The capabilities of identity resolution platforms

What should you expect when licensing an identity resolution platform?

Identity resolution platforms support marketing processes around targeting, measurement and personalization for both known and anonymous audiences across digital and offline channels.

Virtually all of the enterprise identity resolution platform vendors offer the following core features and capabilities:

  • Data onboarding (including online/offline matching).
  • Proprietary identity graph.
  • Client ownership of first-party data.
  • Persistent individual and/or household ID.
  • Compliance with privacy regulations.
  • APIs for third-party system integration.

Vendors begin to differentiate their platforms by offering more advanced features, sometimes requiring additional investment, which include – but are not limited to – the following:

  • Match confidence scoring.
  • Private (first-party) and/or second-party cooperative identity graphs.
  • Pre-built connections to martech/adtech platforms. The following section examines these features and capabilities in more detail, and the key considerations involved in choosing an enterprise identity resolution platform.

Data onboarding

Data onboarding is the first step in the identity resolution process. Client data is typically onboarded via secure file transfer (SFTP), although several vendors profiled in this report also provide direct API transfer or pixel syncs. Data is processed with the goal of establishing a universal view of the customer and includes the following:

  • Matching individual identifiers in the identity graph (see below) to associate the customer with their interactions across touch points, particularly online to offline.
  • Suppressing unresolved IDs and interaction data for potential future use.
  • Hashing or tokenizing personally identifiable information (PII) with an anonymized customer ID.
  • Linking matched IDs to a universal ID representing the customer profile and all of its associated attributes.
  • Validating the accuracy of matches to a pre-established “truth set” of referential data known to be precise and accurate.

Virtually all of the vendors provide persistent customer IDs during the identity resolution process, which means the ID follows the individual (or household) even as identifiers change, which they inevitably do. For example, when browser cookies expire or are deleted or customers buy and use new devices, the customer ID will remain the same. Persistence is also critical to enabling temporal time-series analytics, such as churn analytics.

Matching algorithms differ among vendors, with matches established via probabilistic or deterministic methods or a combination of both. Deterministic matching relies on explicit links between identifiers, such as an email address that is used to sign in to a website or mobile app and can be associated with the resulting cookie or mobile ad ID (MAID). Probabilistic matching relies on implicit links between identifiers, such as a desktop cookie and MAID both associated with a residential IP address. The goal is to consider multiple signals like location and browsing history.

Both approaches have their pros and cons, which should be considered when choosing an identity resolution platform. Deterministic matching takes an omnichannel view that attempts to connect identifiers across digital and offline interactions. It can be difficult to scale and prone to inaccuracy. Probabilistic matching can “weed out” inaccurate data because it looks at a variety of data points versus binary matches. Its drawback is that it is limited to online touch points. Some vendors are using hybrid identity resolution approaches, which try to compensate for deterministic and probabilistic weaknesses while capitalizing on their advantages. It uses deterministic and probabilistic linkages, and then merges the two linkage sets together to form new, combined clusters.

Many vendors provide their overall match rates to potential clients. A few vendors go a step further, providing clients with customizable match algorithms or confidence scores (how likely the matches are accurate) based on their specific first-party customer data and data quality profiles. For example, a pure online organization may rarely use postal addresses and likely have lower quality address data than an organization that relies on fulfillment to a physical shipping address. Addressability is another factor that can help marketers measure their match accuracy by assessing the number of consumers that can actually be contacted.

Identity graph

Every identity resolution vendor profiled in this report maintains a proprietary identity graph or database that houses all the known identifiers that correlate with individual consumers. There is no standard model for an identity graph. Each vendor differs in the types of foundational PII used, the matching methods employed and the non-PII integrated to enrich the individual profiles. Across the buyer’s journey, many identifiers can be associated with an individual, including email addresses, physical addresses, landline and mobile phone numbers, mobile ad and device IDs, account usernames and loyalty numbers. The identity graph collects these identifiers and links them to customer profiles, which are used to target and personalize marketing messages.

Identity graphs may also incorporate demographic, behavioral, financial, lifestyle, purchase and other data compiled or licensed from third-party sources, such as online news sites, purchase transactions, surveys, email service providers (ESPs), motor vehicle records, voter registration and other public records. Having all of this customer device, channel and behavioral data in one place allows brand marketers to more accurately measure the reach and frequency of their campaigns, and analyze how different ads and marketing tactics perform across channels.

In response to the dwindling availability of third-party cookie data and the increasing use of consumer privacy tools, such as advertising and location blocking apps, several identity resolution platform vendors are offering new identity graphs built on first-party or second-party datasets.

First-party identity graphs are exclusively used by a brand to house and match known customer data. Second-party identity graphs use cooperative data-sharing agreements between multiple brands or publishers to create common, anonymized identity assets. Participating organizations can build, plan, activate and measure custom audience pools to either target or suppress customers across addressable media.

Privacy compliance and data ownership

Marketers with customers in the European Union have had to comply with GDPR since May 2018. The CCPA, impacting all brands with customers residing in California, went into effect in January 2020, and empowers consumers to make a Subject Access Request to see all the data an organization has about them, which raises the stakes of identity resolution match accuracy. CCPA defines personal information as anything that can be associated or linked with an individual or household.

Marketers in the highly regulated healthcare market must follow Health Insurance Portability and Accountability Act (HIPAA) and Health Information Technology for Economic and Clinical Health Act (HITECH) regulations. In addition, all organizations that accept, process, store or transmit credit card information must maintain a secure environment that meets Payment Card Industry Data Security Standards (PCI DSS), as well.

These regulations are driving an expanded industry focus on data transparency and consumer consent, with a view toward complying with new standards for the benefit of consumers, as well as marketers. Many identity resolution platform vendors adhere to advertising industry guidelines from the Digital Advertising Alliance (DAA) or Interactive Advertising Bureau (IAB).

Lastly, and importantly, the majority of vendors profiled in this report allow enterprise brands to retain ownership of their first-party data.

Third-party software integration

The ultimate marketing goal for identity resolution is to support and enable data activation by pushing segmented audiences into highly personalized campaigns through a variety of martech (CRMs, DMPs, marketing automation platforms, ESPs, etc.) and ad tech (DSPs, SSPs, ad exchanges, etc.) tools and platforms.

Identity resolution platforms should be able to streamline integration with the client’s martech and ad tech ecosystems by providing pre-built (or native) connections and an extensive set of APIs for custom integrations. Access to these APIs may or may not be included in base pricing.


Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.


About The Author

Karen Burka, Senior Research Consultant, conducts research and provides analysis for Third Door Media’s Martech Intelligence Reports. She has worked as a digital marketer and industry analyst for more than 20 years at companies such as Peppers and Rogers Group, Cowles Business Media and Simon & Schuster.

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