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Identity resolution is a process of reconciling different customer records to create a single view of the customer.
There are two main types of identity resolution: deterministic and probabilistic methods.
Identity resolution is crucial for creating a unified customer view by reconciling data from various sources.
Leveraging identity resolution enhances customer experiences through personalized interactions and consistent communication.
Identity resolution is a process of reconciling different customer records to create a single view of the customer. The approach entails taking appropriate measures to ensure that customer data — typically coming from a broad range of sources — is correctly associated with the corresponding individual.
For instance: A customer may interact with a brand through social media, by making a purchase on their website, or by later contacting customer service with a follow-up question about their purchase. Effective identity resolution ensures these disparate data points don’t get lost in the noise, but instead empower the brand to “connect the dots” — yielding a rich trove of actionable customer data.
By compiling data into composable customer data platforms (CDPs) or data warehouses, businesses can take a number of different approaches to resolving this information into a single view of the customer.
Generally speaking, approaches to identity resolution fall into one of two categories: deterministic or probabilistic.
Deterministic identity resolution (deterministic matching) relies on clear, identifiable information — such as an email address or phone number — to accurately match relevant data to the correct customer. A deterministic method might call for identifying shoppers by their email address when linking a purchase to an individual’s record.
Probabilistic identity resolution (probabilistic matching) employs statistical algorithms that link data together based on a variety of less definitive indicators, such as browsing behavior or device usage.
Both identity resolution methods have their benefits and drawbacks. While deterministic matching methods may be precise, their reliance on exact matches can be potentially limiting — in other words, some data may be mismatched or overlooked in the case of a misspelled customer name or change of address. Probabilistic methods, while less accurate, may discover more meaningful connections through algorithmic analysis or third-party data sets like LiveRamp or TransUnion.
That being said, you can leverage both methodologies based on data availability and the specific needs of your brand or marketing team.
Data onboarding is a pivotal step in the identity resolution process. It involves securely transferring offline customer data — such as in-store purchases — via secure transfer protocols and data integration tools to an online environment. Users may use platforms such as customer data platforms and data management platforms to facilitate and ensure the security of this process.
This transition enables brands to have a universal view of the customer, connecting matched IDs to customer profiles and validating these connections for accuracy. From there, deterministic and/or probabilistic matching techniques can link and evaluate data, effectively integrating it into CDP.
The growing emphasis on data regulation, as well as increasing consumer demand for data transparency and security, has elevated the importance of identity resolution in ensuring privacy and compliance.
Thankfully, identity resolution aids in the creation of consolidated customer records, a crucial component of compliance under data protection laws like the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA). Moreover, these consolidated records can be instrumental in fraud prevention by providing reliable identity verification, enabling real-time fraud alerts through anomaly detection.
The success of an organization’s identity resolution strategy is often influenced by the data quality and source. It relies on two primary types of data: first-party and third-party data.
First-party data is information directly conveyed to the organization by the customer — such as transaction history, website interactions, or provided contact information. It’s generally considered both reliable and valuable in identity resolution, more accurately reflecting customer behaviors and preferences.
Using first-party data is a great way to promote better data governance, ensuring compliance with various digital privacy laws, such as Europe’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
Customer data infrastructure tools like Segment, MetaRouter, and Rudderstack can help support and organize first-party data.
Third-party data is information obtained from external sources, who may or may not have directly engaged with the customer. This data can include website tracking cookies. While this data can expand an organization’s understanding of their customers, its accuracy may be less reliable. Using third-party data is also becoming less common in large part due to the aforementioned privacy laws.
LiveRamp and TransUnion are two technologies that can help organize and support third-party data.
While identity resolution and entity resolution both work to aggregate customer data, they do so at different levels of detail.
Identity resolution focuses on collating data at the individual level, offering marketers a close-up view of their customer interactions across various channels and touchpoints. When combined with a detailed analysis, this customer 360 approach enables brands to provide more tailored, targeted engagement to their users — ideally increasing their satisfaction and repeat purchases.
Conversely, entity resolution operates at a broader scale. It often aggregates data around households, accounts, or devices. Unlike identity resolution, which targets individual customer interactions, entity resolution reconciles data records pertaining to broader entities — for instance, your office’s GrubHub account or your family’s shared Netflix account. Grubhub, for example, may employ this approach when analyzing your office’s collectively preferred meal times, restaurant choices, etc., in order to determine the best time, approach, etc. for its targeted marketing.
Both processes can be crucial tools for any business creating accurate, actionable profiles or groupings for their customers.
With a solid identity resolution strategy, organizations are better positioned to navigate the ever-evolving demands of the modern consumer. As prospects increasingly expect tailored experiences from their preferred brands, identity resolution becomes an indispensable tool to avoid missteps and seize opportunities with their customers.
By accurately tying a wide range of data points to individual customers, brands are empowered to provide engaging and personalized experiences that will build relationships, brand loyalty, or additional purchases. Identity resolution also avoids sending irrelevant or spammy messages that may discourage a purchase or interaction. This is especially important in industries like finance and healthcare, where you must avoid sending sensitive information to the wrong individual.
Beyond providing a comprehensive view of each customer, organizations stand to see several key benefits from an identity resolution strategy.
Personalized interactions - Create offers and tailored marketing messages based on individual preferences and past behaviors.
Consistent communication - Maintain uniform communications and conversations across various channels, building brand value and recognition.
Data privacy compliance - Adhere to data privacy regulations by managing customer data responsibly.
Fraud detection - Potentially identify fraudulent activities with inconsistencies in customer data.
Streamlined processes - Reduce data redundancy with a unified view of customer data
Accurate attribution - Credit sales and interactions to the right marketing channel, identifying top-performing areas of your customers’ journey.
Customer insight - Gain deeper insights into customer behaviors that can inform your engagement strategy.
Market responsiveness - Empower your brand to react swiftly to market trends and customer feedback — keeping ahead of the competition.
Your organization can leverage identity resolution strategies across teams to manage data, improve operational efficiency, and enhance the customer or user experience. Some notable examples include:
Customer segmentation - With a comprehensive customer profile, you can segment customers based on behavior, preferences, and past interactions to tailor marketing strategies, which is helpful in both B2B and B2C contexts.
Customer retention - Use behavioral data to identify at-risk customers and develop targeted campaigns to retain them.
Risk management - Effectively calculate and manage risk by accurately identifying customers and their financial behaviors. Identity resolution can help ensure that each financial decision is informed by a full spectrum of customer data.
Fraud detection - Provide an added layer of protection when verifying customer identity, a crucial safeguard against identity and other kinds of financial fraud.
Inventory management - Align inventory levels and product availability based on preferences identified in your customer profiles.
Patient identification - Ensure your clinician team can recommend screenings or appointment recommendations to the right patient by accurately matching patient records.
Billing accuracy - Improve billing accuracy and reduce administrative errors by ensuring correct patient identification.
Identity verification - Guard against identity fraud by using multiple data points to verify the identities of individuals accessing services or benefits.
Student services - Schools can employ identity resolution to combine numerous information points across academic, administrative, and behavioral data sources to create personalized learning plans, experiences, and more.
Alumni engagement - The same approach can be taken with alumni relations: track their interactions, donations, and more to create a stronger, more responsive alumni network.
Enhanced guest experience - Taking a 360-degree view of your customer will empower your business to surprise and delight them throughout their visit: restaurants and hotels can review past guest amenities, food preferences, room options, and more. These can also be used to create a robust digital loyalty program for your regular customers.
Identity graphs are a direct representation of your identity resolution strategy. It’s a database for all customer IDs, touchpoints, and behaviors. It also includes the logic that brings those touchpoints together. It’s a web of a customer’s personal information and trackable behavior.
After obtaining a “close-up” view of your customers, identity graphs allow you to map your findings into a structured framework. These “graphs” store information such as email addresses, physical addresses, mobile ad IDs, loyalty numbers, engagement history, and more, about individual customers.
Ssometimes called “identity spines” or “identity charts," identity graphs are often stored in a cloud data warehouse. Each customer’s data is compiled in a separate table or node in that database.
By using identity graphs, marketers can tailor messages that resonate with individual preferences and behaviors, significantly enhancing engagement and conversion rates.
Comprehensive identity graphs provide a 360-degree view of individual customers and prospects. That means marketers can use identity graphs to build customer personas and user profiles that ultimately inform:
Audience segmentation and personalized experiences
Targeted messaging
Advertising, both traditional and digital
Identity resolution initiatives can pose a number of challenges — especially at the implementation stage.
Data quality and accuracy - Ensuring data integrity when collecting from multiple sources can be daunting — make sure you’re taking steps like frequent information audits to ensure that your identity resolution process is proceeding according to plan.
Data privacy regulations - Navigating the complex landscape of data privacy regulations is a complicated but essential element to avoiding any thorny legal battles over customer privacy. This, naturally, involves staying aware of, and compliant with, any relevant legal provisions related to customer data collection in your markets.
Data integration and management - Integrating disparate data sources is a complicated undertaking, and the reins can be easily lost without regular spot-checking and employee education about these tools.
Interested in implementing an identity resolution strategy for your business? Here are some simple steps to review before getting started:
Engaging key stakeholders - Collaborate with stakeholders across marketing, IT, legal, and data management teams to ensure your goals are aligned, and that everyone is on the same page regarding the plan’s execution.
Data assessment and cleanup - Conduct a thorough data assessment to ensure accuracy and consistency across your various “sources of truth.”
Technology and vendor selection - Carefully consider and choose the right technology and vendors that align with your organization's needs and scale.
Continuous monitoring and optimization - Implement regular data audits in order to maintain data accuracy and legal compliance.
This approach aids in navigating the challenges posed during implementation, unlocking untapped value from customer data, enhancing customer engagement, and driving better-informed business decisions.
The identity resolution tools you use will depend on where your data is stored. If your data lives in a traditional (packaged) CDP, the CDP will likely have built-in identity resolution tools. The downside to this setup is that you may not have the ability to customize identity resolution rules based on your company's needs and goals.
If your data is stored in a data warehouse, you can lean on your in-house data team and SQL to create identity resolution logic — either deterministic, probabilistic, or both. There are also tools that handle identity resolution inside your data warehouse, such as Zingg and Truelty.
But if you don’t have a data team at your disposal, some composable CDPs offer identity resolution tools that tap into your data warehouse. These tools are typically customizable, so you can match the identity resolution rules to your business needs.
GrowthLoop’s identity resolution model is built around keeping your data in your cloud data warehouse. Book a demo to learn how our always-on, transparent identity process can enhance your marketing campaigns and grow revenue for your business.
