Attribution Modeling for ETL is a comprehensive project designed to implement and analyze various marketing attribution models. These models help businesses evaluate the performance of different marketing channels in driving customer conversions. The project integrates with ETL processes to facilitate data-driven decision-making for marketing analytics.
- First-Touch Attribution: Assigns all credit to the first channel that interacted with the customer.
- Last-Touch Attribution: Credits the last channel before the conversion.
- Linear Attribution: Distributes credit equally across all touchpoints in the customer journey.
- U-Shaped Attribution: Emphasizes the first and last interactions while sharing remaining credit among middle touchpoints.
- Time Decay Attribution: Allocates more credit to touchpoints closer in time to the conversion.
This project includes a sample dataset (attribution_data.csv) that is publicly available from the internet. The dataset contains simulated marketing touchpoints, channels, and customer conversion data.
Ensure you have the following installed:
- Python (>=3.8)
- Pandas
- NumPy
- Jupyter Notebook (optional, for interactive exploration)
- Clone the repository:
git clone https://github.com/Bishal-RD/Attribution-Modeling-For-ETL.git
- Navigate to the project directory:
cd Attribution-Modeling-For-ETL- Explore Attribution Models Open the Jupyter Notebooks provided for each attribution model:
jupyter notebookContributing Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.
License This project is licensed under the MIT License. See the LICENSE file for more details.
Contact If you have any questions or suggestions, feel free to reach out:
Bishal Ramdam GitHub: Bishal-RD