Indepth: Data Clean Rooms - Navigating a privacy-focussed advertising landscape

Data clean rooms (DCRs) are becoming increasingly important in a privacy-focused environment with stricter privacy laws. As the advertising technology ecosystem evolves, organisations and brands are seeking ways to leverage first-party data while respecting user privacy and complying with regulations.

DCRs provide a controlled environment where multiple parties can collaborate and analyze data without directly accessing or sharing personally identifiable information (PII). These environments are designed to protect user privacy and ensure compliance with privacy laws and regulations.

Here are a few reasons why DCRs are important in a privacy-focused environment:

1. Privacy compliance: Stricter privacy laws, such as the General Data Protection Regulation (GDPR) require organisations to handle personal data responsibly and protect user privacy. DCRs allow organisations to collaborate and analyze data while minimizing the risk of privacy violations.
2. Data Protection: DCRs enable organisations to work with first-party data in a secure and controlled environment. By keeping the data within the clean room, organisations can mitigate the risk of unauthorized access or data breaches.
3. Cross-Organisational Collaboration: DCRs facilitate collaboration between multiple organisations while preserving data privacy. Advertisers, publishers, and technology providers can analyze data collectively to gain insights and improve advertising strategies without directly sharing sensitive information.
4. Advanced Analytics: DCRs provide a way to perform sophisticated analytics on aggregated data while preserving individual privacy. By combining data from different sources, organizations can gain valuable insights without compromising user privacy.
5. Trust and Transparency: Utilizing DCRs demonstrates a commitment to user privacy and responsible data handling. This can help build trust with customers and stakeholders, especially in an era where privacy concerns are high.

Overall, DCRs play a crucial role in a privacy-focused environment by allowing organisations to harness the power of first-party data while respecting user privacy and complying with privacy laws. They provide a controlled and secure environment for collaborative analysis, enabling organizations to gain insights and make data-driven decisions while safeguarding user privacy.

As the advertising technology ecosystem evolves, organisations and brands are rethinking ways in which first-party data can be integrated.

Mitesh Kothari, Co-founder and Chief Creative Officer, White Rivers Media, considers DCRs to be a fundamental requirement for authentic data collection and usage in these highly evolving times. He points out, “Illegal practices and consumer alertness have both grown tremendously over time and have led to stricter user privacy laws. Times are changing with advancing technology and the best bet to stay safe is to remain privacy-centric and undisputed. Stringent governance and permissions for data usage in a DCR can allow risk-free, legal data integration.”

As privacy laws become more stringent, DCRs have grown in importance in the advertising technology ecosystem, allowing organisations and brands to safely and securely integrate first-party data while respecting consumer privacy, notes Kalyan Kumar, CEO and Co-founder, KlugKlug.

DCRs create a safe environment in which two or more parties can share data without directly exchanging it, allowing businesses to combine datasets for analysis and insights without transferring raw data. This extra layer of privacy and security ensures that businesses can comply with regulations while also maintaining consumer trust. With the increased emphasis on first-party data, DCRs have become even more important as companies seek to responsibly and transparently leverage their data while creating personalised experiences and driving business results.

“We are not moving, but we are already in the cookieless world,” says Himanshu Nagrecha, Vice President, India & South Asia, TrafficGuard, adding, “With privacy guidelines becoming more stringent, marketers and publishers have realized the importance of having first-party data. Not only that, but they also recognize the need for reliable partners to consistently enhance their data sets. This exercise cannot be accomplished without Data Clean Rooms (DCRs). DCRs collect behavioral and contextual data (non-personally identifiable information) from various sources, assign a unique identification to it, and create cohorts. Marketers can then utilize these cohorts to target relevant audiences across the web.”

Will more advertisers, who possess significant media buying budgets, adopt and utilize data clean rooms as their popularity grows in the advertising industry? What factors contribute to this trend, and what are the potential benefits and challenges associated with implementing data clean rooms?

The growing importance

As the advertising landscape evolves and privacy regulations tighten, advertisers are seeking new solutions to navigate the cookieless world and maintain effective targeting strategies. Data clean rooms have emerged as one such solution. These controlled environments allow advertisers to collaborate with data providers, publishers, and other partners while adhering to privacy guidelines.

Several factors contribute to the increasing deployment of data clean rooms by advertisers with substantial media buying budgets. First, privacy concerns and regulations, such as the General Data Protection Regulation (GDPR), have placed restrictions on the use of third-party cookies and personal data. Advertisers recognize the need to adapt and find alternative methods for audience targeting and campaign optimization, making data clean rooms an attractive option.

Second, the reliance on first-party data has grown in importance. Advertisers realize that building and leveraging their own data sets can provide valuable insights and improve campaign performance. Data clean rooms enable advertisers to collaborate with trusted partners to enrich their first-party data and gain a deeper understanding of their target audiences.

Third, the demand for effective audience targeting remains high. Advertisers want to reach the right users with relevant messages to maximize their return on ad spend. Data clean rooms offer a privacy-conscious way to create cohorts and segments based on behavioral and contextual data, allowing advertisers to target specific audience segments accurately.

Increased adoption

More advertisers with considerable media buying budgets will increasingly deploy data clean rooms.

Data is a crucial, indispensable aspect of targeted advertising along with data security being a requisite for legal practice, points out Mitesh Kothari. According to him, data clean rooms go a long way in helping achieve the two together. “In a totally privacy-friendly way, a DCR can help garner a valuable data pool of audiences as per need without revealing the identifiers that can breach any privacy laws, thus minimizing any risks. In the year 2023 itself about 80% advertisers are expected to use this technology by utilizing over one billion dollars of media budgets.”

Given the growing emphasis on data privacy and the limitations imposed by cookie-less tracking, it is imperative that more advertisers with large media buying budgets will turn to data clean rooms to target and measure their campaigns effectively, says Kalyan Kumar. “In some ways the common ad platforms will have to build such DCR approaches for a more democratized service. Brand collaborations will also become more common in this context, to create more impactful campaigns,” he adds.

The future of DCR

With the world moving towards regulations like GDPR and enhanced user privacy, are we heading towards a world of cookieless advertising?

Third-party cookies have been a major mechanism to help marketers deliver targeted campaigns for years, says Mitesh Kothari. Going forward, he adds, although that phase is now coming to an end, we are positive in utilising the available resources to the best practice that is both legal and efficient.

“The availability of multiple DCRs along with walled gardens like Google’s ADH, Amazon Marketing Cloud and Meta Advanced Analytics can help achieve sustainable outcomes despite the newer restrictions. It’s ultimately going to be a win-win for advertisers to extract the large information pool offered by DCRs in a completely safe and privacy-compliant manner,” Kothari adds.

“A big yes,” says Kalyan Kumar. He further adds that this shift has already begun with the rise of contextual advertising and the use of first-party data. “The future of digital advertising may lie in a combination of these methods and developing core behavioral insights about consumers, as well as emerging technologies such as artificial intelligence and machine learning to accelerate those aspects which can help to personalise advertising without relying on cookies. Respecting a consumer’s privacy is/will not be an option very soon. Back to basics on sharpening your insights that trigger brand/business adoption. A good time for marketers to get out of eased performance marketing based on existing intrusions of privacy. I think its a beautiful challenge for marketers going back to brand marketing and an appropriate and a happy place for consumers,” he explains.

Customer Data Platforms

Customer Data Platforms (CDPs) are designed to address several technical barriers that organisations often encounter when dealing with customer data. There are some key technical barriers that a CDP helps overcome.

Mitesh Kothari says that customer data platforms are similar to DCRs in terms of secure and scalable data collection. “A CDP, however, is mainly used for internal data aggregation where we can also store user IDs and personal identifiers, of course, as long as they are collected authentically by compliance. We cannot share most of it with third parties for privacy reasons and thus the CDP serves exclusively the owner. So while a DCR would be a more neutral and large-scale ground, a CDP can benefit the first-party data collection points.”

A CDP, as is the very definition, typically integrates data from multiple sources, creates standardisation, hygiene, real-time data protocols and the ability to create a simpler unified customer view, says Kalyan Kumar. “Thus, CDPs enable marketers to improve their targeting via a centralised platform. All such targeting, the hope is, will yield better customer experiences and subsequently, increased revenue.”

A CDP enables organizations to consolidate all their first-party customer data from various sources into a unified location, says Himanshu Nagrecha. “For instance, if a user interacts with a promotional email and subscribes to a service, the CDP will create a comprehensive view of the customer’s name, mobile number, email address, IP address, and device ID. In the absence of a CDP, personal information would typically reside in the organisation’s Customer Relationship Management (CRM) tool, while digital information like IP address and device ID would be stored in a Data Management Platform (DMP). Having all this data in a CDP offers several advantages. It facilitates a better understanding of consumer behaviour by providing a holistic view of customers across different touchpoints. The CDP’s unified customer profiles enable organisations to analyze and segment their customer base into cohorts for more targeted marketing efforts. This means organisations can tailor their messages and offers to specific groups based on shared characteristics and behaviors. By leveraging a CDP, organisations can unlock the power of their customer data, gain insights, and make informed decisions to enhance their marketing strategies and deliver personalized experiences,” Nagrecha adds.

ML and AI

Machine learning (ML), artificial intelligence (AI), and related technologies are already playing a significant role in shaping the future of contextual targeting, personalization, and related areas. Here are some ways in which these technologies will have an impact:

1. Contextual Targeting: ML and AI algorithms can analyze vast amounts of data to understand the context in which ads or content are displayed. By analyzing factors such as keywords, user behavior, content type, sentiment, and more, these algorithms can determine the most relevant context for ad placements. This allows advertisers to target their messages more accurately and deliver content that aligns with the interests and preferences of individual users.
2. Personalization: ML and AI algorithms are key to delivering personalized experiences to users. These technologies can analyze user behaviour, preferences, historical data, and contextual information to create individual user profiles and make tailored recommendations. Whether it's personalized product recommendations, content suggestions, or customized marketing messages, ML and AI enable organizations to provide personalized experiences at scale.
3. Predictive Analytics: ML algorithms excel at predictive analytics, which can help in forecasting user behaviour, customer preferences, and future trends. By analyzing historical data, ML models can identify patterns and make predictions about future outcomes. This allows businesses to anticipate customer needs, optimize marketing strategies, and make data-driven decisions for improved targeting and personalization.
4. Natural Language Processing (NLP): NLP, a subfield of AI, enables machines to understand and interpret human language. With NLP, organisations can analyze text-based data such as customer reviews, social media conversations, customer support interactions, and more. This analysis helps in understanding customer sentiment, identifying emerging trends, and improving contextual targeting and personalization efforts based on the language used by customers.
5. Image and video recognition: ML and AI techniques like computer vision have made significant advancements in image and video recognition. This enables organizations to extract meaningful insights from visual content, such as identifying objects, scenes, emotions, and even individuals. By analyzing visual data, businesses can deliver personalized experiences based on users' preferences and interests, as well as enhance contextual targeting in visual-based advertising.
6. Real-time decision-making: ML and AI algorithms can process vast amounts of data in real-time, allowing organizations to make instant decisions and responses. In the context of contextual targeting and personalization, this means that organizations can deliver relevant content, recommendations, or offers to users in the moment, increasing the chances of engagement and conversion.

ML, AI, and related technologies will continue to evolve and play a crucial role in shaping contextual targeting, personalization, and other areas. They empower organizations to understand customer behaviour, extract insights, and deliver tailored experiences that drive engagement and customer satisfaction.

Machine learning and AI are advancing at a rapid rate, offering golden opportunities across industries, points out Mitesh Kothari. “Human behaviour is exceptionally convoluted and needs a deep understanding to support campaigns in the advertising world. AI can help efficiently analyse and utilise this behaviour and prove to be a game changer. It can not only help with custom, accurate targeting, but also provide a large pool of information that can be beneficially leveraged for content strategization and curation, yielding great ROIs,” Kothari adds.

Machine learning and artificial intelligence are already changing much of contextual targeting and personalization in the future, says Kalyan Kumar. “These technologies possess the remarkable capacity to handle immense volumes of data and derive insights from user behaviour. This capability will empower businesses to deliver highly focused and personalized experiences to their customers. By incorporating layers of synthesized behavioral economics that are even more substantiated through empirical evidence, the potential for advancements in this field is set to surpass our current comprehension. This includes personalized product recommendations and advertising campaigns finely tuned to optimize each input parameter. As machine learning (ML) and artificial intelligence (AI) continue to progress, the pace of advancements will escalate exponentially, dwarfing anything we have witnessed thus far and appearing almost linear in comparison to the forthcoming developments,” Kumar adds.

As the 2023 deadline for Google Chrome’s cookieless era looms closer, marketers face limited options to remain compliant, says Kalyan Kumar, who believes that marketers should shun outdated data methods.

It’s crucial for marketers to redirect their attention towards platform partners capable of achieving results without relying on cookies. According to Kumar, “Instead of clinging to outdated data methods, the future of personalized advertising relies on embracing adaptability and exploring new avenues. In this evolving landscape, insights-driven marketing and creative optimization will experience a resurgence. The beauty of AI lies in its ability to emulate the intricate decision-making processes traditionally performed by humans. It can incorporate principles like the Nudge Theory and other effective marketing techniques, ensuring the delivery of impactful campaigns. By embracing this paradigm shift, marketers can navigate the cookieless era and harness the power of AI to create meaningful connections with their target audiences. It's time to embrace change, adopt innovative approaches, and pave the way for the future of personalized advertising.”