analyze campaign parameters and generate strategic insights
• Content Analysis - Evaluate marketing content for readability, SEO, and engagement potential
• AI Prompt Generation - Create optimized prompts for AI-powered content generation
• Key Phrase Extraction - Identify important phrases from marketing copy
• ROI Calculation - Calculate return on investment with detailed metrics
• Type Hints - Full type annotation support for better IDE integration
• Dataclasses - Modern Python data structures for clean code

Installation

pip install ai-marketing-campaign-optimizer

Or clone and install locally:

git clone https://github.com/user/ai-cmo-net
cd ai-cmo-net/python
pip install -e .

Usage

Running the Demo

python main.py

Using as a Library

from ai_marketing_campaign_optimizer import (
    create_campaign_strategy,
    analyze_marketing_content,
    generate_ai_prompt,
    calculate_campaign_roi
)

# Create a campaign strategy
strategy = create_campaign_strategy(
    campaign_name="Q1 Product Launch",
    target_audience="B2B SaaS Decision Makers",
    goals=["Lead Generation", "Brand Awareness"]
)

print(f"Target Audience: {strategy.target_audience}")
print(f"Primary Channels: {strategy.primary_channels}")

# Analyze content
metrics = analyze_marketing_content(markdown_content)
print(f"Readability Score: {metrics.readability_score}")
print(f"SEO Optimization: {metrics.seo_optimization}")

Example: Campaign Analysis

from ai_marketing_campaign_optimizer import create_campaign_strategy

strategy = create_campaign_strategy(
    campaign_name="Summer Sale Campaign",
    target_audience="E-commerce Shoppers",
    goals=["Sales", "Brand Awareness", "Customer Retention"]
)

print(f"Content Themes: {strategy.content_themes}")
print(f"Estimated Reach: {strategy.estimated_reach:,}")
print(f"Budget: ${strategy.budget_allocation:,.2f}")

Example: Content Optimization

from ai_marketing_campaign_optimizer import analyze_marketing_content

content = """
# Your Marketing Content Here

Analyze this content for marketing effectiveness.
"""

metrics = analyze_marketing_content(content)

print(f"Word Count: {metrics.word_count}")
print(f"Readability: {metrics.readability_score}/100")
print(f"SEO Score: {metrics.seo_optimization}/100")
print(f"Engagement Potential: {metrics.engagement_potential}/100")
print(f"Detected Tone: {metrics.tone_analysis}")
print(f"Content Type: {metrics.content_type}")

Example: AI Prompt Generation

from ai_marketing_campaign_optimizer import generate_ai_prompt

prompt = generate_ai_prompt(
    topic="AI Marketing Automation Tools",
    tone="Professional",
    keywords=["AI", "marketing", "automation", "efficiency"],
    target_length=500
)

print("Generated Prompt:")
print(f"  Format: {prompt['format']}")
print(f"  Constraints: {prompt['constraints']}")
print(f"  Outline: {prompt['suggested_outline']}")

Example: ROI Calculation

from ai_marketing_campaign_optimizer import calculate_campaign_roi

roi = calculate_campaign_roi(revenue=50000, costs=15000)

print(f"ROI: {roi['roi_percent']}%")
print(f"ROAS: {roi['roas_multiple']}x")
print(f"Profit Margin: {roi['profit_margin']}%")
print(f"Net Profit: ${roi['net_profit']:,.2f}")

API Reference

Classes

CampaignStrategy

Dataclass representing a complete marketing campaign strategy.

Attributes:

• campaign_name - Name of the campaign
• target_audience - Target audience description
• campaign_goals - List of campaign goals
• content_themes - Recommended content themes
• primary_channels - Optimal marketing channels
• estimated_reach - Estimated audience reach
• budget_allocation - Recommended budget

ContentMetrics

Dataclass containing content analysis metrics.

Attributes:

• word_count - Total word count
• sentence_count - Total sentence count
• readability_score - Flesch Reading Ease score (0-100)
• seo_optimization - SEO score (0-100)
• engagement_potential - Engagement potential score (0-100)
• keyword_density - Keyword density percentage
• tone_analysis - Detected content tone
• content_type - Detected content type

MarketingContentAnalyzer

Analyzes marketing content for effectiveness metrics.

Methods:

• analyze_content(markdown_content: str) -> ContentMetrics

MarketingPromptGenerator

Generates optimized prompts for AI content generation.

Methods:

• generate_prompt(topic, tone, keywords, target_length) -> Dict

KeyPhraseExtractor

Extracts and analyzes key phrases from content.

Methods:

• extract_phrases(content, min_frequency) -> Dict[str, int]
• get_top_phrases(content, top_n) -> Dict[str, int]

ROICalculator

Calculates ROI metrics for marketing campaigns.

Methods:

• calculate_roi(revenue, costs) -> Dict[str, float]
• compare_campaigns(campaigns) -> List[Dict]

Functions

• create_campaign_strategy(campaign_name, target_audience, goals) -> CampaignStrategy
• analyze_marketing_content(markdown_content) -> ContentMetrics
• generate_ai_prompt(topic, tone, keywords, target_length) -> Dict
• extract_marketing_key_phrases(content, min_frequency) -> Dict[str, int]
• calculate_campaign_roi(revenue, costs) -> Dict[str, float]

Development

Running Tests

pip install -e ".[dev]"
pytest

Code Formatting

black .
ruff check .

Links

• Source: ai-cmo
• Repository: https://github.com/user/ai-cmo-net
• PyPI: https://pypi.org/project/ai-marketing-campaign-optimizer/

License

MIT License - see LICENSE file for details