Marketing Attribution using Markov Chains
Overview
Welcome to our Marketing Attribution project! As a marketer at Company A, understanding the contribution of each marketing channel to user conversions is crucial for optimizing our advertising strategy. This project employs a simple probabilistic model known as Markov Chains to analyze and attribute conversions to specific marketing channels.
Problem Statement
In the realm of marketing, customer journeys are comprised of multiple interactions across various channels or campaigns. The goal is to allocate conversion credit to each touchpoint in a user's journey, determining the contribution of each marketing channel to the end result of conversions. This understanding allows us to make informed decisions about resource allocation and investment in channels that significantly impact customer conversions.
What is Contribution?
Contribution, in this context, refers to the science of comprehending how our multi-channel marketing strategy influences the ultimate outcome of conversions. It involves assessing the relationship between different channels – if increasing spending on Facebook ads, for instance, affects conversions or search volume in Google ads.
Project Components
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Markov Chain Implementation
The core of this project is the implementation of a Markov Chain model within the Jupyter Notebook. This probabilistic model helps us analyze the transition probabilities between different marketing channels in a user's journey. By understanding the probability of a user moving from one channel to another, we can attribute conversions appropriately. -
Data Preparation
For the Markov Chain model to be effective, we need historical data on customer journeys. This involves collecting and organizing data on user interactions with our marketing channels. The data preparation phase ensures that we have the necessary information to build and train our model. -
Attribution Analysis
Once the Markov Chain model is applied to our data, we can conduct attribution analysis directly within the notebook. This step involves calculating the contribution of each marketing channel to conversions. The results provide insights into the effectiveness of each channel in driving user conversions.
Getting Started
To get started with this project, follow these steps:
- Clone the Repository:
Copy code git clone https://github.com/your-username/marketing-attribution.git
2. Install Dependencies:
Install the required dependencies listed in the notebook using:
python
Copy code
!pip install package-name
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Prepare Data:
Populate the data/ directory with your historical customer journey data in a suitable format. -
Open the Jupyter Notebook:
Open the marketing_attribution.ipynb notebook using Jupyter or any compatible platform. -
Run the Notebook Cells:
Execute the notebook cells sequentially to run the Markov Chain model on your data and review attribution results.
Contributing
We welcome contributions to enhance the functionality and efficiency of our Marketing Attribution project. Feel free to submit issues, pull requests, or suggestions directly on the notebook.
Happy optimizing your marketing strategy! 🚀