Optimize Your Ad Spend Fast: No-Code Media Mix Modeling using ExpressMMM
With ExpressMMM, you can run a media mix model in just a few clicks — no coding or statistical expertise required. Just bring your data!
Yes, you read that correctly — running a Media/Marketing Mix Model (MMM) is now as simple as a few clicks. This isn’t another complex, enterprise-grade tool from Meta, Google, or Uber. Instead, I’ve developed a streamlined solution powered by PyMC, designed to make advanced marketing analytics accessible without unnecessary complications.
How It Works
Step 1: Go to the ExpressMMM Website
https://expressmmm.streamlit.app/
Simply upload your dataset to get started. For demonstration purposes, I’m using a sample dataset that I generated to showcase how the model works.
Step 2: Choose your variables
Select the variables you want to include in your model. In this example, I am selecting four marketing channels along with two control variables. To ensure accurate budget allocation, you must include at least one control variable.
Step 3: Sit Back and Analyze Your Model 😁
Once the model has finished training, you’ll receive a detailed output showcasing the results. Here’s what the final output should look like:
- Model Accuracy (R2)
2. Media and Baseline Contributions
Note: 1 year seasonality is automatically added, no need to add seasonality in the control variable
3. Return on Ad Spend (ROAS)
4. Spend vs Effect
I’ve also included a table for a structured view of the results — perfect for those who prefer raw data over visualizations (though I must say, the visualizations are crafted with great care!).
5. Direct Response Curves
6. Budget Allocation
You also have the option to download the trained .nc model file, allowing you to further analyze and refine your results directly in PyMC if needed.
And that’s it! Now you can run a Media Mix Model with ease. If you found this helpful or have any questions, feel free to connect with me on LinkedIn — I’d love to hear your thoughts!
