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Practitioner Series
304 | Cookieless Data & Technology Guide
2
2
3
1 Cookieless Targeting Guide
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304
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Cookieless Measurement Guide
Cookieless Optimizations Guide
Cookieless Data & Tech Guide
305
5 Cookieless Testing Guide
The Cookieless Future (Part IV)
Practitioner Series
This document is a continuation of an advanced
content series that will provide an overview of
expected platform changes and preventative
measures to prepare for cookie and third-party
identifier deprecation.
Who Should Read This?
Agency Strategy, Analytics, Data, Tech and Precision
teams to further understand the impact cookieless
has on their technology ecosystem and data
infrastructure. This guide will also help practitioners
to develop a holistic data, identity and technology
framework to win in the cookieless future.
3
1. Provide an overview of the data and tech
dependencies of marketers in a privacy
first world
2. Provide guidance on the impact of
cookie deprecation as it relates to data
and tech
3. Example assessments you can execute
to get started
4. Create a comprehensive list of data and
technology assets that need to be
changed, and recommendations on how
to change
5. Partner Solution highlights to help
kickstart your own strategy planning
The Goal of this Installment is to:
This Blueprint Covers:
Channels
• Data & Tech
Overviews:
• Data
• Identity
• CDP
• Clean Room
Best Practices
Table of Contents
Overview05
10
18
Assessments
Data
CDP35
43
54
Clean Rooms
Summary
24 Identity
5
Cookieless & Identity Content in
Knowledge Center
Tools & References for Managing Cookie Deprecation
Identity Coalition
Teams Channel
Identity Coalition Resource
Center on Teams
6
Overview
7
Brands Will Need to Adapt to Thrive In a Cookieless World
A CDP supports a modernized data
platform with both public and private
enterprise cloud technology, improved 1P
data hygiene, and ongoing data hydration
through data enrichment and AI.
Modernized Data Infrastructure
Brands will need a way to not only
identify, but organize and activate on
anonymous, pseudonymous and known
identities in the future state
The Identity Pyramid
With data being more aggregated, less
accessible, and more modeled, it will
be imperative for marketers to lean into
both enterprise and partner specific
clean room technology to support
measurement, analytics and insights .
3D Measurement
CDP
Getting Started: Focus on the Marketing Foundations
Identity CDP Clean Room
1. Determine which Walled Garden Clean
Rooms you will need based on your
partner portfolio
2. Start interviewing enterprise Clean
Rooms like Prospect, Neustar, InfoSum,
LiveRamp, DataFleets, etc.
3. Lean into any testing opportunities
1. Confirm when your DMP contract expires
2. Hold a CDP education session
3. Execute a subsequent CDP Workshop
with PM Consulting
4. Conduct a CDP RFI
1. Begin evaluating identity strategies and
partners
2. Build your own identity framework
3. Reach out to your PM Consulting team
for tactic specific testing frameworks for
contextual, authenticated and 1P
publisher coop testing
9
Getting Started: A More Media Driven Tactical Checklist
Measurement
❑ Conduct a Media Impact
Assessment: By market analysis
of media spend against browser
type, device type, targeting type
and Conversion Type
❑ Audit Data Assets and Marketing
Architecture
❑ Build a Blueprint for Privacy First
Consumer Engagement
❑ Strategize with Accessible 1P
data: Think through quick wins for
building out 1PD data strategy.
Activation
❑ Test both ID and non-ID based
solutions as they become
available
❑ Define the Role of Customer
Data Platform (CDP)
❑ Consider partner portfolio
diversification platforms to
ensure that we can control
frequency
❑ Prioritize valuable publishers &
relevant contexts - audience &
message relevance, reach
potential
❑ Conduct a Measurement Assessment:
Inventory all KPI’s that are dependent
on iDFA and 3PC
❑ Consider privacy preserving, cloud
enabled Advertiser owned
analytics infrastructure
(e.g. CDPs, clean rooms)
❑ Explore privacy preserving, cloud-
based Partner owned analytics
options, examples:
❑ Google ADH
❑ FB Advanced Analytics Limited Release
❑ Amazon Marketing Cloud Beta
C O N F I D E N T I A L : F O R P U B L I C I S M E D I A C L I E N T S & A G E N C I E S O N L Y .
Data
10
Assessments
How are Ecosystem Changes Affecting Data & Tech?
Changes in consumer privacy expectations, new data
legislation, and updated policies from the major tech
companies are leading a significant deprecation in the
data signals available to marketers.
These changes are having a particular impact on the
identifiers that currently power many advertising use
cases. Apple’s Identifier For Advertisers (IDFA) has
become opt in only as of April 2021, and 3rd Party
Cookies, the foundation cross-site tracking, are being
phased out across all browsers, culminating with
Google Chrome in 2022.
These changes will cause a fundamental shift in the
way that digital marketing works, with a significant
impact to both data and technology.
Signal Deprecation Data Impact
Technology Impact
Data sets that are built on 3rd party cookies or IDFAs will be most
severely impacted, with these audience sets diminishing in scale and
eventually becoming obsolete. This includes data categories such as web
and device based behavioral, pixel-based behavioral, data onboarded to
become 3rd party cookies and some marketer collected device IDs.
All technologies built on 3rd Party Cookies or mobile identifiers, as well as
those built on identification approaches such fingerprinting, will be
impacted to some degree. This includes technologies such as Data
Management Platforms as well as techniques such as Multi-Touch
Attribution
11
Which is Why its Important to Partner with a Solid Identity Solution(s)
An ID based identity solution is being proposed to support addressable, personalized and one-to-one targeting and suppression. This
technology provides consumers meaningful transparency and control over their data and choices, while also providing the brand with an
interoperable framework for execution.
12
ID BASEDDigitally Derived IDs
Control Tags
Event Pixels
Email Pixels
Mobile
NAME BASEDCustomers/CRM
Transactions
Reference Data
Third Party
& Partners
Engagement
A futureproofed identity solution requires
a combination of factors:
Established 1st Party Online Identifiers
• Not deleted or blocked by default on browsers
• Supports browsers which block 3rd party cookies
• Have a longer lifespan than 3rd party cookies
Online Identity Grounded At The Person Level
• Identity based on deterministic matching events conducted by a
person rather than an ID level
• Connected back to portable first party data
• Combines multiple identifiers (1st party cookies, publisher IDs, device
IDs, emails etc.) with online/offline purchase data
13
1st Party Data will form the basis of the new advertising ecosystem,
meaning a coherent and connected enterprise-wide strategy is a key data
and tech requirement. This may be advertiser 1PD -OR- Publisher 1PD
• Post-cookie deprecation, new identifiers will be required that are built on
transparency and interoperability, while also promoting consumer choice and
valued relationships.
• First-party data will become the core of this new ecosystem, both for
advertisers and publishers. As first-party data is based on actual consumer
interactions, both in real-time and over extended timeframes, it is the
foundation for consumer understanding.
• It allows advertisers to garner the insights, accuracy and control required to
recognize, assess and interact with consumers in a personalized and valuable
way.
• Developing a holistic 1st party data strategy, which spans across your data and
tech ecosystem, is a crucial step to adapt to ecosystem changes.
Identity Built on 1PD as the Currency
Source: Epsilon 3rd Party Cookie Deprecation Report, February 2021
62% of marketers
are prioritizing
1st party data
strategies
in 2021
14
The Unification & Harmonization of that Data Living in a Single Technology – a CDP
Data Unification can be performed in real time within modernized, cloud-based technology—a customer data
platform (CDP)
Key Features
• Customer 360 view: understand
all interactions with your business
• Identity Resolution: connect
different interactions and data
points to a single record.
• Enterprise Intelligence: include all
customer signals – marketing,
customer service, commerce etc.
• Outbound integration: make data
available to activation platforms
• Analytics: Ingest performance
data to enable measurement in a
privacy preserving environment
(e.g. Multiparty cleanroom)
15
The CDP is the Foundation for a Modernized Technology Stack
Website Data
CRM
Chat/Call Center
Ad Server
2PD
3PD
Survey Data
Consent Preferences
Consent Management Platform
Customer Data
Platform
1. Collect 2. Unify & Enrich 3. Orchestrate 4. Activate 5. Measure & Optimize
Identity
e.g., Tealium,
Salesforce
e.g. Liveramp,
Epsilon
DSP
Social
Search
e.g., DV360, TradeDesk
e.g., Facebook, Twitter
e.g., SA360, Bing Ads
e.g., Salesforce, Adobe
Clean Room e.g. DCM,
Flashtalking
Ad Server/DCO
Attribution/Modelling
Web Analytics
e.g., Neustar, Brand Tracking
e.g., ADH, Amazon
e.g., Google Analytics
API
The CDP Ties Together “Owned” Data to “Paid” Data through Clean Room Partnerships
Data FoundationThe foundation of current measurement enablement are rooted in cookie & Device IDs. New data infrastructure will
be necessary to build as we move into a cookieless future. First-party strategy, ID graphs, and clean rooms provide
advertisers ways to continue tracking behaviors at the individual level. The more granular or identifiable data
marketers can collect, the more ability to quantify, measure & optimize in a custom-centric way.
Mathematical ComputationMeasurement is hardly just about collecting the right data; analysts need to apply mathematical computation or
statistical modeling to quantify the impact. The methods marketers consider need to evolve along with the
infrastructure they build. To estimate uncounted conversions due to loss of data signals, modeling and probabilistic
approaches will become more important.
Enablement We can no longer track whatever we want and then figure out what to act upon later. Marketers need to prioritize their
measurement plans and focus on the metrics they can optimize against. This is a great opportunity for Marketers to
combat measurement paralysis – reporting too many metrics that yield too little action. This is where a clean room
comes in.
1
2
3
17
Data
18
Data Collection Strategy Overview
Crawl
Find simple ways to collect customer
contact information such as names,
email addresses and phone numbers
e.g. Lead Generation Ad Units,
Offer/Discount Forms
Walk
Develop interactive experiences that
improve the customer experience
while gathering information about
consumer preferences.
e.g. loyalty programs, mobile app
experiences, personal shopping
forms
Run
Build omnichannel experiences
around all product touchpoints,
including real world interaction
points.
e.g. product interactivity, experiential
events, enriched shopping
experiences
19
Value Exchange
Consumers are becoming more aware of the value of data, leading to an
increased expectation of an offer in return for information. The level of value
exchange depends on factors such as the type of information being collected.
Transparency
It’s important for brands to clearly communicate how they intend to use customer
data in order to build trusted relationships with consumers.
Privacy & Consent
Data privacy legislation has come into effect in many jurisdictions that brands
need to adhere to when managing data. This means that consumer data must be
stored securely, and appropriate consent obtained to allow the data to be used.
Considerations
20
Getting Started: Focus on the Foundation 1PD
Data Hygiene Data UnificationData Integration
Connect all sources of customer
data into a single platform. This
should include in-store and online
sales data, customer service
data, web browsing data,
advertising and marketing
automation platform data,
loyalty/CRM, and any legacy data
from across the business.
Clean data to remove any records
that are incomplete, corrupt or
redundant. Usually involves four
key steps: data validation,
merging of duplicates,
standardization and purging of
outed or incorrect data.
Pull the cleaned data together
into a single view of the customer
, which incorporates all of their
interaction with a brand. This
involves matching different 1st
party customer identifiers to
establish a golden customer
record.
Harmonization
of Consent
As regulations become more
sweeping, it will be important to
have a single view of consent for
customers. For example. Have
they opted into email, but not
identity, or iDFA, but not email.
21
Second-party data is defined as someone else’s first-party data that is sold to an advertiser for targeting or analytics.
Second-party data can only be access through a marketplaces or direct relationships between businesses
Lean into 2nd Party Data Marketplaces
2PD Marketplaces Fundamental Pillars
Governance –protect both parties
through consumer centric and ethical
sourcing of data. This is typically
provisioned via a clean room.
(e.g., Prospect)
Origin – Consumer centric and ethical
sourcing of data is vital to honor
consumer data trust.
Permission – Ensure shared privacy
& data standards, particularly around
compliance and consent management.
Second Party Data Marketplaces enable advertisers to connect with multiple
companies that are willing to monetize their internal datasets. The exchange
of data sets takes place in a privacy preserving environment which often
contains information from multiple sources such as publishers, retailers and
other brands.
Second-party data can be used alongside first-party data to expand reach,
inform targeting and drive insights. Advantages include:
• Transparency: understanding of where the data has come from and how it
has been collected
• Quality: obtained from direct customer relationships rather than
aggregation
• Recency: data often regularly refreshed
• Exclusivity: opportunity to be one of the few accessing the data
• Precision: can support with individual level insights and signals
22
When evaluating Third-Party Data Partners, the following areas are key considerations when deciding which
partners to work with:
Audit 3rd Party Data Providers Directly
Liability
Accuracy
Transparency
Consent
• Stay away from data exhaust partners
• Establish data governance guidelines
• Establish a data auditing process
• Maintain data security standards
• Leverage cloud to automate persistent data
• Integrate & automate data streams
• Assign a data steward across the enterprise
• Focus on ethical and transparent data sourcing and AI biases
• Select partner with most reach and highest quality match fidelity
and can process both known and unknown users (PII and non-PII)
Best Practices for Partner Evaluation
23
Identity
24
Post-Cookie: The New Identity Framework
The trade off with authenticated ID based solutions is accuracy versus scale, so advertisers need to be prepared to use
more than one solution.
Incre
ase
d A
dd
ressa
bilit
y
Incre
ase
d S
ca
le
2P Authenticated
1P & 2P Unauthenticated,
Modeled, AI.
Cohorts and Segments
Contextual
Mass (e.g., roadblocks, takeovers)
1P Authenticated
25
What ID’s Do Each Level of the Schema Use?
Uses logged in email, name, address
Uses logged in email, potentially IP or user agent if the inventory
is O&O and user is logged in.
Uses attributes associated to the identity or ID, to create
propensity models, or feature sets too inform targeting
Requires a minimum of 5,000 “users” in a FLOC (Google only) and uses an
anonymized ID controlled by the browser. Similar concepts are being adopted
across the ecosystem. Ad server IDs are a common ID used to build cohorts.
Non-audience based.
2P Authenticated
1P & 2P Unauthenticated,
Modeled, AI.
Cohorts and Segments
Contextual
Mass (e.g., roadblocks, takeovers)
1P Authenticated
Demo/geo based – broad audience parameters
26
Identity (Authenticated) Solutions Overview
Logged-In
Users
→ Users log in and this information is used to create a persistent ID
→ Coverage: Low (20-25%)
→ Accuracy: High (same as current 3pc system)
When Cookies are deprecated, the only way advertisers will be able to achieve addressable 1:1 targeting
is through authenticated ID based identity solutions.
27
Example below is from our friends at Epsilon
Identity in Action: 1P Authenticated Solution
EPSILON COMPLIMENTS
MARTECH & ADTECH PLATFORMS
FOR DELIVERING BETTER
PERSONALIZATION & TARGETING.
CORE ID200+MM Individuals
7000 Attributes
First Party Client
IntegrationsTransactions
Person/
Household Data
Segmentation PersonalizationJourney
Orchestration
Predictive
Models
Unified Customer
Data
Identity and
Data Sharing
Analytics &
Measurement
Experience
DeliveryMedia Activation
28
Publisher 1st Party Data can be a highly valuable commodity and is particularly valuable when it is made available
directly within buying platforms. A number of activation opportunities are under development.
Identity in Action: Publisher 1st Party Data Marketplace Solution
First-Party Segments
• SSPs work with Prebid to enable
SharedID technology, allowing publisher
1st party data to be targeted via Deal ID
• Segments are based off publisher-
classified inventory into interest-based
categories
• On the roadmap for exchanges to read
the signals of these first-party segments
directly in open auction
Publisher 1st Party Audience
Guarantees
• Adslot facilitates direct connectivity
between advertisers and publisher ad
servers
• Enables availability forecasting of
guaranteed audiences based on
publisher 1st party data. This essentially
opens up a publisher 2nd party data
marketplace.
• Cannot execute programmatically today.
Only upfront buys are available but DSP
integrations are on the roadmap.
Publisher Provided
Identifier (PPID)
• PPID is a way for publishers to sell their
first-party data through Google Ad
Manager by building custom audience
segments
• PPIDs can be activated through
traditional reservations or programmatic
guarantees; it is on the roadmap to
enable activation through open auction
• Available only through Google Ad
Manager or where publishers sell
through the Authorized Buyers SSP
Algorithms that run on an audience
data set to create highly descriptive
seed audiences, which can be used to
build lookalike models. Audiences are
built off all data signals and can be
defined by the advertiser e.g., high
lifetime value, likely converters. Relies
on platform models (e.g., Google,
Facebook) to build lookalikes, losing
some transparency & control.
Algorithms that assess impressions,
rather than users, to predict
performance. These models do not
require audience data to function.
Instead, they use other impression
attributes such as website, time of day,
location, and publisher 1st party data
to predict conversion performance.
Can be applied directly to impressions
does not rely on platform-owned
models for activation.
Similar to impression scoring models
but focus on assessing the context of
an impression. Advances in natural
language processing allow for deep
understanding of page content, which
is indexed and used to make
predictions on future performance.
Allows advertisers to drive decisions
based on keywords rather than
audience data.
29
Artificial Intelligence and Machine Learning approaches can be used to enhance predictive modelling and automatically
drive more precise marketing decisions. These custom algorithms can be applied across three different dimensions:
Identity in Action: AI Prospecting Models & Custom Algorithms
Audience Approach Impressions Approach Context Approach
30
Cohort solutions are a privacy preserving method of ad targeting.
• Rather than using third party cookies or other digital identifiers
to target individuals, these are based on collecting users into
groups people with similar interests or browsing habits -
‘cohorts.’
• Each cohort is of large enough scale to protect the privacy of
individuals and designed in a way that makes it impossible to
establish which users make up the cohort.
• There are a range of cohort solutions in development by some
of the largest players in the industry, such as Google. They are
largely built on first-party cookies and are executed within the
browser.
Cohort Solutions Overview
Cookie Based
Targeting
Cohort Based
Targeting
31
A collection of proposals, including solutions to support programmatic buying post third-party cookie deprecation.
Its mission is to create a web ecosystem that is privacy safe and respectful of its users
Identity in Action: Google Chrome Privacy Sandbox
FLoC‘Federated Learning of Cohorts’
A solution that aims to supplement interest-based targeting by
organizing users into clusters or ‘cohorts’ of users grouped based
on the sites they’ve visited in order to infer interests. Users are
placed into cohorts alongside other users that have similar
browsing behavior to theirs. A user can be placed in only one
cohort at a time.
Use Cases: Behavioral and Interest Based Audiences
DV360 Tactics Replaced: Affinity/Custom Affinity, Similar
Audiences, ADH Audiences, Custom Intent/In-Market
FLEDGE‘First Locally Executed Decision Over Groups Experiment’
A solution allowing buyers to partner with an ad server to store
data about a given campaign during the auction process This API
introduces the concept of a trusted third-party ad server
responsible, during the advertising auction process, for storing all
information about a campaign’s auctions and budgets in order to
enable remarketing.
Use Cases: Retargeting & Audience Creation
DV360 Tactics Replaced: Floodlight Audiences
32
Other companies are also developing potential solutions within the Privacy Sandbox, some of which look to improve
upon Google’s proposals while others are pointed at solving different use cases.
Identity in Action: Additional Cohort Solutions in Review
PARAKEET
‘Private and Anonymized Requests for Ads that Keep Efficacy and
Enhance Transparency’
A Microsoft proposal across the Edge browser that does exactly
as its name suggests. Modifications will be applied to the ad
request before it is sent out and interest-based and contextual
information will be used in the auction on the privacy-anonymized
ad request.
Use Cases: Context and Interest Based Ad Targeting
PELICAN
‘Private Learning and Interference for Causal Attribution’
A proposal that considers what a privacy-safe multi touch analytics
system could look like without using personal information.. The
initial focus is on understanding browser pathways, understanding
converting & non-converting sequences, and grouping similar
elements to inform learning.
Use Cases: Measurement & Analytics
33
You can also reach out to your PM Consulting team for tactic
specific testing frameworks for contextual, authenticated and
1P publisher coop testing
Prioritize Testing Based on Use Cases
34
CDP
35
The Customer Data Platform (CDP) aggregates customer data from 1st, 2nd, and 3rd
Party from internal and external sources creating a unified customer view and
enabling organizations to blend disparate data sets and gain “actionable insights”
and unlock “new use cases that drive immediate actions — and faster ROI”
The DMP ecosystem is evolving. DMPs were extremely reliant on 3P cookies and
have been building to the new norm since ITP was announced in 2017.
36
What is the difference between a CRM, DMP and CDP?
CRM CDP DMP
Data Sources CRM activity Any marketing activity All online activity
ID TypeKnown PII
e.g., email addressKnown PII + 1P cookie ID Anonymized IDs (3P cookie ID)
Real-Time Data? No Yes Limited
(on-site and media data)
Primarily Used For Customer data storage
• Unifies customer data
• Cross-channel personalization
• Real-time segmentation
• Audience extension
e.g., Lookalike modeling
• Digital media activation
• Media analytics
C O N F I D E N T I A L : F O R P U B L I C I S M E D I A C L I E N T S & A G E N C I E S O N L Y .
37
A customer data platform (CDP) is software that collects and unifies data from multiple sources to build a single view
of each customer.
What is a CDP?
Source: CDP Institute “Customer Data Platform Basics”
Connects to Most
Marketing Platforms
Real-Time
Platform
Ingests All
Sources of Data
Creates a
Persistent ID
e.g. CRM, Call Center, Loyalty,
Point of Sale, etc.
e.g. DMP, CRM, web analytics,
email service, etc.
C O N F I D E N T I A L : F O R P U B L I C I S M E D I A C L I E N T S & A G E N C I E S O N L Y .
38
Spotlight: Examples of CDP Use Cases
Customer Acquisition Customer Retention Journey OrchestrationAwareness
Cross-channel Marketing:
• Display
• Social
• Push
• Ads / Flyers
• Etc.
Behavioral Targeting
Chase Card Application
Call Center Integration (Resolution)
Lookalike Audiences
Customer Acquisition
Marketing / Programmatic Optimization
SEM / SEO
Targeted Media
Lead generation
Loyalty & Engagement
Re-targeting
ML and Predictive
Likelihood-to
purchase
Cleanroom and
Sandbox Activation
Personalization
Identity Resolution
Time-based targeting
User Experience
Product Bundles
Data Monetization
and Media Networks
Cross-channel
segmentation
Measurement
& Attribution
Audience
Suppression
Delivery Optimization
Yield Management
Unified Customer
Profile
Data Activation
Content/ Contextual
Recommendations
In-Airport
Trigger Communications:
Cart Abandonment
Behavioral Targeting
Chase Card Application
Marketing / Programmatic Optimization
SEM / SEO
Next Best Offer
Reach & Cross-sell
Call Center Integration (Resolution)
Travel Alerts
Chase Card Application
Loyalty & Engagement
Customer Preferences
Next Best Offer
Express Check-Ins
In-Airport Kiosk
Location Based Triggers
Churn & Retention
Demand Forecasting Destination-based Targeting
Inventory Management
Behavioral Targeting
Lookalike Audiences
Targeted Media
Marketing / Programmatic Optimization
SEM / SEO
Lead generation
Location Based Triggers
39
The end-to-end implementation process can be managed by Publicis Groupe
STEP 3
Strategic Set Up Strategic framework
reflective of key
objectives
STEP 4
Technical
Implementation Technical
configuration
of platform
STEP 5
Use-case RolloutBest practice
execution of
use-cases
STEP 2
Vendor SelectionMartech vendor RFI
process & selection
STEP 1
Readiness
AssessmentEvaluate project
needs, guidelines,
and risks
COPYRIGHT PUBLICIS GROUPE | CONFIDENTIAL
*Timeframe estimations are based on 1 brand and 1 market
6-8 weeks 8-10 weeks 12-16 weeks 12 weeks for POC
C O N F I D E N T I A L : T H I S D O C U M E N T I S F O R P U B L I C I S M E D I A A G E N C I E S A N D C L I E N T S O N L Y .
40
1. Confirm when your DMP
contract expires
2. Hold a CDP education
session
3. Execute a subsequent
CDP Workshop with PM
Consulting
4. Conduct a CDP RFI
CDP To Do’s
41
Clean Rooms
Flexible activation for use in
owned & paid channels giving
greater control
Access to walled garden
data & campaigns; activation
limited to one platform
42
A Clean Room is a data platform where brands and technology
partners can analyze anonymized marketing and advertising data
from multiple parties. Different from other data-sharing methods,
Clean Rooms include detailed advertising event level data, with
privacy-conscious restrictions on outputting user-level results.
It is estimated that there are 250 to 500 clean room deployments
active or in development today.
Spotlight: Clean Rooms
Sample Companies
Walled Garden Open Activation
According to Gartner, by 2023, 80% of advertisers with media
budgets of $1 billion or more will utilize data clean rooms.
Sample Companies
43
A privacy safe environment that gives you granular access at the
individual level to all prospect and customer data powering your
people-based marketing.
Spotlight: Open Activation Clean Room Basics
MEASUREMENT
Fully transparent down to the individual journey
Allows for experimentation
ANALYTICS AND INSIGHTS
Recognize intent and power better models
ACTIVATION
Portable marketing addressable ids for use in your preferred channel
ID-BASED CONSUMER PROFILES
Combine unique, bespoke data with yours
44
Ads Data Hub allows advertisers access to
complete campaign data. It provides user IDs
(cookie or user based), as well as device IDs.
ADH allows advertisers to join their data with
event level ad campaign data in a privacy-
conscious manner.
While ADH enables user-level analysis, any
reports need to come out in aggregate (with
segments >50). Users can use the AHD API to
run queries in a privacy safe environment and
output reports in their own BigQuery projects.
Spotlight: Walled Garden Clean Room Example: Google’s Ad Data Hub (ADH)
Note: Publicis Groupe has global ADH CoE to provide guidance on best practices
Graphic provided by Google
45
Spotlight: Open Activation Clean Room Example: Epsilon PeopleCloud Prospect
Person-based IdentityA comprehensive and historical view of your customers and prospects that aligns all their
first- and third-party behavior and marketing interactions to a person-based ID across
devices and groups. You can federate your Person-based ID across the ecosystem.
Granular Data, Partner accessA secure, privacy based analytical environment for advertisers and their partners to
interact with unique data. Granular data access at the individual ID level enables
modeling of customers and prospects at higher degrees of accuracy.
Unique AI/ML Signals
Leverage Epsilon’s library of pre-built unique and highly predictive digital intent signals or
customize your own brand-specific signals using contextual data to reach people when it
matters most.
Flexible ActivationSource of real time truth and intelligence, ensuring your marketing programs have the
most relevant consumer data to inform and drive strategies, all while leveraging your
existing partner activation channels.
Transparent Measurement
Purchase insights and attribution measured at the person level, providing brands
with a clear understanding of campaign effectiveness.
A privacy-safe clean room that gives you
granular access at the individual level to all
prospect and customer data powering your
people-based marketing.
46
Spotlight: Open Activation Clean Room Example: Neustar Fabrick
Graphic provided by Neustar
Neustar Fabrick is an identity-based
customer cloud that powers data
management, media activation, and
marketing analytics without a reliance on
cookies or mobile ad IDs.
Neustar is building a range of clean room
and data collaboration solutions to bring
brands, publishers, and data providers
closer together while maximizing control,
security, and performance.
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Spotlight: InfoSum: Bunker Technology
Graphic provided by InfoSum
Unlock the power of first-
party data with a data
onboarding solution that
does not require data
movement.
Unify insights from
multiple first-party
datasets to create a
single source of customer
truth to drive marketing.
Build trusted data
relationships by making
your rich customer data
available as second-party
data for strategic
partnerships.
Build data-driven
alliances underpinned by
trust & privacy, to power
consumer knowledge and
targeting.
Connect datasets and
analyze customer overlap
to unlock rich insights for
direct activation.
InfoSum is an infrastructure solution to build a privacy-first ecosystem of identity and data across advertisers,
platforms, and publishers.
InfoSum deploys a ‘Bunker’ to each advertiser that mirrors their first-party data. Bunkers can then be
connected to anyone else on the InfoSum infrastructure.
Data Onboarding Unified
Customer View
Second-Party
Audiences
Data Alliances Audience Insights
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LiveRamp Safe Haven enables global advertisers to extract
maximum value of owned data and partner data assets through
privacy-conscious data collaboration in a neutral and secure
environment that’s based on a foundation of governance and
permissions.
✓ Audience Building: Combine owned data with partner data to
create and amplify audiences for targeting and personalization
✓ Controlled Activation: Activate owned data with pre-approved
partners to personalize messaging to customers
✓ Measurement: Bring together event-level conversion and
exposure data to measure the impact of ad spend
(Closed-loop, MTA)
✓ Advanced Insights: Unify disparate data sources to create a
complete view of customers for modeling and analytics
Spotlight: Open Activation Clean Room Example: LiveRamp Safe Haven
Graphic provided by LiveRamp
Zero trust data architecture is based on analyzing data without moving or giving access to the information within a database.
Built on advanced mathematical & cryptographic techniques, the approach is currently in the very early stages of application
to advertising use cases.
Looking Further Ahead: Zero Trust Data Architecture
Homomorphic Encryption Multi-Party Computation Blockchain
A methodology that permits users to
perform computations on its encrypted data
without first decrypting it. This enables
analysis to be performed on private data
sets without compromising the security of
that data.
Cryptographic methods that enable multiple
parties to join and analyze their data sets
while keeping those data sets private. The
approach aims to protect the privacy of data
between the parties involved and so has
potential use case in clean rooms.
Technology that allows data sets to be
stored and validated in a decentralized and
distributed model, removing the need for a
single entity manage the records. Many
advertising use cases are being tested,
although none of these has managed to
scale to date.
Focus on model
portability and
federated learning
as a critical priority
Optimizing against
industry identifiers
like hashed emails
with improved
customer data
hygiene
Progressing past
hashed emails to
include more reliable
persistent digital and
physical identifiers
like physical
addresses
Evaluating partner
scale across new
offerings vendor
partners
Managing shifting
functionality as
partners migrate
audience activation
technology closer to
customer data
technology
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Spotlight: Clean Room Emerging Best Practices
C O N F I D E N T I A L : T H I S D O C U M E N T I S F O R P U B L I C I S M E D I A A G E N C I E S A N D C L I E N T S O N L Y .
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1. Determine which Walled
Garden Clean Rooms
you will need based on
your partner portfolio
2. Start interviewing
enterprise Clean Rooms
like Prospect, Neustar,
InfoSum, LiveRamp
DataFleets etc
3. Lean into any testing
opportunities
Clean Room To Do’s
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Summary
A Marketers Tech Stack: Current State
CRM Database DMP
Multi-channel
attribution platform
Website Analytics
CRM
Loyalty
POS / Transaction
eCommerce
Product Feeds
Call Center
Demographic Data
Name Based Inputs
AI and Machine Learning
E
One Way
3P Data
Exposure Logs
SDK / MAIDs
Site Analytics
eCommerce
CTV IDs
ID Based Inputs
Content Management
Platform
Tag Management
Cloud
Compute Preference
Manager
Marketing
Automation
SSP
DSP
MMP
Analytics
Platform
Supply
Chain
A Marketers Tech Stack: Future State
CDP
AI and Machine Learning
Consent Management Platform
Data Lakes
& APIs
Clean
Rooms
Multi-channel
attribution platform
Supply
Chain
CRM
Loyalty
POS / Transaction
eCommerce
Product Feeds
Call Center
Demographic Data
Name Based Inputs
3P Data
Exposure Logs
SDK / MAIDs
Site Analytics
eCommerce
CTV IDs
ID Based Inputs
SSP
DSP
MMP
Website Analytics
Content Management
Platform
Tag Management
Cloud
Compute Preference
Manager
Marketing
Automation
Analytics
Platform
Data Warehouse
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A cookieless strategy should
be informed by a brand’s
current capabilities, exiting
martech stack, digital maturity,
and 1st party data readiness.
No single strategy will solve all problems for a brand.
1st Party DataHow much customer data does your brand have & how well is it structured? Do you have gaps in your data
sets? Are there data hygiene considerations to resolve?
Customer Profile Capabilities Do you have siloed platform data related to your customers’ interactions with your brand, or are you on a
path to stitching this data together against a single customer profile? Are you engaging in progressive
profiling across brand touchpoints?
Sophistication of Segmentation PracticesHow sophisticated are your current segmentation practices? To what extent is segmentation done in-house
or outsourced? Are you leveraging modeled audiences or real-time capabilities?
Measurement MaturityHow developed is your measurement practice? Are you currently using specific models for attribution or
cross-channel measurement?
PersonalizationTo what extent are your brand messages or experiences today personalized? Do your customers typically
log-in? Do you have opportunities to improve the value-exchange with your customers?
Technology StackWhat technology is your brand currently leveraging? Are you actively utilizing a CDP, DMP, or on-site
personalization & targeting tools? what customer identifier dependencies do you have?
Getting Started
Identity CDP Clean Room
1. Determine which Walled Garden Clean
Rooms you will need based on your
partner portfolio
2. Start interviewing enterprise Clean
Rooms like Prospect, Neustar, InfoSum,
LiveRamp, DataFleets, etc.
3. Lean into any testing opportunities
1. Confirm when your DMP contract expires
2. Hold a CDP education session
3. Execute a subsequent CDP Workshop
with PM Consulting
4. Conduct a CDP RFI
1. Begin evaluating identity strategies and
partners
2. Build your own identity framework
3. Reach out to your PM Consulting team
for tactic specific testing frameworks for
contextual, authenticated and 1P
publisher coop testing
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Looking AheadThe Cookieless Testing Guide will provide a
framework to help teams develop a testing
approach as legacy identifiers deprecate.
2
3
1 Cookieless Targeting Guide
301
302
303
304
4
Cookieless Measurement Guide
Cookieless Optimizations Guide
Cookieless Data & Tech Guide
305
5 Cookieless Testing Guide
Cookieless Targeting
58
Appendix
60
Taking people paired with the right technology & a flexible process to quickly test and adapt fluid workflows, while
still building towards creating experiences informed by privacy-first approaches to targeting & measurement.
Reference Architecture
Clean Room ID Graph
Advertiser’s Data Infrastructure
1st P
arty &
2n
d P
arty
Da
ta S
trate
gy
Customer Data Platform
Data Infrastructure
MMM MTA
Test & Learn
Su
pp
lem
en
tal S
olu
tio
ns
Op
era
tion
al R
ep
ort /
KP
I
Unified Measurement
Methodologies
Activation & Optimization
C O N F I D E N T I A L : F O R P U B L I C I S M E D I A C L I E N T S & A G E N C I E S O N L Y .
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Client Database
Comprised of data that is sources across paid and
owned media properties that are all using the below identifiers
to build 1, 2, and 3P data sets.
How We Used to Identify a Consumer
NAME
ADDRESS
COOKIES
DEVICE ID’s
IP ADDRESSES
1P, 2P, 3P Data
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Client Database
Comprised of data that is sources across paid and
owned media properties that are all using the below identifiers
to build 1, 2, and 3P data sets.
How We Used to Identify a Consumer in the Future
NAME
ADDRESS
COOKIES
DEVICE ID’s
IP ADDRESSES
1P, 2P, 3P Data