Question 1

How NeuroRadar's MMM Model Works?

Accepted Answer

This guide shows how NeuroRadar isolates the impact of each marketing channel on revenue using statistical transformations, regularized regression, and automated model selection.

Step 1: Raw Spend
This is the actual marketing spend by channel and week. It's the input before any transformation or modeling takes place.

Step 2: Adstock (Carryover Effect)
Robyn uses adstock transformation to capture the lagged effects of media spend across time. The carryover effect is represented as:

Adstock_t = Spend_t + θ × Adstock_{t-1} (θ = 0.7)
Step 3: Saturation (Diminishing Returns)
(https://en.wikipedia.org/wiki/Diminishing_returns)
This step models diminishing returns of media, using a Hill function:
Response = x^α / (γ^α + x^α)
Step 4: Ridge Regression
(https://en.wikipedia.org/wiki/Ridge_regression)
Robyn estimates the beta coefficients using ridge regression (L2 regularization). Channels with low predictive value get coefficients closer to 0.
Step 5: Apply to All Channels
The above steps are repeated for each marketing variable: TV, OOH, Facebook, Search, Newsletter, etc.
Step 6: Hyperparameter Optimization

Robyn runs thousands of model configurations using Nevergrad to tune adstock, saturation, and regularization parameters. Evaluation is based on NRMSE, Decomp.RSSD, and optionally MAPE.
Step 7: Model Selection
Models on the Pareto front (https://en.wikipedia.org/wiki/Pareto_front)(best trade-offs) are selected and clustered. Each cluster selects a representative winner used in simulation and reporting.
Step 8: Budget Simulations
With the final model, NeuroRadar can simulate multiple scenarios: maximizing response for a given spend, or minimizing spend for a target efficiency (ROAS/CPA).

Question 2

Q: Can I run Attribution models (MTA) with NeuroRadar?

Accepted Answer

A: NeuroRadar primarily focuses on providing advanced Marketing Mix Modeling (MMM) capabilities using open-source tools and machine learning, specifically built on Meta’s Robyn framework. This allows businesses to make data-driven media decisions across both online and offline channels with transparency, automation, and predictive power.

However, while Multi-Touch Attribution (MTA) models, which track and assign credit to individual advertising touchpoints, are not a core feature of NeuroRadar, MTA can be suported if the necessary data is provided

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