Today the IAB Tech Lab is publishing version 1.0 of the Open Private Join and Activation (OPJA) clean room interoperability standard. Throughout the past year, together with a growing number of industry collaborators and members of the Tech Lab’s Privacy Enhancing Technologies (PETs) and Rearc Addressability working groups, our team played a leading role in developing OPJA with the goal of enabling interoperable privacy safe ad activation based on PII data.

Beyond our work on the initial proposal, we have several broader goals with OPJA:

1. We aim to define an open and standard set of requirements for a type of clean room operation that enables an advertiser and a publisher to match sensitive datasets containing user PII, such as email addresses or phone numbers, while limiting information exchange between parties as much as possible.
   
   
2. We want to develop and promote the adoption of standard mechanisms in OpenRTB that enable ad targeting of OPJA-matched user ad impressions, using any compatible SSP or DSP.

3. We want to provide open reference implementations that enable OPJA while adhering to the stated requirements.
   
   
4. We want to support both OPJA’s encrypted labels as a way of securely activating matched audiences from Optable, as well as interoperate with other vendors based on OPJA’s secure matching mechanisms.

While we think that there is room for clean room vendors and collaboration platforms to offer their own proprietary spin on the activation use case (many already do), we’re hoping that they will make an effort to evaluate and align their implementations to better adhere to OPJA, and we intend to make it easy for them to do so.

In order to achieve our goals, agreeing on an independently trustable manner in which user data can be matched and activated in the multi clean room vendor setting was imperative.

Doing this work in the open is essential, as it ensures that it is widely accessible and that any vendor can contribute ideas and review the proposed protocols and technologies. Open-source promotes transparency, collaboration, and inclusiveness in the development process. We believe that providing a common foundation that anyone can access, modify, and contribute to is essential to achieving interoperability between all vendors, instead of a select few.

Why Activation?

We decided to focus our initial interoperability standards efforts on the activation use case not only because it is a frequently encountered use case in industry, but also because we have noticed confusion regarding the extent to which user information is exchanged between parties that enable the use case in proprietary ways today.

On the surface, activation of overlapping audiences matched using a clean room is straightforward. Consider the case of an advertiser with a list of customers that wants to display ads to those customers when they are interacting with a publisher’s websites or applications. If users have provided personally identifying information, such as their email address, to both the publisher and advertiser directly, then the advertiser and publisher can compare datasets in a clean room in order to construct an audience of overlapping users. Here’s a Venn diagram illustrating the operation:

While seemingly simple on the surface, when it comes to the sharing of information associated with individual users, there are several subtle but material differences that may arise when such an operation is performed in practice. Notably, what new user information could the advertiser and publisher parties learn as a result of performing the match and targeting operation? Will the advertiser be able to track which of its individual customers are also browsing the publisher’s websites? And will the publisher learn which of its registered users are also the advertiser’s customers?

To answer such questions, a standard set of security and privacy design goals, input and output requirements, and clear documentation regarding the extent to which private user information is exchanged between parties when enabling the ad activation use case were all elaborated and made part of the OPJA specification. Ultimately, our goal with OPJA is to enable ad targeting on overlapping users without the parties leaking user information to each other. This is not only good for end user privacy, but it also prevents data sharing that could be exploited by competitors.

Raising the Privacy Bar

A defining characteristic of clean rooms is their potential to limit the scope of the processing of user data controlled by multiple parties. A simple example of this in practice is the construction of an aggregate report describing the intersection of two audiences originating from separate parties. In such a report, the joining, grouping, aggregation, and statistical noise injection can all be performed in a data clean room, thus preventing either party from learning anything about the other party’s data, other than what is included in the prescribed report.

This limiting capability of data clean rooms is inherent in the activation matching operation prescribed by the OPJA specification. In OPJA, a secure match is performed in order to determine which individual users are in the intersection of audiences originating from an advertiser and a publisher. Rather than the list of matched users being shared with either party, the presence or absence of each user in the intersection is encoded in the form of a label and is then encrypted. These encrypted user labels are shared with the publisher who cannot decrypt them, but who is able to insert them into ad requests. Ad requests are processed by ad tech (SSPs and DSPs), and only the advertiser’s designated DSP can decrypt corresponding match labels, enabling the DSP to make decisions on whether and how much to bid for the opportunity to show an ad. Critically, PII such as email address or phone number are never shared or transferred in ad requests, or outside of the match operation.

Equally important is that thanks to label encryption, OPJA allows the hiding of information about which individual users are in the audience intersection from both the advertiser and the publisher. This reduces data leakage between advertisers and publishers, and enables remarketing without requiring user tracking. Fundamentally, it’s an approach that adheres to the data minimization and purpose limitation principles of privacy by design.

Privacy Enhancing Technologies

OPJA outlines two approaches enabling the matching of user PII data in the multi-vendor setting, and they’re both based on Privacy Enhancing Technologies (PETs). The first is a purely software based, delegated private set intersection. This method enables the comparison of encrypted datasets using commutative encryption, without decrypting the data. The delegated helper server cannot decrypt the match data and is used merely to execute data comparison and generate encrypted data for activation. Additional trust in the helper server could be provided through hardware provided remote attestation.

The second approach is based on hardware provided Trusted Execution Environments (TEEs). This method ensures that match data is encrypted exclusively for the secure processing hardware provided by a helper server.

The use of PETs offers a robust foundation from which trust between vendors regarding how user data is matched can be achieved. OPJA matching requires that the data remains protected with encryption during processing, through a combination of cryptography software and TEE hardware. This greatly reduces the number of things that vendors and service providers need to trust each other with.

OPJA’s matching approaches are also not theoretically limited to a single cloud or infrastructure environment. These characteristics make PETs based approaches great as matching interoperability candidates in the multi-vendor setting.

Learn More

You can read the OPJA specification as well as the IAB Tech Lab Data Clean Room Guidelines here. Additionally, here's the Tech Lab's latest announcement on the 1.0 spec release.

For a fun introduction to OPJA, check out Digiday’s excellent WTF is IAB Tech Lab’s Open Private Join and Activation?

For a simple walkthrough on how commutative encryption can be used to enable double blind matching (not specific to OPJA), have a look at the little explainer here.

Integrate

If you’re a data or ad tech vendor (SSP, DSP, ad server) interested in interoperating with the Optable data collaboration platform using OPJA, we’d love to hear from you. Drop us an email.

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Finally, it’s our hope that OPJA is a catalyst for future open proposals associated with measurement, audience modelling, and other use cases that involve the sharing of sensitive user data between advertisers and publishers.

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As people spend more and more time online, consumers have demanded more control over their digital privacy. They’ve become particularly uncomfortable with digital tracking technology like third-party cookies that enable marketers to gather information about their browsing behavior. But eliminating third-party cookies puts marketers in a tough spot. Their businesses have relied on cookies to find new customers for over two decades. 

Government agencies in the US and Europe have responded to consumer demands by enacting regulations that offer more protection and control to users over how their data is  collected and processed. And many of the web browsers have already phased out cookies. Google has been the last hold out and they’re expected to fully phase out cookies by the end of 2024. 

But simply eliminating cookies won’t solve the privacy protection problem for consumers. Digital footprints are always expanding and companies need to be more vigilant than ever about protecting their customers’ data. There’s an enormous opportunity to build an ad ecosystem that respects users' privacy more than ever.

Privacy Enhancing Technologies (PETs) have emerged as a crucial ally for safeguarding consumer data. This emerging technology uses advanced cryptographic and statistical techniques to protect consumer information while still allowing marketers to glean valuable insights.

What are PETs?

PETs are a set of tools and methods designed to help organizations maintain digital privacy. They provide a layer of defense against unwanted surveillance, data breaches, and unwarranted data collection by enhancing user control and safeguarding data during its lifecycle. PETs are instrumental in upholding privacy, security, and freedom in the digital realm.

There are several types of PETs being used throughout the digital advertising ecosystem:

1. K-anonymity 
2. On-Device Learning
3. Secure Computation
4. Trusted Execution Environment
5. Differential Privacy

PETs will play a vital role in creating an advertising ecosystem that is primarily privacy focused. Optable is exploring the use of multiple types of PETs as we build a privacy-safe environment where clients can safely collaborate with their data partners. The following blog series will demystify the complex world of PETs and take a closer look at how advertisers are using them.

The digital world has brought unprecedented convenience and connectivity but also raised significant concerns about data privacy. As we share more of our lives online, the need for robust privacy-enhancing technologies has become paramount. On-device learning has emerged as a powerful tool to protect personal data while enabling advanced capabilities. In this blog, we will explore on-device learning, its role in enhancing privacy, and how it’s used.

What is On-Device Learning?

On-device learning, sometimes referred to as federated learning, is a machine learning approach that allows training models directly on a user’s device with data available on their device. Only updated model parameters are sent to a remote server or cloud. This means that a user’s smartphone, tablet, or other device can learn and adapt to their preferences without constantly sending their data to remote servers. This gives users more control over their data, protects their privacy, and reduces the need to send raw individual user data to external servers.

How does On-Device Learning Work?

On-device learning operates with the following four principles:

1. Local Data Processing: Instead of sending your data to the cloud, on-device learning processes data directly on your device. This can include training machine learning models, recognizing patterns, or adapting to a user’s  preferences.
2. Privacy-Preserving Algorithms: Privacy-preserving algorithms ensure that only the updated model parameters leave the device. The user’s personal data remains on their device and is never exposed to third parties. 
3. Personalized User Experience: On-device learning allows a user’s device to provide a personalized user experience by understanding their preferences, habits, and requirements without compromising data privacy.
4. Offline Functionality: Due to local data processing, on-device learning enables a user’s device to adapt to their preferences immediately even when it's not connected to the internet. This ensures that the user can benefit from personalized features when they’re offline as well.

How are Marketers Using On-device Learning? 

With on-device learning, online retailers can gain insights on consumers’ preferences and behaviors without tracking their individual preferences. The way this works is, each consumer’s device downloads the current model, improves it by learning from the data on their phone. The model updates from each of these devices are then collected, compiled, and are fed back into and improved on the central model. Thus, the marketers just learn the overall purchase pattern or behavior without ever learning individual consumer preferences or behaviors. 

Let’s look at a real-world example of a data collection sequence that uses on-device learning:

1. A user’s web browser downloads a cross-sell prediction model from an advertising platform like Meta ads or Google Ads. 
2. The user clicks an ad and makes a purchase. Let’s say they clicked an ad for a smartphone and subsequently bought a smartphone as well as a screen protector. 
3. The model performs inferences from the purchase data without sending the data to the advertising platform server or cloud. 
4. The model gathers such inferences across millions of devices and compiles them to improve the advertising platform's central model. 
5. Over time, the model improves and can be used to find an increasingly specific audience for screen protectors.

On-device learning is not perfect from a privacy perspective. When model parameters leave users’ devices they still leak information about the underlying local training data. So, the risk of sensitive information being shared is only reduced and not completely eliminated.To prevent this, on-device learning is often combined with other PETs such as differential privacy and secure computation, which we will cover in different posts on our blog.

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In this blog, we will outline what audience activation is, why it is important for marketers, and how to activate audiences. We’ll discuss how publishers and advertisers can work together to connect data with technology like Google’s Ad Manager, Prebid.org, leading DSPs such as The Trade Desk and Amazon DSP, and major ad platforms like TikTok and Meta for activation purposes. 

What is audience activation and why is it important for marketing?

Audience activation, in the context of advertising, is the process of identifying and targeting a specific audience with relevant content and offers. It is important for marketers to use audience activation because it allows them to reach their target audience more effectively and efficiently. Audience activation can improve marketing campaigns by increasing brand awareness, driving leads, and generating sales.

Marketers today are faced with complex buyer journeys, exacerbated by the loss of third party data and cookie deprecation. To succeed, marketers must move away from the channel-first approach and create unified customer profiles with data from all available channels. With a centralized profile based on first party data, marketers can target audiences with just the right message, at the right time. 

By maximizing partnerships with media partners, marketers can look across consumer touchpoints to increase the effectiveness of activation. Technology can help organizations gain value from advertising partnerships while navigating challenges with data privacy. 

How to activate audiences with connected data 

Data is usually kept in a cloud environment like Snowflake, GCP, AWS or Databricks, and activation technology must be able to work with the data environment safely and with privacy in mind. 

Optable Collaborate is a Data Clean Room solution, fully interoperable across cloud environments. It utilizes leading privacy-enhancing technologies (PETs), and is purpose-built with advertising-specific frameworks and utilizes a simple pricing & activation model. 

Collaboration is enabled by tools like Optable’s DMP, which creates, segments and analyzes audience data before and after it is utilized within a clean room. Optable DMP provides an easy-to-use interface for commercial teams and plugs into a wide array of data sources, including real-time & event-level data, allowing you to scale & manage the importing, building, activation and measurement of audiences throughout all phases of advertising.

Once data is collated in a privacy-safe way, these are the steps to take to activation:

• Create, segment, and analyze your audience data. This will help you understand who your target audience is and what they are interested in.
• Deep dive into each audience segment. This will allow you to tailor your content and offers to each segment's specific needs.
• Form partnerships with publishers and advertisers. This will give you access to a larger audience and allow you to reach them more effectively.
• Connect your data with key advertising technologies. This will help you track the effectiveness of your campaigns and optimize your results.
• Control the security and privacy of your data. This is essential to ensure that your data is used responsibly and ethically.
• Analyze across audience and ad event data. This will help you understand how your campaigns are performing and make necessary adjustments.

Once audiences are activated effectively, with data-informed decisioning, marketing campaigns will become imminently more effective. To find out more, contact us for a demo. 

This week Google's announcement somehow managed to send shockwaves through the ad tech world.  In essence, they've confirmed what has been communicated between the lines for a long time: Google has no interest in helping other platforms in any way.  There is a clear path towards cutting out the competition, doing so under the promise of privacy and "greater good."

This is the greatest opportunity for adtech in a long time.

At Optable, our focus is on data connectivity for this new era in ad tech.  Our thesis is simple:

1. Privacy is now a feature in software, and that trend is not stopping
2. Legislation around personal data protection is going to continue to proliferate around the world

This is all leading to a world with more walled gardens that care deeply about their first party data, curating that data with direct consent provided by end-users.

As a result, the best way to compete with the incumbents is to work together on the basis of this data.  Prior to the erosion of global identifiers, this connectivity layer wasn't necessary. The whole ecosystem was stitched up using cookies and MAIDs.  Now, it is very much is tablestakes.

To make this collaboration easier while improving on trust issues, we are now offering new ways to connect data, ways that are anchored in security and privacy, using cryptography as an enabler.

The future is not going to lean on one identity framework that replaces global identifiers: the new ecosystem will be constructed using a patchwork of identity frameworks, operating in and around walled gardens, connecting data to each other without sacrificing users' privacy.

How exciting is that?

Why did I join Optable?

When did you first realise that GAFA (Google and Facebook, followed quickly by Amazon and Apple), had become so dominant in digital advertising that the very idea of a free and open internet was under threat? I was like a frog in increasingly tepid water, going about my mundane existence, until one day it felt like it was almost too late. 

I do believe the big tech platforms can be a force for good. Yet a world where journalism, content, commerce, entertainment, and even transportation is dominated by a small number of powerful companies starts to feel very dystopian very quickly; and while I do not believe their motives should be distrusted, I do believe their power should be checked. I'm not an advocate for government intervention in markets - I believe innovation and disruption can do the job. 

I would argue that there are three things which have enabled GAFA's dominance of the digital ads market. Centralised identity, aggregation of first-party data and a divided eco-system of brands, publishers, and intermediaries who have no choice but to conform and partner with the dominant players even if it is not in their best interest to do so. 

An open and fair digital ads market. Is, in my view, a vital component of a free and open internet. How can the supply and demand side of the digital marketing ecosystem embrace fragmentation, leverage de-centralisation and disrupt the incumbent players?  By creating a whole new paradigm based on cooperation, collaboration and mutually aligned interest.

Centralisation drove dominance for GAFA

Google, Facebook, and Amazon have built massive empires off the back of centralised identity structures on both the mobile and desktop internet. Recently, they’ve extended this to CTV and smart home devices,enabling them to aggregate more audience data than had ever been imagined possible.  Essentially, everything they see and touch becomes a valuable source of first-party data which can be used to drive outcomes for advertisers.

The ecosystem of independent publishers, media owners, agencies, and platforms are almost powerless in the face of this. Government intervention in the form of privacy legislation has inadvertently made matters worse by strengthening the centralised platforms while increasing fragmentation on the open internet and further dividing the ecosystem.

Decentralisation of identity and audience data is a force for disruption

The way to challenge dominant centralised structures is not with more centralisation. In the same way that defi is attacking the institutions of centralised finance, the digital marketing ecosystem can leverage fragmentation, encourage decentralisation, challenge the status quo, and create an entirely new paradigm for data-driven advertising. 

In order to turn decentralisation into an attack vector, independent media owners, brands, and mar-tech intermediaries must find ways to collaborate and share data which respect user privacy, preserve data sovereignty, ensure compliance, and enable activation at scale.

Introducing Optable: Decentralised Data Collaboration for the Digital Marketing EcoSystem - Built In Montreal

It's been said that we are seeing a renewed cycle of innovation in digital marketing - new platforms are emerging and new ways of working are being defined. Optable is not the first company to blaze this trail and I will offer a respectful tip of the hat to those that have been focused here ahead of us. Healthy competition and offering choice to clients will ultimately benefit the ecosystem as a whole.

Vlad Stesin, Bosko Milekic, and Yves Poire have assembled an experienced team of product designers and engineers to build Optable off the back of their experience as the founders of DSP and ad-serving platform Ad Gear, which became the foundation of Samsung’s CTV advertising business. With privacy, security, and data sovereignty at its core, Optable has been built for a connected TV world; the platform is a game changer and I could not be more excited to join the team as Chief Revenue Officer.

At Optable we view interoperability first and foremost through the lens of digital advertising’s critical systems. And when you consider the systems used for ad campaign planning, activation, and measurement, you quickly realize that these systems were all inherently interoperable for a long time thanks to widespread data sharing. With identity and data sharing on their way out for a variety of reasons, new ways of interoperating within each of these systems are required. Clean rooms are a way to achieve data interoperability in advertising, and that’s why we have invested significantly in this area.

But, the trouble with clean rooms is that both parties have to agree to use the same one in order to interoperate. The central idea with clean room technologies is that two or more parties come together around a neutral compute environment, enabling them to agree on operations to perform on their respective datasets, on the structure of their input datasets, on the outputs generated by the operations and, importantly, on who has access to the outputs. Additionally, various privacy enhancing technologies may be used to limit and constrain the outputs and the information pertaining to the underlying input datasets that is revealed.

So, what does true interoperability look like for data collaboration platforms, built from the ground up for digital advertising? Here are three important pillars:

✅ Integration with leading DWH clean room service layers. A DWH clean room service layer is the set of primitives (APIs and interfaces) made available by leading DWHes (Google, AWS, Snowflake, etc), that enables joining of disparate organization datasets, and purpose limited computation. Optable streamlines this by automating the flow of minimized data to/from DWHes, and by federating code to these environments. The end result? A collaborator with audience data sitting in Snowflake can easily match their audience data to an Optable customer's first party data, all within Snowflake using Snowflake DCR primitives to enable trust, without the Optable customer lifting a finger. In this example the matching itself happens inside of Snowflake, but the same thing can be done with other DWH clean room service layers as well.

✅ Compatibility with open, secure multi-party compute protocols like Private Set Intersection (PSI). What if your partner wants to match their audience data with you but they cannot move their data into a cloud based DWH? SMPC protocols such as PSI enable double blind matching on encrypted datasets, without requiring decryption of data throughout. Open-source implementations provide an independently verifiable, albeit purpose constrained clean room service layer. The end result? A collaborator with audience data sitting on premise can execute an encrypted match with an Optable customer using a free, open-source utility.

✅ Built-in entity resolution, audience management and activation, with deep integration to all major cloud and data environments. In the real world, few organizations have all of their user data assets neatly connected in a single environment. Sure, they exist, but more often than not, organizations need to do quite a bit of work to gather, normalize, sanitize, and connect their user data so that they can effectively plan, activate, and measure using data collaboration systems. It’s therefore no wonder that when the IAB issued their State of Data report earlier this year, respondents cited time frames of months up to years to get up and running with clean room tech! Moreover, even when one company has got their user data together, their partners often require help with entity resolution. These are the reasons why Optable makes it easy to connect user data sitting in any cloud environment or system into a cohesive and unified user record view, out of the box, with no code required. Got part of your user data in your CRM? And another sitting in cloud storage? And another in your DWH? No problem.

At Optable, we believe that these pillars are the groundwork on top of which interoperability can happen, and we’re partnering with industry peers who share the same vision. Stay tuned for more exciting announcements on this front!

The culling of the cookie. Increasing consumer awareness. The realization that third-party data isn’t all that effective. All these factors have slowly but surely driven advertisers to implement alternative targeting solutions.

One such alternative is data clean rooms (DCRs).

The problem is that while they are a viable solution, most traditional DCRs still operate as third-party databases, meaning that users have little to no control over what is being done with their data.

The solution? A new generation of privacy-preserving data collaboration software has emerged that is able to provide advertisers with DCRs that measure and match overlaps in data - all without infringing user privacy.

But why should the industry pay attention, and what are the benefits of leveraging this new software?
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1. A purpose-limited environment for advertisers

The key here is the phrase ‘purpose-limited.’ These privacy-preserving DCRs are created with advanced cryptography that minimizes data leakage, providing a purpose-limited environment for advertisers in which to work. 

This means users have to explicitly consent to their data being used for things such as analysis, activation or measurement. 

Since these DCRs are limited to the purposes for which the users have consented, they not only give advertisers an opportunity to analyze, activate and measure data - but also to protect the privacy and sovereignty of user data. 
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2. Maximum collaboration opportunities for publishers 

Next-generation DCRs stand out for their frictionless collaboration and interoperability capabilities.

In Optable’s case, for instance, only one side of the match needs to be the company’s customer - the other partner can be from any organization, opening up much wider opportunities for collaboration.

The only thing the publisher needs to do is create an identity graph. Once this is set up, they can start collaborating with a number of different partners.

Publishers can invite these partners to join the DCR by either:

1. utilizing Optable’s open source utility to encrypt their data at source, regardless of the system it sits in - and executing a multi-party computational protocol with their own data set
2. working with other industry partners such as cloud data warehouses, to allow brands using their services to leverage the DCR without their data ever leaving the data warehouse.
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3. Adapting to everchanging consent statuses

A key stand-out for this new breed of DCRs is their ability to collect and push out data in real time.

As well as leveraging privacy-enhancing technologies, DCRs such as Optable have also built real-time programmatic workflows around these technologies. This means they are not only purpose-limited, but are also able to keep up with users’ changing consent statuses.

If, for example, a user who has consented to analytics withdraws that consent at a later stage, Optable is able to gather that information in real time and remove the user from a clean room immediately.
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4. Activating data on both the buy-side and sell-side 

By using our ‘data collaboration nodes’, we can ensure that data sets from different partners are physically decentralized from one another. This means, for instance, ensuring that data from the buy-side and sell-side is never merged, and stays inherently separate within the clean room.

Audiences can still be activated and targeted directly outside of the clean room, but none of the data is pushed into the open bitstream or connected to a third-party ID - ultimately preserving the integrity of the DCR.

This is important as it means that brands are able to activate and measure their data - on both sides of the coin - without compromising on privacy standards.
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5. Privacy-centric activation 

As well as activating data, brands can also schedule data matches with partners to look at the overlap between publisher data and advertiser data, for example. These create a matched audience over time that brands can analyze to gain useful insights into their customers, enabling them to target them more effectively.

These insights can include specific traits within a customer base that a brand or advertiser might not have known about their audience before. And this can all be done without ever pushing any of the data out of the DCR.

Our publishers, advertisers and brands can effectively send in first-party cookies (and other non-matchable first-party identifiers) that we use to produce a key value. This is then pushed directly back into Google Ad Manager or any other ad server. This allows publishers to invite advertisers into their own DCR without the data leaving its original source - all the while being able to activate campaigns and target their first-party data.

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In the age of cookieless, Optable provides customers with a powerful privacy-preserving tool that can match publishers to advertisers and activate audiences in real time. Request a demo today and see how our DCR technology can help you collaborate with ease.

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Photo by Timon Studler on Unsplash

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One of the most common misconceptions about data clean rooms and data collaboration is that there are requirements on having tons of identified data. 

Most publishers we meet have this concern:  “Do we really have enough data to drive significant revenue? Won’t we be limited by the size of the match, and therefore won’t be able to run any media at scale?“

Typically they are surprised to learn that mitigating low volumes of identified data is part of the solutions offered today by this class of data collaboration technology.

No matter how little identified data any given publisher has, they can benefit from growth using data collaboration technologies. The reason is quite simple:  any campaign is better off when it starts with real data. 

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Unlocking Audience Insights and Prospecting Powers

Following a match with an advertiser, the publisher has a few options: one, a simple one, is simply to have insights on the matched audience. The publisher can better understand the brand’s customers or prospects as a function of their own data, which in turn allows them to create better media products.  It also shows the brand that the publisher reaches the right audience for them.  Insights are offered as a report that provides aggregate numbers – by definition, it is a privacy-safe product. 

The second, and an important one, is the possibility of creating a prospecting audience out of the match.  Optable’s prospecting clean room app automatically creates an expanded audience that provides scale, performance and value when it comes to reaching the right audience.  Not only that, but we do it in a privacy-safe manner, since the publisher does not learn the intersection – only the prospecting audience becomes eligible for targeting. 

Considering that a publisher’s audience consists of both identified and unidentified users who share a number of traits, Optable prospecting clean room app allows a publisher to configure a model that ultimately creates an addressable audience that is sizable enough to drive significant growth. 

For brands, the use of customer or prospect data also doesn’t have a limiting factor – in fact, there are few brands that can boast having significant data on all their customers. For everyone else, the objective is to have some data – enough to allow our systems to make better audience decisions. 

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Optable’s approach

We make publisher-driven data collaboration easy for all parties:  our end-to-end solution includes direct integration for activation straight from the clean room environment, and offers frictionless interoperability. 

Given the emergence of retail media and the democratization of data through data warehouse clean room APIs, data collaboration is quickly becoming a major revenue opportunity. 

Forward-looking publishers who are looking for revenue growth must prioritize future-proof, privacy-safe solutions to driving revenue.