Think about the last time a customer complained about seeing an ad for something they just bought. Or a loyal customer who received a consumer offer for the first time because your systems didn’t see it across channels.
These are not crimes. They are symptoms of broken identity data.
By 2026, with 54% of mobile and 36% of desktop content without identifiers, according to Comscore, connecting customer data across devices and channels is now one of marketing’s most pressing technical challenges. When identity is broken, personalization fails, repression fails and measurement becomes unreliable.
That’s why proprietary solution platforms have moved from the niche martech stage to a core part of the marketing stack.
Our newly released and comprehensively updated report, “Identity Resolution Platforms: A Marketer’s Guide,” shows how marketers are tackling this issue and the trends shaping the market today. There are several notable events.
Clean data rooms are corebuild the ownership infrastructure
Clean rooms used to be niche tools reserved for large enterprises managing collaborative marketing data relationships. By 2026, they have become a cooperative infrastructure.
Clean rooms allow brands, publishers and measurement partners to aggregate data sets without exposing raw personally identifiable information. Instead of sharing data directly, participants conduct inquiries within secure environments that enforce privacy laws.
Cloud platforms such as Google BigQuery, Snowflake and Databricks now offer clean room capabilities that support secure multi-party joins.
Identity vendors have responded by building cleanroom functionality directly into their platforms rather than offering it as an add-on.
Recent M&A activity reinforces the change. WPP acquired data cleanroom provider InfoSum in April 2025, while Publicis acquired identity platform Lotame in March 2025, encompassing identity assets that now total nearly four billion global profiles.
For corporate marketers, cleanrooms are increasingly becoming a cross-company ownership interaction.
Machine learning now does a lot of identity matching
Artificial intelligence and machine learning play a major role in identity solutions, even if they receive less attention than manufacturing AI.
Proprietary platforms use machine learning models to analyze large data sets and identify connections between disparate records. For example, the system can determine that “Michael Smith” in one dataset and “Mike Smith” in another may represent the same person.
Rather than relying solely on manually defined rules, machine learning models calculate the probability that different data signals belong to the same person based on historical patterns.
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Natural language processing can also extract identity signals from unstructured sources such as emails or social posts.
As productive AI is embedded deeper into marketing workflows, identity optimization becomes a prerequisite for effective AI-driven personalization.
Real-time identity correction replaces batch processing
Identity processing is moving from batch updates to real-time processing.
Historically, ID systems updated customer records in scheduled batch cycles. Audience segments were periodically posted and pushed to downstream platforms, often leaving the data out of date by the time it was used.
Modern identity platforms are increasingly resolving identities during customer interactions using distributed infrastructure and edge computing.
Many vendors now provide real-time APIs that allow personalization engines, advertising systems and experience platforms to query identity graphs on demand.
That change allows marketers to respond to customer behavior immediately rather than hours or days later.
The dream of universal ID is now a multi-ID reality
When third-party cookies begin to decline, the industry hopes that a single, universally protected privacy index will take their place.
Instead, many ownership structures have now merged.
Platforms such as Unified ID 2.0 from The Trade Desk, RampID from LiveRamp, ID Universal ID by ID5 and ID Panorama by Lotame each operate within their own ecosystem, with limited interoperability between them.
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Advertisers must now support multiple ownership structures to maintain access to all advertising and sales environments.
The scope of ID integration and ecosystem connectivity is an important consideration when evaluating vendors.
What does this mean for marketers testing proprietary platforms
A proprietary solution is now a core capability of the modern marketing infrastructure.
As identifiers disappear and privacy laws change, organizations increasingly rely on identities to connect customer signals across channels and systems.
Understanding how vendors support cleanrooms, machine learning, real-time processing and multiple ID frameworks can make a big difference in choosing a platform.
MarTech’s report “Identity Resolution Platforms: A Marketer’s Guide” explores these topics in depth. The report includes vendor strength tables across 12 platforms, pricing guidance and a step-by-step buying framework for vendors evaluating proprietary solutions.
It also includes a companion podcast and AI-powered chatbot designed to help marketers find answers that fit their organization’s use case.
MarTech Intelligence Report 2026“Identity Resolution Platforms: A Marketing Guide,” dives deep into all of this – with capability tables for all 12 vendors, a step-by-step buying guide, a pricing guide, and complete vendor profiles. You can also catch a companion podcast for expert commentary, or use our AI chatbot to get answers tailored to your organization’s specific use case. Access all three here!
