Multi-Touch Attribution using Shapley and Markov chains
Project description
td-ml-mta
Treasure Data Multi-Touch Attribution (TD-ML-MTA) Package
Welcome to the TD-ML-MTA package, designed for Multi-Touch Attribution (MTA) within the Treasure Data environment. This Python package uses Shapley and Markov models to help businesses understand the impact of different marketing touchpoints on conversions or other key goals.
Overview
Multi-Touch Attribution (MTA) is a critical aspect of marketing analytics, enabling companies to analyze how various marketing touchpoints contribute to customer conversions or other desired outcomes. The TD-ML-MTA package integrates Shapley values and the Markov model to provide a comprehensive solution for MTA within Treasure Data.
With TD-ML-MTA, you can determine the individual and collective contributions of different touchpoints, considering the temporal aspect of customer journeys and leveraging the power of Treasure Data for data storage and analysis.
Installation
You can install the TD-ML-MTA package using pip:
pip install td-ml-mta
Thank you for choosing TD-ML-MTA for your Treasure Data Multi-Touch Attribution needs! 📊🚀
`Copyright © 2022 Treasure Data, Inc. (or its affiliates). All rights reserved`
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