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Add tv intrecept to readme #661

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May 2, 2024
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Add tv intrecept to readme #661

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fee8e5d
add tv intrecept to readme
juanitorduz May 1, 2024
aced436
add to comparison table
juanitorduz May 2, 2024
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -59,6 +59,7 @@ Leverage our Bayesian MMM API to tailor your marketing strategies effectively. B
- **Custom Priors and Likelihoods**: Tailor your model to your specific business needs by including domain knowledge via prior distributions.
- **Adstock Transformation**: Optimize the carry-over effects in your marketing channels.
- **Saturation Effects**: Understand the diminishing returns in media investments.
- **Time-varying Intercept:** Capture time-varying baseline contributions in your model (using modern and efficient Gaussian processes approximation methods).
- **Visualization and Model Diagnostics**: Get a comprehensive view of your model's performance and insights.
- **Out-of-sample Predictions**: Forecast future marketing performance with credible intervals. Use this for simulations and scenario planning.
- **Budget Optimization**: Allocate your marketing spend efficiently across various channels for maximum ROI.
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3 changes: 2 additions & 1 deletion docs/source/guide/mmm/comparison.md
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Expand Up @@ -11,7 +11,8 @@ Given the popularity of the Media Mix Modelling (MMM) approach, there are many p
| Open source| ✅ | ✅ | ✅ | ✅ | ❌ |
| Model | 🏗️ Build | 🏗️ Build | 🏗️ Build | 🏗️ Build | 🛒 Buy |
| Budget optimizer | ✅ | ✅ | ✅ | ❌ | ✅ |
| Time-varying parameters | coming soon | ❌ | ❌ | ✅ | ✅ |
| Time-varying intercept | ✅ | ❌ | ❌ | ✅ | ✅ |
| Time-varying coefficients | coming soon | ❌ | ❌ | ✅ | ✅ |
| Custom priors | ✅ | ✅ | ❌ | ❌ | ✅ |
| Lift-test calibration | ✅ | ❌ | ✅ | ❌ | ✅ |
| Out of sample predictions | ✅ | ✅ | ❌ | ✅ | ✅ |
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