---
title: How to Choose the Right Priors (Hyperparameters) in Morpheus Marketing Mix Modeling
description: Learn how to select the best priors for your Bayesian Marketing Mix Modeling in Morpheus. Understand what each hyperparameter controls and how to fine-tune them for better marketing insights.
---
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March 28, 2025
# How to Choose the Right Priors (Hyperparameters) in Morpheus Marketing Mix Modeling
## Learn how to select the best priors for your Bayesian Marketing Mix Modeling in Morpheus. Understand what each hyperparameter controls and how to fine-tune them for better marketing insights.
### Introduction
When building a Marketing Mix Model (MMM) in **Morpheus**, one of the most important steps is setting the **right priors**, also known as **hyperparameters**. These values define how your model behaves before it sees the actual data, and they can make a big difference—especially when your dataset is small or noisy.
In this article, we'll explain in simple terms:
- What priors are,
- What each Morpheus hyperparameter does,
- And how to adjust them based on your marketing knowledge.
### What Are Priors in Marketing Mix Modeling?
In Bayesian modeling (which Morpheus uses), **priors** express our expectations *before* analyzing the data. For example:
- If you believe TV usually has a small, delayed effect, you can use a prior that reflects this.
- If you’re unsure about a parameter, you can leave it “open” using a weak or neutral prior.
Think of priors as **guidelines** for the model. The more confident you are, the stronger your guideline should be.
### Default Morpheus Priors Explained
Here’s a breakdown of the default priors used by Morpheus and what they mean in plain language:
| Hyperparameter | What it controls | Default Prior | What it means for marketers |
| --- | --- | --- | --- |
| **intercept** | Base level of sales without media or trend | Normal(0.2, 2) | Expected baseline sales; broad range to let data decide |
| **saturation\_beta** | How fast channels reach saturation | HalfNormal | Ensures the curve bends (diminishing returns); can't be negative |
| **saturation\_lam** | How sharp the saturation curve is | Gamma(3, 1) | Moderate curve steepness; higher values = faster saturation |
| **gamma\_control** | Effect of control variables (e.g. price, season) | Laplace(2, 0.2) | Strong but sparse influence expected from control variables |
| **gamma\_fourier** | Strength of seasonality | Normal(0, 0.3) | Seasonality is centered around 0 but allowed moderate influence |
| **likelihood** | Noise or uncertainty in observed data | Normal | Assumes errors are normally distributed around predicted values |
| **adstock\_alpha** | Decay of media effect over time | Beta(2, 2) | Assumes carryover, but not too persistent or too short |
| **peak\_effect\_delay** | Delay until media impact peaks | Beta(1, 4) | Most impact is assumed to happen early, within a few days |
| **coef\_trend** | Long-term trend component | Normal(0, 1.5) | Allows for slow, steady trends (positive or negative) |
[Learn more about Morpheus hyperparameters.](https://support.dataslayer.ai/understanding-hyperparameters-in-morpheus-for-marketing-mix-modeling-mmm?hsLang=en)
### How to Adjust Priors Based on Your Marketing Knowledge
#### 1. **Use Your Campaign Experience**
If you’ve run campaigns before, you probably have some expectations:
- TV takes time to build impact → set a **higher delay** or **stronger adstock**.
- Facebook Ads quickly drive conversions but saturate → set **lower saturation lam**.
You can modify these priors in Morpheus to reflect your intuition, especially if the data sample is small.
#### 2. **Start Simple, Then Refine**
If you're not sure about a parameter:
- Use the **default** prior.
- Run your model and check the results.
- Then adjust one prior at a time to see how results change.
This approach is especially useful for **sensitivity analysis**, which helps confirm your model is stable and not overly dependent on any one assumption.
#### 3. **Avoid Making Priors Too Tight**
Don’t try to force the model to confirm your belief by setting extremely narrow priors. For example:
- A **Normal(0.2, 0.01)** prior on the intercept tells the model: “I’m 100% sure baseline sales are exactly 0.2” — which might be risky.
- Instead, use something like **Normal(0.2, 2)** to say: “I think it’s around 0.2, but I’m open to a wide range.”
#### 4. **Test What Happens When You Change Priors**
Good practice in MMM includes testing:
- What happens if you increase the variance in a prior?
- Does the media ROI change drastically?
- If yes, your model might be too sensitive and needs more data or better regularization.
### Common Pitfalls to Avoid
| Problem | What’s Happening | What to Do |
| --- | --- | --- |
| Model results change too much | Priors too strong or too weak | Try using more moderate priors |
| Unstable or weird results | Data can’t guide the model enough | Use more informative priors or simplify model |
| Channels show zero effect | Over-regularized priors (too tight) | Loosen the prior (increase standard deviation) |
| Model ignores control vars | Prior assumes they’re irrelevant | Use a more neutral or wider prior (e.g. Laplace(0,1)) |
### Auto-adjusting Hyperparameters with New Data
One of the most powerful upcoming features in Morpheus will be the ability for your model to **auto-adjust hyperparameters** as new data becomes available.
Currently, each time you retrain a model with new data, you manually review or reset your priors. But in the near future, Morpheus will support **incremental learning**, meaning:
- You train your model once with initial priors.
- As new data (e.g. monthly performance) is added, Morpheus will **update the priors based on what it has learned**.
- This creates a smarter, adaptive modeling process without starting from scratch.
**Why this matters:**
- Saves time for recurring MMM analyses.
- Builds better long-term insights as the model “learns” from ongoing campaigns.
- Reduces risk of overfitting on small datasets, since past knowledge is preserved and refined.
### Summary
Choosing priors in Morpheus doesn’t require deep statistical knowledge—but it does require marketing intuition. Ask yourself:
- What do I already know about how this channel works?
- Should the model have flexibility, or do I want to guide it more?
- Am I testing how my assumptions affect the outcome?
By understanding what each hyperparameter controls, and how it relates to marketing behavior, you can fine-tune your models for better, more reliable results.
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