Practitioner Series

304 | Cookieless Data & Technology Guide

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1 Cookieless Targeting Guide

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Cookieless Measurement Guide

Cookieless Optimizations Guide

Cookieless Data & Tech Guide

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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.

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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

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Cookieless & Identity Content in

Knowledge Center

Tools & References for Managing Cookie Deprecation

Identity Coalition

Teams Channel

Identity Coalition Resource

Center on Teams

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Overview

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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

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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

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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

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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.

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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

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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

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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)

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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

Email

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.

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2

3

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Data

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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

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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

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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.

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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

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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

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Identity

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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

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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

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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.

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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

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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.

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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

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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

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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

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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

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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

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CDP

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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.

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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 .

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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 .

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Spotlight: Examples of CDP Use Cases

Customer Acquisition Customer Retention Journey OrchestrationAwareness

Cross-channel Marketing:

• Display

• Social

• Email

• 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

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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 .

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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

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Clean Rooms

Flexible activation for use in

owned & paid channels giving

greater control

Access to walled garden

data & campaigns; activation

limited to one platform

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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

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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

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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

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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.

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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.

47

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

48

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

50

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 .

51

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

52

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

55

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

57

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 .

61

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

EMAIL

NAME

ADDRESS

COOKIES

DEVICE ID’s

IP ADDRESSES

1P, 2P, 3P Data

62

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

EMAIL

NAME

ADDRESS

COOKIES

DEVICE ID’s

IP ADDRESSES

1P, 2P, 3P Data