Question 1

Understanding Incrementality Measurement

Accepted Answer

Polaris uses two techniques to measure incrementality:

Media mix modeling
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.

General

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

Incrementality Explained

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

Media Mix Modeling

These resources contain information about media mix modeling.

Analyst's Guide to MMM

This web page lays out, in simple terms, how media mix modeling works, and provides practical adv

Question 2

Data Sharing

Accepted Answer

Overview

Broadly, there are two types of data imports:

Historical: 3 to 12 months of historical data, aggregated daily
Ongoing: only new data since the last sync, aggregated daily

There are also two categories of data.

Marketing: spend and impressions
App events: performance metrics

The marketing and app events datasets are quite different in terms of dimensions and metrics. It should be noted that each data category can be shared using a different method.

Considerations

There are some important points to consider when importing data for the purposes of media mix modeling and incrementality measurement.

Country Consistency

Our media mix models are partitioned by country so it is the most critical dimension. First, country values must be consistent across the marketing and app events datasets. For example, if Great Britain is represented as “GB” in the marketing dataset, but “UK” in the app events dataset, the model will not be able to allocate the "UK" app events to any campaigns since the spend and impressions are in a different country called "GB".

Country Allocation

It is important to scrutinize any data that cannot be allocated to a single country. This can apply to both the marketing and app events datasets, but is most common for marketing data. Some channels, in certain situations, will not be able to report spend or impressions at the

Question 3

Step-By-Step Onboarding Guide

Accepted Answer

Welcome to Polaris. By now you should have received your login credentials, so we want to take this opportunity to share the steps necessary for a successful partnership. Having worked with a large number of companies wanting to explore incrementality, we have identified some key stages to follow.

Stage 0: Understand the Techniques

This prerequisite stage makes it much easier to interpret and trust the incrementality data provided by Polaris by giving you a basic or even deep understanding of the techniques we use to produce it.

Understand Incrementality Measurement

Stage 1: Import and Validate Input Data

This stage is critical since without good input data, the media mix models (MMMs) will be inaccurate. The Getting Started help category contains articles that detail the data we need for input into the media mix models (MMMs), the format we need it in, and what else to expect during the initial stage of onboarding.

Question 4

Incrementality Analysis

Accepted Answer

Introduction to Incrementality Analysis

Country Level Metrics

When analyzing incrementality data in Polaris, it is important to understand that the hierarchical media mix model (MMM) used to produce the results ensures that metric totals always match up with the app event data that was input into the model at the app level and at the country level, minus some very minor rounding errors.

For example, if the app event data pulled from your MMP or uploaded as a custom import reflected a total of $1,000 in day 1 revenue across the entire app, Polaris would also show a total of about $1,000 in incremental day 1 revenue at the app level. Similarly, if the app event input reflected a total of $500 in day 1 revenue within the US, Polaris would also show a total of $500 in incremental day 1 revenue across the US. The purpose of the model is to allocate the $500 in the proper proportions to each channel, campaign, and source app (publisher app) running in the US based on the incrementality of each.

Basic and Incrementality Metrics

You’ll notice that Polaris provides 2 sets of metrics: basic and incrementality. Incrementality metrics are distinguished by the prefix “Inc.”. All other metrics are considered basic metrics. Basic metrics are normally configured to reflect last touch attribution data. Incrementality metrics reflect the output of the MMM. Incrementality metrics should be interpreted the same as their last touch counterparts in terms of business impact. Day 7 ROAS

Question 5

Incrementality Actionability

Accepted Answer

A New Reality: Multiple Sources of Truth

When translating incrementality measurement data into action, it is important to consider the nature of decision making with two sources of truth, incrementality and last touch, and the natures of those differing methodologies. For the most part, incrementality metrics can be used exactly how last touch metrics are used both strategically and operationally. Polaris can display incrementality and last touch metrics side by side to make this process much easier.

For example, if the day-to-day operational process dictates that campaigns with day 7 ROAS of less than 50% should have budgets reduced by 25%, incremental day 7 ROAS can be used in the same process as a drop-in replacement. This applies to most marketing processes. Simply put, ideally the marketing process remains unchanged while the input into the process is changed from last touch to incrementality.

Significant Optimization Swings

When large discrepancies exist between the two sources of truth, it's possible that the marketing process will dictate a very large swing in budget. Imagine that last touch data, the current input into the marketing process, claims that a channel is performing poorly, while incrementality data claims the opposite. Using incrementality data as a drop-in replacement could cause a significant increase in budget.

Use Experiments For Truth

Polaris [automatically recommends experiments](https://metricworks.freshdesk.com/a/solutions/articl

Question 6

Custom Export

Accepted Answer

In cases where sharing via data partner isn’t possible or preferable, data can also be custom exported from your internal systems and shared with us via a secured S3 bucket.

File Requirements

File Naming

Each file uploaded to S3 must include two components in the file name:

The category of data (marketing or events)
A .csv or .csv.gz (if GZIP compression is used) extension

Other than the requirements listed in this section, files can have any name and prefix (folder) structure. Once an import is complete, the file will automatically be renamed, but will still reside in the same S3 location. You should not rename the file back to the original extension since it will be imported again.

Single App

Single app files must also include the app's store ID (on Android, a package name like com.company.app and on iOS, a numeric ID like 123456789) in the file name.

For example, a marketing data file for an Android app with store ID “com.company.app” could be named marketing_com.company.app_2021_01.csv. An app events data file for an iOS app with store ID “123456789” could be named events_123456789_2021_02_01.csv.

Multiple Apps

Files that contain data for multiple apps must instead include the text __multi__ (leading and trailing double underscores) anywhere in the file name. Each app's store ID must be specified in each line under a column called "Store ID" within the CSV file's content. See [Custom Marketing Data](/en/article/custom

Featured & Frequently Read Articles

Browse All Categories