What Comes After Third-Party Cookies?

June 1, 2022

A return to the foundations of first-party marketing.

When the recent announcements about third-party cookie and IDFA deprecation shook the advertising landscape, we couldn’t help but wonder: What is the solution?

We at DELVE don’t do well with rhetorical questions, so this article offers solutions aimed at real-world marketers: those in charge of web analytics and customer data, those buying online media and analyzing performance, and those accountable for advertising.

Our goal is to help marketers navigate through the “cookie-pocalypse” to minimize the impact on revenue and advertising ROAS—and to help mitigate anxiety (and prevent a few gray hairs) once third-party cookies and IDFA disappear.

In brief: Google and Apple are shaking up the world of digital marketing

The AdTech world is buzzing about Google’s announcement that Chrome would no longer support third-party cookies, as well as Apple’s announcement that users can choose to block IDFA identifiers by default in iOS14.

Solutions like Unified ID 2.0 attempt to replace third-party cookies with an alternative identifier that’s tied to hashed and encrypted email addresses. However, this path toward establishing an alternative consumer-tracking mechanism depends on a number of factors, such as consumers agreeing to share their data with the coalition of companies behind the Unified ID 2.0 movement.

Brands should make one “safe” choice: invest in their own first-party data

Unified ID 2.0, and other such frameworks that promise an alternative to third-party identifiers, may or may not work—and brands cannot wait to see how the industry reacts to changes in third-party tracking. Why? Third-party cookie deprecation may adversely and significantly impact their online revenue growth and advertising ROAS.

DELVE believes that your brand’s data should become its own source of competitive advantage. We suggest that you build an in-house, first-party data ecosystem within your own proprietary consumer data platform (CDP), created from inexpensive off-the-shelf components, that will (a) store your brand’s internal first-party data that has been (b) enriched with external first-party data from your strategic partners.

Just as buying IBM never got anyone fired, taking ownership and control of your first-party data is never the wrong call.

There are four critical shifts in how brands should think about first-party marketing

In light of third-party cookie and IDFA deprecation, DELVE urges brands to embrace four critical shifts in thinking that will help reprioritize their marketing and advertising investments in 2021 and beyond:

1. First-party data wins
First-party data is a strategic asset that is unique to each brand, and key to media efficiency and revenue growth. Third-party cookie deprecation will accelerate the urgency for brands to adopt a first-party data strategy (supported by second-party data enrichment) to future-proof their marketing.

2. MMM is better than attribution for cross-channel decisions
We need to end the debate about which data-driven attribution model is best because none are completely accurate, even with third-party cookies. Attribution produces biased results as it tends to measure middle- and lower-funnel performance or intent channels and misses upper-funnel awareness channels. We urge marketers to focus on full marketing funnel cause-and-effect analysis using a budget allocation model that includes MTA, MMM, incrementality, and new proxy ID tools for more reliable insights.

3. Identity resolution is a must
The walled gardens are growing, and brands have little choice but to integrate the competing stacks on their end through onboarding of a solution such as a marketing data lake. Third-party cookies had been addressing this need, but their deprecation is now forcing brands to upskill internal technical integration teams and lean on the power of second-party data

4. Insights enable competitive advantage
Data science and analytics capabilities are essential core competencies for optimizing marketing and media as they allow brands to extract and apply unique insights that their competitors can’t easily replicate. To effectively utilize data science in marketing and advertising, brands should take a deep breath and ask difficult questions—because the answers can translate into tangible results.

Unfortunately, four data myths still keep brands from embracing first-party marketing

Many brands have yet to implement the “four critical shifts” mentioned above because of four data myths that stand in direct opposition:

Third-party cookies are essential for media targeting and effective prospecting; without them, my brand will find it difficult to achieve marketing performance.

Single-channel, cookie-based attribution is good enough for understanding media performance— and making budget allocation decisions.

User-level audience matching across advertising platforms can be outsourced to solutions like LiveRamp, taking the responsibility of understanding cross-platform consumer behavior off my brand’s shoulders.

My brand’s Marketing Analytics or Data Science teams are not able to answer the questions I’m asking them (and even if they could, it’s unlikely those insights would impact my advertising or marketing plans).

Let’s explore each data myth in detail.


Third-party cookies are essential for media targeting and effective prospecting

Traditional targeting models that rely on third-party cookies were never targeting your best customer—and the audiences upon which those targeting models have been built are outdated.

Companies like BlueKai, Lotame, and Eyeota build solutions that claim to help marketers target their best audiences, ranging from “BMW lovers” to “people in the market for a new sofa” to “interested in energy drinks.” While such third-party audiences sound appealing and seem tailor-made for a brand’s needs, they are anything but.

For example, the audience of people looking for “a new sofa” may have been built because a consumer read an article about sofas 30 days ago—or as far in the past as 183 days ago (the lifespan of a cookie).

Moreover, the actual purpose behind the intent signals matters as well. Just because a prospect is reading about “BMW cars” doesn’t mean they intend to buy a BMW.

To achieve better efficiency and effectiveness in their marketing efforts, brands should:
• Rethink their targeting strategy, relying more on second-party data (from Google, Facebook, or Amazon) that is deterministic and has a strong and well-defined intent signal
• Create their own first-party intent signals, and use that data for targeting
• Stop relying on audiences generated by third-party providers.


Single-channel ROI reporting (from Facebook Ads Manager, Google Ads, etc.) cannot be used to make decisions about which platform is best and how media dollars should be allocated.

Single-platform-driven attribution is largely rooted in two false assumptions: one, that buyer journeys are (mostly) linear; and two, that buyer behavior can be reliably tracked at user, device, and channel level.

We all know that consumers do not buy in a linear path, such as single device, digital-only channels. They navigate myriad online and offline touchpoints leading up to purchase. One common example of an attribution gap is when last touch credit is given to organic search while missing the touchpoints that originally triggered the organic search visit—such as word of mouth, social media with no click, or organic social.

What is the unintended consequence of the attribution debate? Marketers have been conditioned to only invest in what they can measure.

Brands should focus on a full-funnel marketing approach (top, middle, and bottom), backed by incrementality measurement across all steps in the customer’s journey from impression to conversion.

Marketers should recognize the differences in measurement approaches for top-funnel awareness vs. mid- and bottom-funnel performance channels:
• Measuring performance in awareness channels typically includes implicit metrics, such as engagement that requires effort by the consumer (e.g., time spent on content consumption, or the number of brand interactions).
• Measuring performance in performance or intent channels uses explicit metrics, such as click behavior in search engines, CTR from ads, and on-site behavioral and conversion data.

We urge marketers to focus on full marketing funnel cause-and-effect analysis using a budget allocation model that includes MTA, MMM, incrementality, and new proxy ID tools for more reliable insights.


User-level audience matching across advertising platforms can be outsourced to solutions like LiveRamp

While LiveRamp (and its competitors such as Merkle and Conversant) have built great technologies to match consumers across different environments based on their IP addresses or social media profiles, these approaches overlook the complexity of today’s consumption patterns within a household.

Non-intent-based matching techniques are flawed due to different platforms being used by multiple people at different times. For example, while a Roku TV device and an iPhone can belong to the same person, that doesn’t mean advertising should be the same across these platforms. Why? These devices can be used by different family members, and what people want from watching a new episode of The Handmaid’s Tale on Hulu is very different from browsing for new shoes on their iPhone’s Amazon app.

Another flaw: solutions such as LiveRamp are incentivized to maximize audience matching. That “over-matching” can result in a 25% decrease in overall media efficiency because cookies that aren’t directly relevant could be appended to your original customer records.

We recommend that brands pursue a first-party data strategy, supported by investing in a CDP or home-grown marketing data lake that offers a 360° view of the consumer. We further encourage brands to pursue a first-party, data-driven marketing strategy that focuses both on customer retention and customer acquisition.

For customer acquisition, data will come from two sources: walled gardens and the open web.

• Intent data from walled garden sources can be viewed by its role in the buyer journey: awareness/interest (Facebook), consideration (Google), and purchase (Google, Amazon)
• Data from the open web will be owned by the identity consortiums

For customer retention, we recommend that consumer brands build a data lake to enrich their first-party, user-level data with traditional consumer data sources such as Experian or TransUnion for better segmentation.

• This enrichment data also acts as the key, or anchor, for further connection with second-party data.


My brand’s Marketing Analytics or Data Science teams are not able to answer the questions I’m asking them (and even if they could, it’s unlikely those insights would impact my advertising or marketing.

Data-driven insights can help you cut waste, and put advertising dollars or your team’s time toward what works, increasing ROAS and top-line revenue.

We frequently see Marketing Analytics, Data Science, or Data Engineering teams working separately from the Marketing team, leading to lack of collaboration or generation of insights that have no real impact on advertising ROAS or brand revenue.

More often than not, analysts cannot provide answers without first knowing the exact questions to ask. Moreover, an analyst or data scientist who has never bought media in Facebook Ads Manager, Google Ads, or Display & Video 360 might not understand these tools well enough to effectively formulate the right questions.

For brands to act faster and more efficiently than their competitors, and to achieve better results in 2021 and beyond, the increased complexity of marketing choices requires that analytics capabilities directly support marketing and advertising decisions.

At DELVE, we have been guiding clients through marketing analytics for the last 10+ years, and the most common issue we’ve seen is underinvestment in analytics, or analytics being viewed as “non-working budget.”

Current changes are forcing brands to take charge of their own first-party data, accelerating the need to have the right people and tools in house to collect and process such data—and likely leading to increased competition for qualified resources. Brands that find the right people first will gain competitive advantage over their rivals over the coming years.

What does all of this mean? How do you operationalize a first-party marketing strategy?

We will use a theoretical brand—Retailer X, an online-only apparel brand—as an example of how a brand can start to own their first-party data.

Retailer X is not a leader in online apparel sales but they’re doing fine, with a growth rate that matches their industry. While Retailer X was hit hard during the pandemic, they’re feeling pretty stable in 2021.

Retailer X has been outsourcing most of their marketing and analytics operations to agencies and data providers. Now, they’re worried about the impact of third-party cookies going away, and considering taking control over their own data going forward—but are not entirely sure how to do it.

Most of their technology licenses are provided through their media agency, and they’re using attribution models from Google Analytics 360 to measure some of the agency’s homework. At the same time, all agency-generated media reports are based on in-platform reporting (from Facebook Ads Manager, Google Ads, Display DSPs, etc.

Retailer X is not yet ready to move their processes or people in-house, and they don’t have a substantial hiring budget.

As a first step toward first-party marketing, they decided to in-house their data—not only to start understanding their marketing performance, but also as a way to put pressure on, and ultimately become independent from, their external agency partners.

DELVE’s first-party marketing roadmap for Retailer X

Every brand that’s doing digital marketing already has tools in one of the following five buckets, each of which corresponds to a step in the data journey. These tools Collect the data, others Transform it, then Analyze, Visualize, and finally Activate on that data in marketing and advertising.

In more basic scenarios, some tools can play more than one role. For example, Google Ads can be used across all five buckets since it allows marketers to:

1. Collect data through conversion and audience pixels;

2. Transform that data through calculated metrics such as CPA;

3. Analyze it for emerging patterns through in-tool prompts;

4. Visualize the data in its reporting center; and

5. Activate on the data by modifying targeting or bid adjustments.

In most cases, however, there will be more than one tool being employed—and not all tools are created equal.

Building a truly durable first-party marketing program takes effort across all five steps of the data journey. Over many years of helping clients with such projects, DELVE has defined the key activities that help brands achieve optimal outcomes at each step.

Let’s explore these activities as recommended for Retailer X.

1: Collect

GOAL: Understand your advertising technology and collect all first-party data


Third-Party Data Assessment
Examine the impact of third-party deprecation on revenue growth or advertising ROAS.

First-Party Data Enablement
Assess the potential opportunities from creating a first-party marketing strategy

Tech Architecture Audit
Define what tech is owned in-house vs. accessed through agencies; renegotiate contracts and take them in house; realize cost savings from lower fees to achieve higher immediate ROAS.

Web & App Analytics
Re/implement proper tag management and web/app analytics solutions; start to collect user-level non-PII data about consumer behavior; integrate with PII purchase data for a 360° customer view.

OUTCOME: First-party data wins

Retailer X now has their strategic asset properly collected, enabling all future steps to go more smoothly.

2: Transform

GOAL: Connect and transform all first-party data at user level


Data Lake
Once the right web and app data is flowing through the analytics platforms, implement a simple data lake (such as Google Cloud Platform or Amazon Web Services) to merge that data with offline sources, such as a back-end CRM or order management platform.

Data Enrichment
To resolve user identity, enrich it. In the standard data lake setup, Retailer X would use a deterministic user-level “spine” as the key identifier (usually with data furnished by TransUnion or another credit bureau).

Next, integrate it by appending Facebook, Google, Amazon, and other publishers’ cohort definitions to their own “spine,” essentially creating their own match table which can later be used for prospecting on a per-platform level.

Historically, this step could have been outsourced to a third-party solution such as LiveRamp, but that option goes away with the deprecation of third-party cookies. Also, its accuracy has suffered since Apple implemented their Intelligent Tracking Prevention (ITP) mechanism in 2018.

OUTCOME: Identity resolution

Now, Retailer X has their data in a form that can be applied toward solving their marketing optimization issues, and easily shared across their organization.

3 | 4: Analyze + Visualize

GOAL: Put the data to action and derive insights from it


Budget Allocation
Once all the necessary data is flowing in, Retailer X has the prerequisites to revisit their current attribution approach. Since Retailer X does not have the necessary hiring budget or skills in-house to build a team of analysts, they can either work with their media agency’s Analytics team or bring in another partner, usually analytics-first, to develop an automated Marketing Mix Modeling (MMM) approach. MMM helps mitigate the consequences of third-party cookie deprecation by assessing the incremental value that every channel and dollar spent brings to the bottom line.

Data Visualization
Once the data is properly transformed and analyzed, Retailer X’s media modeling partner would help develop an automated dashboard within their platform of choice (such as Looker, Tableau, or Power BI). This can be used by Retailer X’s in-house marketing team to make budget allocation decisions, and to solve other issues identified during the discovery phase, and requires little to no brand-side maintenance.

Retailer X is now making their budget allocation decisions based on the full-funnel Marketing Mix Modeling approach, in combination with attribution, to account for incrementally in their media.

OUTCOME: MMM is better than attribution

Retailer X is now making their budget allocation decisions based on the full-funnel Marketing Mix Modeling approach, in combination with attribution, to account for incrementally in their media.

5. Activate

By this point, Retailer X has achieved their target state and is well-protected against third-party cookie deprecation. However, their analytics can still be improved for better activation and to gain competitive advantage.


Data Science (optional)
While Data Science has been a topic of discussion for many years, it’s still not widely used because many consider it “marketing analytics on steroids” that looks forward in time instead of assessing past results. Plus, brands are still struggling to implement the right data and skill sets to enable their marketing analytics.

The good news is that those who implement data science now will stay ahead of their competition and ultimately capture increased market share. When paired with media activation, data science techniques—such as predicted lifetime value bidding, or automated bid adjustments based on weather in a selected region—can provide brands with significant improvements in their overall media performance.

Performance Channels
Every infrastructure initiative needs media activation in order for value to be realized. With the infrastructure that Retailer X has put in place, they are able to purchase media without dependence on third-party cookies—instead using first-party PII to improve conversion rate and lifetime value through retargeting and reactivation techniques.

Additionally, Retailer X can buy performance prospecting campaigns by utilizing tighter intent-driven targeting that’s tailored to their best customers, instead of relying on less accurate and more expensive third-party data.

OUTCOME: Activate first-party data to increase advertising ROAS

By storing all of its own anonymized and PII data, Retailer X is able to power more personalized and better-targeted branding and performance advertising campaigns.

At DELVE, we believe that right now is the time to take charge of your brand’s first-party data—and to begin the process of dominating the market. Our seasoned Data, Analytics, and Media professionals stand ready to consult with your team, share our experience, and develop a customized first-party marketing roadmap for your brand.

Ready to get started? Contact us today at begin@delvedeeper.com.