Data Clean Room: Next Advertising Era
Data sharing among various players in the advertising industry (publishers, advertisers, ad tech companies) is increasingly gaining market share, with a growing number of startups entering the scene.
One lesser-known feature of third-party cookies was their ability to facilitate data sharing among different market players. An Ad Exchange enabled advertisers (the demand side) to meet publishers (the supply side) for advertising spaces and audiences. However, the phasing out of third-party cookies will no longer allow for this easy and ubiquitous sharing. But Data Clean Room can support on this. Let discover how.
What is a Data Clean Room?
A Data Clean Room is a physical or virtual space where multiple parties can work together on a dataset without sharing sensitive information. This setup is valuable for purposes like research, product development, or problem-solving.
In a data clean room, information is compartmentalized. Each compartment is exclusively accessible to specific users, preventing any individual from seeing or using others' data for unauthorized purposes.
To establish a data clean room, various strategies can be employed. One common approach is using data virtualization software, which creates isolated and secure data views, preventing data intermingling. Another technique involves a more controlled and direct process, where data is securely shared among the relevant parties.
Data clean rooms offer several benefits, including:
Enhanced Privacy and Security: Data clean rooms help safeguard sensitive data from unauthorized access.
Increased Collaboration: They enable parties with conflicting interests to collaborate on a dataset without sharing sensitive information.
Improved Accuracy: Data clean rooms can help minimize the risk of human error.
Data clean rooms are a crucial tool for collaborating on sensitive data and are used across various sectors, including medical research, finance, and defense.
Here are some examples of how data clean rooms are utilized:
Two companies developing a new product might use a data clean room to share product test data without revealing their trade secrets.
A marketing firm could use a data clean room to collaborate with an advertising agency on a new campaign.
A hospital might use a data clean room to share patient data with researchers without disclosing the patients' personal identification information.
The generic definition of a Data Clean Room also highlights its most significant weakness: it's a research environment, lacking out-of-the-box functionalities. Think of it as a database with as many tables as there are involved companies. Each participant can analyze individual rows only in the table they own, while they can access other tables only for aggregated data. Using a Data Clean Room effectively requires a team skilled in its use and clear goals that leverage the tool's features. Otherwise, it's like an expensive empty box: any data is useless without a business objective.
What are the key uses of a Data Clean Room in Marketing/Advertising?
Targeting Optimization: Data Clean Rooms provide an effective tool for identifying and matching users across various datasets. This process enables businesses to develop more precise audience segments, resulting in more accurate ad targeting that can significantly increase the return on investment (ROI).
Campaign Effectiveness Assessment: Using Data Clean Rooms allows for accurately quantifying the impact of marketing initiatives. The analysis gives companies crucial data to optimize advertising strategies, enhancing campaign performance and boosting ROI.
Personalization of Promotional Strategies: Data Clean Rooms enable the creation of tailor-made targeting strategies for a company's marketing personas. This helps businesses better connect with their desired audience, delivering messages that resonate more with their interests. The outcome? Increased engagement and a rise in conversions.
Which trends are emerging in the market?
In the market, I have identified three types of Data Clean Rooms:
Walled Garden
Limited Data Access: These data clean rooms are typically owned and operated by a single company, like Google or Amazon. Advertisers can only access data from that company and merge it with their own, which can limit their ability to get a complete picture of their audience.
Ease of Use: Walled garden data clean rooms are usually user-friendly, as they are designed with a specific purpose in mind. This can make them a viable option for advertisers who are new to data clean rooms.
Limited Flexibility: Walled garden data clean rooms are not as flexible as other types of data clean rooms because they are designed for specific business cases. It can be complicated, sometimes impossible, to develop different business cases with them. With limited functionality, they are perfect for beginners in advertising. They are the tool to start exploring the possibilities of a Data Clean Room before venturing to create one for one's own company.
Cloud
Broader Data Access: Cloud data clean rooms are not owned by a single company, so advertisers can access data from multiple sources. This can provide a more comprehensive view of their audience.
Increased Flexibility: Cloud data clean rooms are more flexible than walled garden data clean rooms, as they can be used for a variety of purposes. This can make them a valid option for advertisers who need to perform more complex analyses.
More Complex: Cloud data clean rooms can be more complex to use compared to walled garden data clean rooms, as they are not designed for a specific purpose. This can make them a better option for advertisers with more experience in data clean rooms.
Specialized
Specific Uses: Specialized data clean rooms are designed for specific purposes, such as measuring the effectiveness of advertising campaigns. This can make them a valid option for advertisers who need to perform very specific types of analysis.
More Expensive: Specialized data clean rooms can be more expensive than other types of data clean rooms because they are designed for specific uses and feature ready-to-use tools and interfaces which, in the end, could turn out to be a less expensive investment than a cloud Data Clean Room where everything must be developed from scratch.
Harder to Use: Specialized data clean rooms can be harder to use compared to other types of data clean rooms because it is necessary to delve into the philosophy of the specific solution adopted. This can make them a better option for advertisers with a lot of experience.
Key Walled Garden Data Clean Rooms:
Google Ads Data Hub (ADH): A trailblazer for marketing data clean rooms, it allows analysis within Google Marketing platforms like Google Ads, Google Display & Video 360, and consequently access to Google search, YouTube, and Google Display Network's cookie space.
Amazon Marketing Cloud: Similar to ADH, it provides access to Amazon's advertising space.
Walmart Connect: Built on Snowflake technology, it uniquely includes both online and offline customer behavior for Walmart, a standout feature in the market.
Disney’s clean room solution: Also built on Snowflake but made accessible through the Habu platform, it offers data from Disney’s digital ecosystem excluding parks, hotels, cruises, etc.
NBC One Platform: NBC's data clean room, which owns brands like CNBC, DreamWorks, Universal Television, is also powered by Snowflake technology.
Leading Cloud Data Clean Rooms:
Snowflake: The undisputed market leader. Its cloud technology was designed for data interoperability, making Data Clean Rooms a natural extension.
Google BigQuery Data Clean Room: A new contender leveraging the BigQuery brand and user-friendliness, plus integration with Google Ads Data Hub as selling points.
AWS Data Clean Room: AWS is the most widely used cloud infrastructure and offers an integration with Amazon Marketing Cloud, making it a favored solution for e-commerce.
Top Specialized Data Clean Rooms:
InfoSum: Possibly the most mature product, having been on the market for several years. It focuses on the business case of data collaboration: sharing data between different companies for analysis and targeting.
LiveRamp Safe Haven: Initially created for measurement, LiveRamp's historical domain, now it also supports targeting. It leverages a data marketplace for data collaboration.
Habu: A disruptive startup that has entered the market with data collaboration as its strongest selling point.
AppFlyer: it has added a Data CleanRoom solution to its suite, particularly aimed at measuring advertising campaign performance and optimization.
Pyte: A startup focused on data collaboration using their secure match product, which offers more security than traditional hashing and operates on-premise.
Samoha: Stands out for its no-code integration with other data clean rooms like Google Ads, AWS Marketing Cloud, The Trade Desk.
Conclusions
Data clean rooms are an excellent tool, but they're best reserved for targeted initiatives. If you're navigating the Google Marketing Suite, kick things off with Google Ads Data Hub. It’s your go-to for integrating your company’s first-party data with Google’s cookie space. The goal? To refine campaign measurement and forge more effective audience segments, all while leveraging the rich data insights that Google offers, including campaign impression details.
For those embedded in Amazon Marketing, initiate your strategy with Amazon Marketing Cloud. It's set up to tackle the same business scenarios you'd address with Google Ads Data Hub.
As you grow more accustomed to these platforms, you might consider expanding your toolkit. This is where specialized clean rooms such as InfoSum and Habu come in, with their ability to connect to walled gardens, enhancing your data strategy.
But before diving in, it's crucial to outline your business objectives and pinpoint the KPIs that will gauge your success. Also, devise a plan for tracking these KPIs. If this preliminary planning seems daunting, a data clean room may not yet suit your needs.
Here are four potential business cases to ponder:
Data Clean Room Business Cases
Cross-Media Campaign Effectiveness Measurement
Challenge: A company is looking to gauge the impact of its multi-channel ad campaign but struggles with fragmented data across various platforms like TV, social media, and online ads, each hosting its own silo of user data.
Data Clean Room Solution: The company leverages a data clean room to integrate performance data from these disparate sources without compromising individual identities or sensitive details. This integration offers a comprehensive view of the campaign's impact, pinpointing the most successful channels and messages, which paves the way for smarter future investment.
Audience Targeting Optimization
Challenge: A fashion label aims to pinpoint customers interested in similar online products while adhering to strict privacy regulations.
Data Clean Room Solution: The label utilizes a data clean room to anonymously analyze purchase patterns with e-commerce partners and retailers. This strategy crafts anonymized interest-based audience profiles, sharpening the precision of targeting for upcoming campaigns.
Collaboration Between Brands Without Exposing Sensitive Data
Challenge: Two synergistic businesses, like an energy drink firm and a health snack brand, plan a joint campaign targeting similar consumer demographics.
Data Clean Room Solution: They share data within a data clean room, identifying overlaps and potential collaboration points without spilling confidential details. This collaboration enables a well-focused joint campaign, maximizing marketing efforts and boosting conversions.
Post-Acquisition Customer Journey Analysis
Challenge: A financial services entity seeks to understand the journey of customers acquired through a recent campaign to determine their long-term value and quality.
Data Clean Room Solution: By deploying a data clean room, the entity anonymously tracks customer interactions post-campaign, respecting privacy concerns. Aggregated and anonymized data analysis yields insights into customer retention, lifetime value, and the enduring impact of the acquisition strategy.
In all these cases, data clean rooms act as a linchpin for companies aiming to collaborate and capitalize on data for marketing without sacrificing data security or user privacy. As technology and regulations evolve, data clean rooms are poised to become the go-to strategy for data sharing and sophisticated analysis, extending beyond the marketing realm.