Brands know interactions that feel personal and relevant to customers are key to driving conversions in our oversaturated digital environment. However, as signal loss continues, brands face significant challenges in maintaining effective reach, targeting, and measurement in their marketing efforts, and now is the time to get your first-party data strategies in order.
Without the signals third-party cookies send, it’s harder for companies to understand how customers interact with their brands across the digital ecosystem and owned channels. Specifically, charting customer journeys beyond the original touch point — for example, an email nurture campaign — becomes more difficult. As time goes on, the signal grows faint, meaning brands urgently need to adopt more robust mechanisms for reaching and engaging customers.
Enter identity resolution.
Identity resolution is the ability to accurately resolve consumer data from a variety of sources to a household or an individual in a privacy-centric manner. Identity should be the foundation of everything you do across your enterprise. As third-party signals fade, a robust identity strategy enables precise and personalized customer interactions, ensuring your marketing efforts build brand loyalty and drive conversions.
What is identity resolution?
Identity resolution connects the dots between consumers’ digital footprints to give the full picture of their online behavior across devices, channels, and touchpoints. It’s a key component of effective data collaboration, promoting consistency and accuracy across multiple platforms and partners.
Identity resolution uses advanced algorithms and machine learning techniques to accurately link disparate pieces of data to the correct individual and create a comprehensive picture of a user’s interactions and behaviors across various platforms. By resolving identities across channels and devices, it becomes possible to recognize customers any time they interact with your brand. This helps to prevent signal loss so you can continue to tailor campaigns to customers’ behaviors, preferences, and interests without using third-party cookies.
How does identity resolution work?
Identity resolution combines data management strategies and advanced analytics to prevent signal loss and establish consistent identifiers across customer journeys.
Key elements of identity resolution include:
Pseudonymous data
Pseudonymous data includes data points that don’t directly identify an individual, but that can be used to gain a sense of a person’s online activities and preferences. For example, a Mobile Ad ID (MAID) is a unique identifier created by a mobile device’s operating system and shared with apps downloaded to the device. A MAID can be used to “remember” an individual’s behavior and choices without using their personally identifiable data (PII). Identity resolution collects and connects these pseudonymous datasets, then resolves them down to individuals, each of whom is assigned a unique identifier that can’t be traced to a specific person.
Offline data
Offline data comprises PII, such as an individual’s name, postal address, email address, and telephone number. This data is compiled over time through continuous customer interactions and stored in an offline reference graph.
Resolution
The resolution process uses advanced algorithms and matching logic to accurately link these disparate data sources to create consistent identities. Resolution can be done at either the individual or household level depending on what makes sense for your purposes. In both cases, resolving identities enables you to personalize customer experiences across channels and interaction points.
Deterministic match
Deterministic matching links different identifiers to known individuals’ PII. For example, when someone starts a new job, deterministic matching can link their new work email with accounts and activity associated with their old one. For the most accurate match and widest reach, this step should use a third-party offline reference graph.
Probabilistic match
Probabilistic identity methodologies complement deterministic matching to create a fuller picture of online identity. Devices can be implicitly grouped by data points such as IP address, operating system, Wi-Fi network, and location. Probabilistic matching uses statistical modeling to understand these groupings and assign them to identities at varying confidence levels.