Think about the last time a customer complained about seeing an ad for something they just bought. Or a loyal customer who received a first-time buyer offer because your systems didn’t recognize them across channels.
These aren’t edge cases. They’re symptoms of broken identity data.
In 2026, with 54% of mobile impressions and 36% of desktop impressions lacking identifiers, according to Comscore, connecting customer data across devices and channels is now one of marketing’s most pressing technical challenges. When identity breaks down, personalization fails, suppression fails and measurement becomes unreliable.
That’s why identity resolution platforms moved from a niche martech category to a foundational piece of the marketing stack.
Our newly released and fully updated report, “Identity Resolution Platforms: A Marketer’s Guide,” shows how marketers are approaching this problem and the trends shaping the market today. Several developments stand out.
Data clean rooms are a core identity infrastructure
Clean rooms used to be niche tools reserved for large enterprises managing co-marketing data partnerships. In 2026, they’ve become core infrastructure for collaboration.
Clean rooms allow brands, publishers and measurement partners to combine datasets without exposing raw personally identifiable information. Instead of sharing data directly, participants run queries inside secure environments that enforce privacy rules.
Cloud platforms such as Google BigQuery, Snowflake and Databricks now offer native clean room capabilities that support secure multi-party joins.
Identity vendors responded by building clean room functionality directly into their platforms rather than offering it as an add-on.
Recent M&A activity reinforces the shift. WPP acquired data clean room provider InfoSum in April 2025, while Publicis acquired identity platform Lotame in March 2025, combining identity assets that now reach roughly four billion global profiles.
For enterprise marketers, clean rooms are increasingly where cross-company identity collaboration occurs.
Machine learning now performs much of the identity matching
Artificial intelligence and machine learning play a major role in identity resolution, even if they receive less attention than generative AI.
Identity platforms use machine learning models to analyze large datasets and identify connections between records that don’t match exactly. For example, a system can determine that “Michael Smith” in one dataset and “Mike Smith” in another likely represent the same individual.
Rather than relying only on manually defined rules, machine learning models calculate the probability that different data signals belong to the same person based on historical patterns.
Track, optimize, and win in Google and AI search from one platform.
Natural language processing can also extract identity signals from unstructured sources such as emails or social posts.
As generative AI becomes more deeply embedded in marketing workflows, identity resolution is becoming a prerequisite for effective AI-driven personalization.
Real-time identity resolution is replacing batch processing
Identity resolution is moving from batch updates to real-time processing.
Historically, identity systems refreshed customer records in scheduled batch cycles. Audience segments were exported periodically and pushed to downstream platforms, often leaving the data outdated by the time it was used.
Modern identity platforms increasingly resolve identities during customer interactions using streaming architectures 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 universal ID dream is now a multi-ID reality
When third-party cookies began to decline, the industry hoped a single privacy-safe universal identifier would replace them.
Instead, multiple identity frameworks now coexist.
Platforms such as Unified ID 2.0 from The Trade Desk, RampID from LiveRamp, ID5’s Universal ID and Lotame’s Panorama ID each operate within their own ecosystems, with limited interoperability between them.
Track, optimize, and win in Google and AI search from one platform.
Marketers must now support multiple identity frameworks to maintain reach across advertising and marketing environments.
The breadth of ID integrations and ecosystem connections is an important consideration when evaluating vendors.
What this means for marketers evaluating identity platforms
Identity resolution is now a foundational capability of modern marketing infrastructure.
As identifiers disappear and privacy rules evolve, organizations increasingly depend on identity platforms to connect customer signals across channels and systems.
Understanding how vendors support clean rooms, machine learning, real-time processing and multiple ID frameworks can make a major difference in platform selection.
MarTech’s report “Identity Resolution Platforms: A Marketer’s Guide” explores these topics in depth. The report includes vendor capability tables across 12 platforms, pricing guidance and a step-by-step buying framework for marketers evaluating identity solutions.
It also includes a companion podcast and an AI-powered chatbot designed to help marketers get answers tailored to their organization’s specific use case.
The 2026 MarTech Intelligence Report, “Identity Resolution Platforms: A Marketer’s Guide,” goes deeper into all of this — with capability tables across 12 vendors, a step-by-step buying guide, pricing guidance, and full vendor profiles. You can also catch the companion podcast for expert commentary, or use our AI-powered chatbot to get answers tailored to your organization’s specific use case. Access all three here!
The post The 5 trends reshaping identity resolution in 2026 appeared first on MarTech.