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# Understanding Incrementality Measurement

Polaris uses two techniques to measure incrementality:

1. Media mix modeling
2. Incrementality experiments

It is important to understand these techniques, at least at a basic level, so you have confidence in the incrementality measurement Polaris provides. The resources linked in this article will give you that necessary knowledge.

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# General

These resources contain information about incrementality measurement in general as well as the basics of media mix modeling and incrementality experiments.

## [Incrementality Explained](https://docs.google.com/presentation/d/e/2PACX-1vToERtwG1037pRg1yyS7zW6hl6ITyDcJGvmc9g5zFZFpAUAjAIw6X6FogB_BxSIBHpmv7_FjHG2XMB5/pub?start=true&loop=false&delayms=10000)

This short presentation provides basic information about incrementality, synthetically controlled incrementality experiments, and media mix modeling, in a simple, narrative format. It's the best place to start because the content is specifically focused on the precise techniques Polaris uses within the context of marketing incrementality measurement. Most of the other resources are a bit more general, especially those not produced by MetricWorks.

**Format**: Presentation
**Audience**: Everyone

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# Media Mix Modeling

These resources contain information about media mix modeling.

## [Analyst's Guide to MMM](https://facebookexperimental.github.io/Robyn/docs/analysts-guide-to-MMM)

This web page lays out, in simple terms, how media mix modeling works, and provides practical advice on preparing for and building models at each phase of the process. It is written by media mix modeling experts at Meta.

**Format**: HTML
**Audience**: Analysts

## [Building and Validating Media Mix Models](https://github.com/mecommerce/ThirdLove-Tech-Blog/raw/master/Media_Mix_Model/ThirdLove_MMM_Whitepaper.pdf)

This whitepaper lays out the structure of a basic media mix model including the underlying math, in a simplified way. While quite simplified, it can provide a good basis for understanding the technique, even though Polaris uses much more complex and powerful models.

**Format**: PDF
**Audience**: Analysts

## [Robyn](https://github.com/facebookexperimental/Robyn)

Meta's Robyn open source project can be thought of as a fairly simple reference implementation of media mix modeling. It is written in R and includes pretty thorough documentation. While Polaris uses significantly more powerful models, browsing through the Robyn code can provide a solid intuition of the core concepts.

**Format**: Open Source Project
**Audience**: Analysts

## [Challenges And Opportunities In Media Mix Modeling](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45998.pdf)

This academic paper provides the math for media mix modeling and then discusses specific challenges that are often encountered as well as potential solutions. Polaris incorporates many of the solutions offered in the paper as well as other novel features important for adapting media mix modeling to performance marketing.

**Format**: PDF
**Audience**: Data Science

## [Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/b20467a5c27b86c08cceed56fc72ceadb875184a.pdf)

This academic paper goes into detail on how to use a Bayesian technique to build and fit a media mix model, accounting for complexity like carryover (adstock) and shape (response curve) effects, all of which Polaris models automatically.

**Format**: PDF
**Audience**: Data Science

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# Incrementality Experiments

These resources contain information about incrementality experiments using the synthetic control method.

## [Inferring the Effect of an Event Using CausalImpact](https://www.youtube.com/watch?v=GTgZfCltMm8)

This 30-minute video presents an easy-to-understand walk-through of synthetically controlled experiments and how Google's open source CausalImpact library can be used to run them. Polaris uses a very similar technique to run its incrementality experiments.

**Format**: Video
**Audience**: Everyone

## [Inferring Causal Impact Using Bayesian Structural Time-Series Models](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/41854.pdf)

This academic paper details how Bayesian structural time series models can be used for causal inference and what to consider when building and applying such a model. This is quite similar to the technique used by Polaris for incrementality experiments.

**Format**: PDF
**Audience**: Data Science

## [Using Synthetic Controls:Feasibility, Data Requirements, and Methodological Aspects](https://economics.mit.edu/sites/default/files/publications/jel.20191450.pdf)

This academic paper details the synthetic control method and how it has been used for causal inference in a variety of fields, which types of studies it's most useful for, and how to best apply it. The synthetic control method is the foundation of Polaris's incrementality experiment methodology.

**Format**: PDF
**Audience**: Data Science