Do's and dont's when using AudienceData
How to document the accuracy?
Why introduce segments with different affinities?
Using targeting with the right conditioning
How to access data AudienceData segments in Adform
How to find AudienceData in MediaMath DMP
How to find segments in BidTheatre
How to find segments in Display & Video 360
Methodology and precision
The distinction between campaign impression profile and impressions in target group
What is deterministic data?
What is probabilistic data?
Accessing targeted data with DFP Audience sync
AdForm publisher integration instructions
How to find data usage information in Google 360 for Publishers former DFP
How to report on AudienceProject segment usage in DFP
Inventory available for realtime targeting in DFP
Sending targeting key values to AdManager
How to create your first audience
How to create your first seed
Case 1: Selecting a customer file
Case 2: Selecting an Amazon S3 file
Case 3: Selecting survey data from UserReport
Creating a seed
What is AudienceHub?
The new generation of AudienceReport
Agencies: managing user access for connected accounts
FAQ: Disconnecting accounts
How to add new clients
How to connect my account to my client's or agency's account
How to disconnect accounts
How to manage access to my accounts
What is the 2-step verification and how does it work?
How is addressable TV measured?
What are the available addressable TV device types?
What is addressable TV?
What is the addressable TV measurement availability?
Getting Started with Pixels
How do URL-Parameters work?
How to add parameters to AudienceReport pixel
How to check if your pixel is firing?
How to create a pixel?
What is a CACHE-Buster and why do we need it?
What is a tracking pixel?
What is the purpose of a t-code?
Setting up Pixels
How to setup measurement in Adform buy-side (Adform flow)
Implementing pixels in Campaign Manager
Implementing pixels in Display & Video 360
The GDPR parameters
SSL - Compliance
Creating and Sharing reports
How to add and export tracking pixels to your reporting items
How to add custom report items
How to duplicate a report
How to export your report
How to share your report with your client
How to understand your report
How to understand your report - Dashboard
How to understand your report - Profile
How to understand your report - Reach
How to use an exported pixel
Recalculation of reports
Getting Started with Reports
The original AudienceReport
Activating Addressable TV measurement
Addressable TV measurement availability
Available Addressable TV device types
How Addressable TV is measured
How to get the Addressable TV measurement tool in AudienceReport
Impact on sample size and frequency
Reporting of Addressable TV campaign
Sharing Addressable TV measurement numbers with TechEdge
What is Addressable TV?
Adserver - Adform
Adserver - VideoPlaza
Double Click DCM Adserver
Extented Sizmek Asia-guide
How to implement creative wrapper in Ad Manager
Programmatic Publisher Ad Server - Adform PPAS
Setting-up video measurement in Google Ad Manager
Sizmek Ad Suite Tracking
Implementing AudienceReport tracking pixels in Webspectators
Brand Lift Studies
Brand Lift Video Studies
Is my cache-buster working?
What is a cache-buster?
Which cache buster shall I use for my ad server?
Why do we need a cache-buster?
Adding tracking points / pixels to your project
Applying filters to your data
Change your target group or report period
Creating your first project
How to merge projects
How your data will change when applying filters
Activating your Customer Segments 3.0
Available Custom Segments
Custom Segments 3.0
Custom Segments and Sample Size
Reach, Coverage and Segments Availability
What are Custom Segments?
Adding tracking points / pixels with event tracking to your project
Event tracking in various adservers
Implementing click trackers
In-view tracking of inlined content
Understanding Event Tracking
What is Event Tracking?
Connect your Facebook Business Manager account to AudienceReport
Connect your Google Ads account to AudienceReport
Connect your Google Display & Video 360 account to AudienceReport
How are data integrated?
How to create an Integrated Report
To-do list before creating an Integrated Report
Understanding your Integrated Report
What is an Integrated Report?
Adform integration set-up
Automatic tracking of DFP campaigns
Google Campaign Manager Integration
Integrate AudienceReport and Techedge AxM (AdvantEdge Cross Media)
How Transparency is measured
How Viewability is measured
How the Overall Quality Score is calculated
Viewability tracking using AudienceReport measurement script
What is Quality?
What is a good Quality score?
What is a hidden referrer or a generic referrer?
What is the difference between no referrer and other referrers (in the tracking point violations table)?
When is a tracking point considered to be violating Geo Compliance/Transparency/Viewability?
Why can’t I drill down on some countries to see in which regions my impressions are loaded?
Why is my overall score not that bad when almost all my impressions are of low quality?
Why is there a discrepancy between the impression count in the Quality tab and the rest of the report while my campaign is running?
Will my viewability score of 0.0 affect the overall Quality score if I didn’t implement in-view tracking?
Customized PDF reports
Deeper Insights with Campaign Audience Measurement
Exporting your report
How to search for your project
Introducing the common affinity profile
Managing your projects with labels
Tired of clicks and imps?
Understanding your project
Can I change the phone number I chose for the two-step verification process?
Getting started with AudienceReport API
How do URL-parameters work?
How often will I be asked to log in through the two-step verification process?
How to track traffic by device type
If you accidentally delete pixels from your project
The procedure to enable the two-step verification
What if I lose my phone and cannot access my account?
Upgrade to the new generation of AudienceReport
Installing UserReport and setting up your media sections
Defining your website in the media section
General Account Information
Installing UserReport on your website or app
Reach and Coverage of Custom Segments
Target Audience verified by Kits
The technology behind Kits
What are Kits?
Working with Kits
The feedback widget
Activate the Feedback widget
Adding context information to ideas and bugs
Customize Feedback widget buttons and links
Customize color, text and position of the Feedback widget
Disabling the Feedback widget on specific devices
Get direct link to the Feedback widget or manually display it
How to activate your Feedback widget
How to change the status of an idea or add a comment
How to disable the "report a bug" feature
Is the Feedback Forum available in my language?
Pre-populating username and email
What is the Feedback widget?
The feedback report
The survey widget
Activate Email Collect
Activate Net Promoter Score ®
Activate the Survey widget and create your questions
Chained questions and how they work
Controlling invitation frequency when using UserReport script and Google Tag Manager
How many questions can be added?
How many surveys answers do I need?
How to add questions to your survey
How to customise you survey widget
How to deactivate and delete your survey questions
How to show or hide results from users
Is UserReport 100% free?
Is the UserReport survey widget available in my language?
Managing invitation rules through Ad-server
Preview your survey
Respecting the user
User invitation to UserReport survey and the quarantine system
Who pays for the prizes in the survey?
Will UserReport slow down my website? Is it affected by popup blockers?
The Google Analytics Integration
The survey reports
Accessing Newsletter signups using API
- All Categories
- The new generation of AudienceReport
- Measurement Methodology
- Open web measurement
Open web measurement
Updated by Corina Alonso
Open web measurement: Audience measurement of open web campaigns
AudienceProject uses advanced technology and robust methodology to measure campaigns running on the open web. By using deep learning and probabilistic modelling in combination with first-party panel data, AudienceProject provides audience reach and frequency insights for open web campaigns in a cookieless and privacy-safe manner.
Today’s media industry is increasingly defined by walled gardens, different ID universes, fragmented media consumption and privacy. This challenges marketers’ ability to measure and analyse campaigns holistically via cookie-based measurement.
AudienceProject overcomes this challenge by using three different enablers; direct clean room integrations, advanced technology and robust methodology.
When measuring campaigns on walled gardens like Meta and YouTube, we utilise our direct clean room integrations with those platforms. However, for open web campaigns, such integrations are not possible, and thus, we use other means to measure these. More specifically, we use deep learning and probabilistic modelling in combination with first-party panel data to provide audience reach and frequency insights for open web campaigns.
How it works
Our general measurement strategy is to use and combine all relevant data for the specific measurement purpose. In the case of open web, this means combining information from loglines with information derived from our measurement panel.
Raw log-level data delivers an abundance of potentially useful information - even when completely stripped of personally identifiable information (PII). Based on raw log-level data such as geographical locations, timestamps, user agent data, etc., we initially use our deep learning model to estimate how many devices are reached by a campaign.
To ensure that we deliver precise device reach estimates, our deep learning model is trained with high-quality and high-volume data from our historical campaign measurements and online behavioural targeting. At the same time, it is constantly validated by taking a critical approach to the outcome of the model, ensuring that it is continuously fine-tuned by incorporating new learnings.
When we have calculated the device reach, we combine this with our knowledge about the“device universe” and geographical information as well as demographic information. derived from our consented and representative measurement panel. This allows us to estimate how many humans are reached by a campaign and thus provides insights into audience reach and frequency. The profiled reach is then modelled based on our consented and representative measurement panel.
As the input of the measurement is aggregated data, the measurement is based on groups, not individuals, ensuring that it is done in a fully privacy-safe manner.
Measurement of audience reach and frequency for open web campaigns:
What is deep learning?
Deep learning is a framework of machine learning methods that utilises flexible mathematical models that contain a very large number of free parameters, enabling modelling of a wide range of phenomena. Systems utilising deep learning have successfully solved many challenges, including image classification, fraud detection, language translation, and even art creation (the latter to a debatable degree of success).
This can sound a bit abstract, but let’s try to simplify it with an example from ‘the real life’.
Imagine you are given the task of telling how many people are visiting a public park on a given day. The only limitation is that you are not allowed to count the number of people. This means that you need to look for other traces that can help you estimate how many people are visiting the park.
For example, you can look for how many to-go coffee cups are in the bins, how many footmarks are on the trails and how many marks are in the grass after picnic blankets. Using these traces will help you get an idea about the number of people visiting the park, and with the presence of historical data, you can even validate and readjust the estimation. Maybe you learn that only half of the park visitors usually bring to-go coffee or that people normally do picnics when the weather allows it.
In the same way as you are looking for traces and using your prior knowledge to estimate the number of people, AudienceProject uses deep learning to estimate the number of devices reached by open web campaigns.
What is probabilistic modelling?
Probabilistic modelling is a type of statistical modelling that incorporates probability distributions to account for uncertainty when drawing conclusions from the data. Combining prior knowledge and stochastic variables, probabilistic modelling can predict the most likely outcome of an indeterministic process. This indeterminism can stem from truly random or unpredictable events or real-world facts that are just unknown in the model.
What is statistical Extrapolation?
In many instances, we do not have access to a direct measurement of the objects we want to know about. Instead, we look at patterns, connections and correlations for similar objects we can observe and extrapolate to the objects we want to know about using the assumption that the patterns we have seen for the objects we extrapolate from are the same as the objects we extrapolate to.
Traditional cookie-based measurement vs new AudienceProject measurement
To validate our measurement methodology, we have compared campaign measurement results from hundreds of campaigns across our markets where our traditional cookie-based measurement methodology and our new measurement methodology based on deep learning and probabilistic modelling have been used.
The graphs below show the reach build-up over time for two of the campaigns where we have made the comparison. The grey line represents the reach build-up for a campaign measured with our traditional cookie-based measurement methodology, whereas the blue line represents the same campaign but measured with our new measurement methodology.
The graphs show that the reach calculations based on our new measurement methodology are very close to those made with our traditional cookie-based measurement methodology.
Reporting and benefits
- Metrics: Reach in target group, frequency, hitrate, on-target percentage and events in target group
- Reach building event types: Impressions, viewable impressions, clicks and video quartiles
- Demographics: All demographics (gender, age, income, employment, education, household size, children in household)
- Reporting period: 84 days
- Get independent measurement of open web campaigns
- Understand the total, de-duplicated reach generated by campaigns on open web and other channels
- Understand the incremental reach generated by open web campaigns