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Revolutionizing Marketing: How AI Empowers Advertisers in the Era of Privacy Laws and Cookie Limitations


The marketing landscape is undergoing a seismic shift as privacy laws and the phasing out of cookies disrupt traditional advertising practices. But amidst these challenges, a new beacon of hope has emerged: Artificial Intelligence (AI). AI is poised to revolutionize the way marketers advertise, providing innovative solutions that respect user privacy while delivering targeted campaigns. In this article, we explore the transformative power of AI and its ability to help marketers navigate the evolving digital landscape.

What are (Digital Tracking) Cookies?

Tracking cookies are labels that are unique to every user and contain an ID that the platform uses to keep track of users and web pages. They label web pages when they load and also label the user who visited. Cookies label demographic information for each user that visits a web page and describe useful information about the users and the web pages they visited. Cookies do not contain directly identifiable information about the user, and they are inherently incomplete when it comes to identifying people behind the user.

Flaws of Cookie Based Tracking:

There are several flaws associated with cookie-based tracking. For example, users do not represent actual people but are a count of unique IDs. These IDs are not shared by different browsers, devices, or locations, can be easily deleted and are often deleted by the people they supposedly represent. Users are only unique to a specific time period and after that time are reset, and the user counts are not perfectly precise. These flaws can have a direct impact on your marketing because whenever you calculate any metrics that rely upon your website counts, you will have data filled with artifacts and errors.

In a nutshell: Why should we move on from Cookies?

  1. They do not represent actual people; they are only a count of unique IDs.
  2. They are not shared by different browsers, devices, or locations.
  3. They are easily deleted and often deleted by the users they supposedly represent.
  4. They are only unique to a specific time period and after that time are reset.
  5. Their counts are not perfectly precise and are often adjusted from the actual numbers by an unknown amount.
  6. Sampled users mean you are only getting a distant snapshot and not precise data if you work with custom segments or add 2nd dimensions to the data.​

Opportunities for Cookieless Tracking:

On the other hand, cookieless tracking is becoming more popular due to the need for more accurate attribution and the use of multiple devices by users from various locations. Unlike cookies, cookieless tracking involves a script that runs when someone visits your site. The data this script captures is not stored in a cookie in the visitor’s browser, but sent to an analytics server that stores the streaming data. Cookieless tracking allows you to track people not only cross-domain but also cross-device. This means that no matter where consumers are searching for you – desktop, laptop, smartphone, etc. – you can accurately attribute them and see their real, complete customer journey.

The Post Cookie World

In a post-cookie world, tracking user behavior and preferences is evolving. Companies are seeking more privacy-friendly solutions to continue running effective online ad campaigns, despite the reduction in direct response data. Google’s main initiative is its Privacy Sandbox suite of tools, which track different aspects of user behavior without using specific identifiers.

A recent experiment by Google with the Sandbox-based Interest tracking tools (IBA) showed that compared to cookie tracking, advertiser spending decreased by 2-7% and conversions per dollar decreased by 1-3%, while click-through rates remained within 90% of the status quo. Google also noted that campaigns using AI-powered optimization were less impacted by the removal of third-party cookies, hinting at the role machine learning can play in driving results​.

The Rise of Privacy Laws:

As governments worldwide prioritize user privacy, stringent regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have come into effect. These laws limit the collection and use of personal data, making it harder for marketers to target specific individuals. However, AI offers alternative avenues for personalized advertising.

Many experts believe AI will play a significant role in advertising, helping to replace reliance on individual user identifiers online. AI can be used to improve the efficiency and effectiveness of first-party targeting, thereby offering privacy-safe ad targeting and measurement. For example, advertisers can use AI systems to evaluate different buyer personas using an organization’s zero- and first-party data, which is more actionable and doesn’t involve invasive data collection practices. Once a campaign has been deployed, AI can power post-click analytics for further optimization and can help home in on attribution

The Demise of Cookies:

Cookies, once a cornerstone of online advertising, are losing their relevance due to privacy concerns and increased user awareness. Web browsers are phasing out support for third-party cookies, making it challenging for marketers to track and target users across websites. AI steps in as a game-changer, providing sophisticated algorithms and techniques to glean insights from limited data.

Leveraging AI for User Data Analysis:

AI-powered tools excel at analyzing vast amounts of data, enabling marketers to gain valuable insights while respecting user privacy. By utilizing machine learning algorithms, marketers can understand consumer behavior, preferences, and purchase patterns without relying heavily on individual user data. This shift toward aggregated insights ensures compliance with privacy laws while delivering effective advertising campaigns.

Other ways AI is being used for privacy-safe targeting include launching contextually-targeted ads. For instance, some firms are using computer vision, a branch of AI, to analyze digital environments and place ads where audiences are likely to be most engaged. AI models can help advertisers target and drive successful campaigns without the use of cookies or IDs. The applications of AI in privacy-safe advertising are expected to expand, with AI being used for dynamic creative optimization, taking in all signals from a digital environment and creating creatives that align with the environment and drive optimal attention.

Marketing in a Post Cookie World:

Post-cookie tracking in 2023 is largely focused on privacy-friendly methods. With growing awareness around data privacy, legislative measures like GDPR and CCPA, as well as technology companies like Apple and Google taking a stand against intrusive cookies, the landscape of user behavior tracking has drastically changed.

Google’s proposed cookie alternative is called the Topics API, where a user’s browser learns about them based on their browsing history and assigns specific interests, or topics, to the user. These topics are automatically deleted after three weeks, and machine learning algorithms are deployed to evaluate the site’s domain name and make an estimate for sites that Google has yet to categorize​

  1. First-party Data Collection:

    As companies lose the ability to easily track users across websites using third-party cookies, there has been an increased emphasis on first-party data. Companies have developed strategies to encourage users to provide their own data voluntarily, through methods like registration, surveys, quizzes, or gated content. These approaches are more privacy-friendly because they rely on explicit user consent.

    Traditional advertising agencies are also building out their AI capabilities to mitigate issues like CPM inflation while enhancing media insights, media performance, and brand safety initiatives. AI can aid advertisers in finding commonalities in data sets and then launching creative tied to those insights at scale by targeting impressions or keywords versus IDs tied to users​
  2. Federated Learning of Cohorts (FLoC):

    As a replacement for third-party cookies, Google proposed the Federated Learning of Cohorts (FLoC). FLoC sorts users into broad groups or “cohorts” based on their browsing habits, instead of creating individual profiles. As of my last update in 2021, Google is testing FLoC with advertisers and plans to roll it out broadly.
  3. Contextual Advertising:

    With the decline of personalized ad targeting, many companies are resorting to contextual advertising, which targets users based on the type of content they’re viewing rather than their past behavior. This method has always been privacy-friendly because it doesn’t involve tracking individual user behavior across the web.
  4. Unified ID Solutions:

    As a response to cookie depreciation, unified ID solutions are becoming more popular. These systems rely on hashed or encrypted email addresses or phone numbers, and they aim to enable targeting and attribution while preserving user privacy. Unified ID 2.0, led by The Trade Desk and backed by several major ad tech companies, is one such solution.
  5. Device Fingerprinting:

    While this method is more intrusive and less privacy-friendly, some companies may use device fingerprinting to track user behavior. This involves collecting data about a user’s device, such as the operating system, browser version, and screen resolution, to create a unique “fingerprint” of the device. However, this method is widely criticized and might face regulatory challenges.
  6. Privacy Sandboxes:

    In a bid to replace third-party cookies, Google is also developing a concept known as the Privacy Sandbox, a set of open standards to enhance privacy on the web. It is still in the developmental stages, but it’s designed to allow companies to target and measure ads without having access to individual user data.

Case Study: Nike Boosts Conversions with AI-driven Analysis

Nike, a leading global sportswear brand, faced significant challenges after privacy laws limited their access to customer data. They turned to AI-based analytics platforms, which leveraged machine learning algorithms to analyze aggregated data from multiple sources. By identifying patterns and trends, Nike successfully optimized their marketing strategies, resulting in a 25% increase in conversions within three months.

AI-Powered Targeting and Personalization:

While traditional methods relied heavily on individual user data, AI enables marketers to create personalized experiences without compromising privacy. Machine learning algorithms can analyze user behavior in real-time, identifying relevant segments and delivering tailored content. This approach ensures a more user-centric advertising experience, resulting in higher engagement and conversion rates.

Case Study: Spotify’s AI-Driven Personalization:

Spotify, the popular music streaming platform, faced a significant setback when third-party cookies became obsolete. They embraced AI-driven personalization techniques, leveraging natural language processing and machine learning algorithms to understand user preferences and interests. By tailoring content recommendations based on real-time interactions, Spotify experienced a 30% increase in user engagement, showcasing the power of AI in personalization.

Predictive Analytics and Forecasting:

AI’s predictive capabilities empower marketers to anticipate consumer needs and trends, enabling proactive campaign planning. By leveraging historical data, machine learning models can forecast market dynamics, identify emerging trends, and optimize marketing strategies. This data-driven approach ensures marketers can stay ahead of the curve, even in the absence of granular user data.

Case Study: Coca-Cola Optimizes Ad Spending with AI Predictive Analytics:

Coca-Cola, a multinational beverage company, faced challenges in allocating their advertising budget effectively due to the limitations imposed by privacy laws. They adopted AI-driven predictive analytics, which accurately forecasted market demand and identified the most effective advertising channels. This strategic shift allowed Coca-Cola to optimize their ad spending by 20%, enhancing their return on investment.

AI-Powered Content Creation:

Creating compelling content is a crucial aspect of successful marketing campaigns. AI tools, such as natural language generation, can analyze vast amounts of data, enabling marketers to generate engaging and personalized content at scale. Brands like McDonald’s have employed AI-powered content creation to craft tailored social media messages, resulting in increased customer engagement and brand loyalty.

Predictions for the post-cookie marketing world:

  1. Rise of First-Party Data:

    With third-party cookies going away, first-party data will become even more valuable. Businesses will likely double down on methods to collect this data directly from their customers, such as through website registrations, subscriptions, and customer loyalty programs.
  2. More Transparent Data Practices:

    As consumers become more aware and concerned about privacy, companies that are transparent about their data practices could gain a competitive advantage. This could lead to more companies openly sharing how they collect, use, and protect customer data.
  3. Contextual Advertising Takes Center Stage:

    As individual user tracking becomes more difficult, marketers may lean into contextual advertising, which places ads based on the content being viewed, rather than the user’s past behavior.
  4. Increased Use of AI and Machine Learning:

    With the shift from third-party data, marketers may rely more on AI and machine learning to analyze and derive insights from the data they do have. These tools can help identify trends, predict consumer behavior, and personalize marketing messages, even without detailed user tracking.
  5. Greater Emphasis on Building Brand Trust:

    As it becomes more difficult to rely on personalized advertising, businesses may place greater emphasis on building strong, trustworthy brands that attract customers organically.
  6. Privacy-First Solutions Will Emerge:

    The void left by the removal of cookies will be a driver for innovation. Expect to see new privacy-first solutions emerge, designed to enable effective advertising and analytics while respecting user privacy.
  7. Growth in Walled Gardens:

    Platforms that have a significant amount of first-party data (like Facebook, Google, and Amazon) could see even more advertising spend, as marketers may have fewer options for reaching specific audiences elsewhere.
  8. Shift Towards Multichannel Marketing:

    In the absence of cookies, marketers might adopt a more multichannel approach, where the focus will be on reaching customers across multiple touchpoints to create a more unified and cohesive customer experience.

The CDO TIMES Bottom Line:

As privacy laws and the demise of cookies reshape the marketing landscape, AI emerges as a powerful ally for advertisers. By leveraging AI-driven user data analysis, targeting and personalization, predictive analytics, and content creation, marketers can adapt to the changing norms while delivering effective campaigns. The examples and case studies mentioned demonstrate how companies like Nike, Spotify, Coca-Cola, and McDonald’s have successfully harnessed the power of AI to overcome challenges and drive tangible results. As we move forward, embracing AI will be crucial for marketers to thrive in the era of privacy laws and evolving consumer preferences.

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Carsten Krause

As the CDO of The CDO TIMES I am dedicated delivering actionable insights to our readers, explore current and future trends that are relevant to leaders and organizations undertaking digital transformation efforts. Besides writing about these topics we also help organizations make sense of all of the puzzle pieces and deliver actionable roadmaps and capabilities to stay future proof leveraging technology. Contact us at: to get in touch.

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