marketing-analytics-toolkit 0.1.3

Advanced marketing analytics toolkit for customer segmentation and analysis

Marketing Analytics Toolkit

The Marketing Analytics Toolkit is a comprehensive Python library designed for marketing professionals and data scientists. This toolkit provides advanced marketing analytics tools for customer segmentation, marketing mix optimization, and customer lifecycle analysis.

🚀 Features

🎯 Customer Segmentation

• Multiple Segmentation Methods: K-means, Gaussian Mixture, DBSCAN, Hierarchical Clustering.
• Automatic Feature Selection: Automatically selects the best features for segmentation.
• Segment Profiling: Analyzes the characteristics of each segment.
• Segment Transition Analysis: Calculates transition probabilities between segments.

📊 Marketing Mix Optimization

• Dynamic Pricing: Predicts optimal price points.
• Elasticity Analysis: Analyzes the impact of price changes on demand.
• Competition-Based Pricing: Considers competitor prices in pricing strategies.

🔄 Customer Lifecycle Analysis

• RNN-Based Behavior Modeling: Predicts customer behaviors using recurrent neural networks.
• Survival Analysis: Estimates customer lifetime and churn probabilities.
• CLV Prediction: Calculates Customer Lifetime Value.
• Churn Prediction: Estimates the likelihood of customer churn.

📈 Channel Attribution Modeling

• Markov Chain Attribution: Analyzes the effects of different channels.
• Shapley Value Attribution: Calculates contributions of each channel.
• Data-Driven Attribution: Machine learning-based attribution methods.

🛠️ Data Preprocessing Utilities

• Advanced Data Validation: Comprehensive data validation checks.
• Outlier Detection: Identifies and handles outliers in datasets.
• Feature Engineering: Includes methods for feature selection and encoding.

📊 Visualization Tools

The library provides various visualization tools to help interpret the results of analyses and models.

🛠️ Installation

Install via pip:

pip install marketing-analytics-toolkit

Developer Installation:

git clone https://github.com/anilcogalan/advanced_marketing_tool.git
cd advanced_marketing_tool
pip install -e .

📖 Quick Start

Customer Segmentation Example

from marketing_analytics.models import AdvancedSegmentationModel
import pandas as pd

# Load data
data = pd.read_csv('customer_data.csv')

# Initialize model
model = AdvancedSegmentationModel(
    method='kmeans',
    n_segments=3,
    feature_selection=True
)

# Fit and analyze
segments = model.fit_predict(data)
profiles = model.get_segment_profiles(data)
recommendations = model.get_segment_recommendations(0, data)

print("Segment Profiles:", profiles)
print("Recommendations:", recommendations)

Pricing Example

from marketing_analytics.models import AdvancedPricingModel
import pandas as pd

# Load features
features = pd.read_csv('pricing_data.csv')

# Initialize model
pricing_model = AdvancedPricingModel(method='ml')

# Fit the model
pricing_model.fit(features, features['demand'])

# Predict optimal prices
optimal_prices = pricing_model.predict_optimal_price(features)
print("Optimal Prices:", optimal_prices)

📊 Visualization Tools

The library offers various visualization tools:

from marketing_analytics.visualization import (
    plot_customer_segments,
    plot_channel_performance,
    plot_customer_journey
)

# Visualize customer segments
plot_customer_segments(segments_data)

# Visualize channel performance
plot_channel_performance(channel_data)

📚 Detailed Documentation

For more detailed information, please refer to our documentation.

🤝 Contributing

To contribute to the project:

1. Fork this repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push your branch (git push origin feature/amazing-feature)
5. Create a Pull Request

📝 License

This project is licensed under the MIT License. See the LICENSE file for details.

📫 Contact

• Project Owner: Anil Cogalan
• Twitter: @anilcogalan
• LinkedIn: Anil Cogalan

🙏 Acknowledgments

Thanks to everyone who contributed to this project. For a complete list of contributors, see the CONTRIBUTORS.md file.

📌 Citation

If you use this project in your academic work, please cite it as follows:

@software{marketing_analytics_toolkit,
  author = {Anil Cogalan},
  title = {Marketing Analytics Toolkit},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/anilcogalan/advanced_marketing_tool}
}