- April, 2024
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Charlie Nicholls
Identity Resolution Solutions: Exploring Options Beyond Third-Party Cookies
Table of Contents
Even with Google pausing plans to depreciate third-party cookies , businesses and advertisers are actively pursuing dependable and privacy-focused alternatives to maintain personalised advertising and customer targeting.
One of the primary challenges they face is identity resolution – the process of integrating identifiers across various touchpoints and devices to create a cohesive consumer profile. The ability to accurately identifying and understand individuals across a plethora of platforms and devices is a challenge exacerbated by the increasing fragmentation of data, devices, and markets, which complicate the task of achieving a unified view of the customer.
Identity resolution (read more in our ultimate identity resolution guide), allows companies to create unified user profiles, target customers with personalised experiences, and deepen relationships. However, with growing concerns over privacy and data protection, some methods used for identity resolution have come under intense scrutiny, with each coming with their own set of advantages and implications.
Navigating the Solutions
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Contextual Targeting: Contextual targeting aligns advertisements with the content of the webpage being viewed, emphasising thematic relevance over individual user data.
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Interest-Based Targeting: Interest-based targeting offers a privacy-conscious alternative to third-party cookies by leveraging user preferences and behaviour to deliver personalised content and ads.
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Fingerprinting: Fingerprinting creates unique digital identifiers based on device attributes, enabling persistent tracking of users across devices and sessions, albeit raising significant privacy concerns.
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Identity-Based Solutions: Identity-based solutions integrate user data across touch-points to establish unified profiles, facilitating personalised marketing and seamless customer experiences across channels.
Continue reading to explore each alternative further and understand their advantages and implications.
Contextual Targeting
Optimising Page-Relevant Experiences
Contextual targeting optimises user experiences based on the content of the webpage visited, foregoing individual data. By analysing page themes and keywords, this strategy tailors content or recommendations to align with page topics, ensuring relevance and engagement.
Example: A user exploring a recipe website for healthy dinner ideas receives dynamic content recommendations including nutritious meals and cooking tutorials, reflecting the page’s focus on healthy eating.
Our Verdict
While contextual targeting ensures that the content or recommendations are relevant to the content being viewed, it does have limitations. Since it does not rely on user behavior or demographics, contextual targeting provides limited insights into the preferences and characteristics of individual users. As a result, companies may miss opportunities for deeper personalisation and engagement.
However, contextual targeting can still be valuable, especially on websites with diverse content sections covering various topics. By strategically optimising the user experience based on the context of the page, companies can enhance engagement and satisfaction among their audience.
Interest-Based Targeting
Google Privacy Sandbox and Topics API
Interest-based advertising tailors ads based on users’ browsing history, contrasting with contextual targeting, which focuses solely on current page content. In response to evolving privacy concerns, Google announced its plan to phase out 3rd-party cookies and introduced Privacy Sandbox as an alternative. A key feature of Privacy Sandbox is the Google Topics API, which employs cohort analysis of browsing history instead of individual user data, offering a more privacy-compliant solution.
The aim is to utilise federated learning of cohorts (FLoC) to group users based on shared interests, rather than tracking their online activities individually. The Google Topics API is designed to empower users with greater transparency and control over their interests. As users browse the web, their browser learns about their interests and categorizes visited sites into predefined topics. This data, limited to the last three weeks of browsing history, informs ad targeting. Users can review and adjust their listed topics or choose to opt out of the Topics API entirely.
Example: Sarah regularly explores websites about healthy living, fitness, and travel. Through the Google Topics API in the Privacy Sandbox, her browser categorises her interests based on her browsing history. When Sarah visits a site supporting the Topics API, she sees content tailored to her interests, such as fitness equipment.Â
Our Verdict
The Google Privacy Sandbox shifts towards interest-based advertising, prioritising user privacy and control. While offering segmentation based on interest categories, it may provide fewer personalisation options than traditional methods.
As it evolves, its impact on user privacy, ad targeting effectiveness, and compliance with regulations like GDPR remains uncertain. Concerns arise that it could hinder the marketing industry’s ability to deliver impactful ads, potentially disadvantaging smaller brands and media companies, according to studies by the IAB.
In response, Google has delayed the depreciation of third-party cookies until at least 2025 while they work with regulatory bodies like the UK Competition and Markets Authority to ensure compliance.
Fingerprinting
What is Digital Fingerprinting?
Fingerprinting generates a unique digital profile of users based on their browser, system, and device’s distinctive features, including IP address, installed plugins, screen resolution, and time zone. Unlike 1st-party cookies, which are limited to a single domain, the consistent unique characteristics enable effortless tracking across various sites, simplifying the tracking process significantly. Moreover, since fingerprinting does not depend on storing data on the client, it is challenging to detect and nearly impossible to evade.Â
Fingerprinting enables tracking for months (as opposed to weeks like with the Google Privacy Sandbox), persisting even after clearing browser storage or using incognito mode, disregarding user preferences against tracking. Despite widespread acknowledgment among standards bodies and browser vendors that fingerprinting is detrimental, its prevalence on the web has steadily risen over the past decade.
Analyse Your Fingerprint:
Our Verdict
As users cannot opt out of this tracking, fingerprinting violates industry-wide privacy principles. Considering this, most browsers have announced plans to limit fingerprinting capabilities in the future.Â
Fingerprinting needs to be seen only as a temporary alternative to third-party cookies during the transition period, but tightening privacy regulations render this solution unviable in the long run. Â
Identity-Based Solutions
Among the emerging solutions, identity-based solutions stand out as promising avenues for navigating the evolving terrain of digital advertising.
Foundations of Identity-Based Solutions
Zero-party and first-party data emerge as the cornerstone of identity resolution as third-party cookies fade into obsolescence.Â
Let’s summarise the different types of data to gain a foundational understanding:
Zero-party data: An example of zero-party data is when a customer fills out a survey on a company’s website, providing information about their product preferences and interests directly.
- First-party data: A company collects data from its customers’ interactions with its website, such as browsing history and purchase behaviour, to tailor personalised recommendations and marketing messages.
- Second-party data: An example of second-party data is when a clothing retailer shares its customer data with a shoe manufacturer to better understand their overlapping customer base and tailor joint marketing campaigns.
- Third-party data: Data collected by advertising networks or data brokers from various websites visited by users, used to create profiles for targeted advertising based on demographics, interests, and online behaviour.
Identity-Based Solutions offer a diverse array of approaches to address the challenges posed by the demise of third-party cookies.Â
Here are some notable identity-based solutions.
ID Graphs
ID Graphs serve as intricate maps connecting personal identifiers with device-level identifiers, drawing insights from zero and first-party data. By stitching together user information, companies can tailor targeted advertising and personalised experiences across various touchpoints.
Example:Â Sarah utilises a fitness app on her phone and frequently browses a wellness blog on her laptop. The app identifies her through her email, while the blog employs first-party cookies. As Sarah logs workouts on the app and reads articles on the blog, both platforms track her activities and link them to her identifiers. This seamless connection enables consistent personalised experiences across her devices.
Our Verdict
ID Graphs offer robust targeting capabilities, allowing brands to activate audiences across channels effectively. However, building and maintaining these graphs can be complex, and if not executed properly, they may inadvertently compromise user privacy.
Data Clean Rooms
Data Clean Rooms provide encrypted and secure environments where multiple parties can collaborate on first-party data without compromising user privacy. Through anonymisation, layering, and matching techniques, these rooms enable privacy-compliant data sharing and analysis.
Example: In the health and wellness industry, a fitness tracker company, a supplement provider, and a magazine publisher collaborate on a campaign. They use a data clean room to securely pool their user data—like fitness activities, supplement purchases, and article views—without sharing individual details. This anonymized data allows them to uncover shared audience insights for targeted marketing, all while prioritizing user privacy.
Our Verdict
While Data Clean Rooms foster collaboration and data sharing, they require a trusted intermediary to uphold privacy laws and agreements between parties. Additionally, scalability remains a challenge, limiting their widespread adoption.
Walled-Gardens and Marketplaces
Walled Gardens and Marketplaces, such as those operated by Amazon, Google, and Meta, offer curated platforms for advertising and selling products or services. Leveraging their own ID-graphs, these platforms provide precise targeting while safeguarding user identification.
Example:I n Amazon’s marketplace, various sellers list products across different categories like electronics and fashion. Leveraging their own ID graph, Amazon provides personalised product recommendations and targeted ads based on user behaviour and preferences. For instance, a fitness equipment seller can target users interested in workout gear, increasing conversion rates. The walled garden ensures a seamless shopping experience across devices, with user data linked for convenience.
Our Verdict
Marketplaces provide precise targeting opportunities and guarantee cross-device advertising. However, they do not share identification with brands and may limit access to user data, potentially favoring larger players in the digital advertising ecosystem.
Universal IDs
Universal IDs serve as single identifiers that recognise users across the digital marketing ecosystem. By enabling seamless information sharing among approved partners, these IDs expand the reach of ad campaigns while adhering to data privacy regulations.
Please find a list of a few of the Universal ID providers below with some of their pros and cons,.
| Universal ID Provider | Pros | Cons |
|---|---|---|
| ID5 | Enables cross-device targeting and attribution | Limited inventory and audience reach |
| NetID | Simplifies user identification and data sharing | Potential privacy concerns |
| Zeotap ID+ | Enhanced ad targeting and personalisation | Requires user consent for data collection and sharing |
| Unified ID 2.0 | Facilitates seamless data integration and activation | Dependency on publisher and advertiser adoption |
| RampID (Liveramp) | Robust identity resolution and audience segmentation | Scalability challenges |
| Utiq | Provides comprehensive user profiles for targeting | Limited availability and adoption |
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Our Verdict
Universal IDs offer significant potential for enhancing ad targeting and reach. However, their scalability relies on agreements between numerous publishers, potentially resulting in fragmented audiences and limited inventory in niche markets. Additionally, user willingness to provide necessary information remains a limitation of its effectiveness and strong incentivisation and education is required by marketers to help users understand the benefits and implications of providing such information. Despite these challenges, Universal IDs represent a promising avenue for advertisers seeking to navigate the evolving landscape of digital marketing in a privacy-conscious manner.
The Right Solution?
Each identity resolution solution presents unique advantages and obstacles. Below is a table summarising the benefits, challenges and barriers for each solution.
| Solution | Benefits | Challenges | Barriers |
|---|---|---|---|
| Contextual Targeting | – Relevant ad placement based on webpage content | – Limited targeting options | – Inability to convey behavioral and demographic characteristics of users |
| Google Privacy Sandbox | – Enhanced user privacy and control | – Limited personalization options compared to traditional methods | – Concerns about impact on ad targeting effectiveness and compliance with regulations like GDPR |
| Fingerprinting | – Persistent tracking across devices and sessions | – Invasive tracking methods disregarding user preferences | – Violates industry-wide privacy principles, difficulty for users to opt-out |
| ID Graphs | – Comprehensive user profiles for targeted advertising | – Complex to build and maintain, potential privacy concerns | – Accidental compromise of user privacy if not executed properly |
| Data Clean Rooms | – Secure data sharing environment, privacy-compliant data collaboration | – Requires trusted intermediary, scalability challenges | – Limited widespread adoption due to complexity and scalability issues |
| Walled-Gardens | – Precise targeting opportunities, cross-device advertising, safeguarded user identification | – Limitations on data access for brands, potentially favoring larger players in the ecosystem | – Potential disadvantages for smaller brands and media companies |
| Universal IDs | – Enhanced ad targeting and reach, seamless data sharing among approved partners, compliance with data privacy regulations | – Scalability issues, potential fragmentation of audiences and limited inventory in niche markets, user willingness to provide necessary information | – Dependency on agreements between numerous publishers for scalability, concerns about user consent and privacy implications |
While some emphasise user privacy and consent, others prompt worries regarding surveillance and data exploitation. With regulations like GDPR and CCPA in flux, companies must responsibly navigate these intricacies, prioritising user privacy while delivering personalised experiences and targeted advertising. Striking the right balance between privacy and personalisation will be crucial in shaping the future of identity resolution.
About the Author
Charlie Nicholls
