Customer identity data is central to building your brand, verifying customer identity, and preventing fraud. Organizations know this and actively look for ways to integrate identity into their platforms. But it’s not as easy as adding more security measures into the login flow because the user experience is also important. Adding friction to identity authentication and login processes creates user frustration. And frustration discourages users from completing tasks that generate the data that you want to collect.
Streamlined identity flows are essential gateways to removing this friction and collecting the data you need. Why is this data collection so important? Connecting data to real users allows you to personalize experiences and reach more of your target audience — but this requires the ability to identify both known and unknown users and connect them to identity data in real-time. With the impending death of the third-party cookie, making these connections has become next to impossible without collecting first-party data. This includes identity data that can be pulled together to create unified customer profiles.
Unifying Data: Creating Customer Profiles through Identity Graphs and Identity Resolutions
Unifying data allows you to understand who your customers are at an individual level. But a few pieces need to be in place to reach the single customer view ambition of first-party data promises.
An identity graph is a database of all the individual pieces of information we collect and know about any given person. This data is collected and projected into a singular, privacy-protected view to understanding who your users are. Identifiers include multiple pieces of contact data, such as name, email, device ID, website visits, transaction history, etc. An identity graph not only visualizes these identifiers but, crucially, shows a tangible connection between them.
The connections in an identity graph form a unified customer profile, allowing you to recognize an individual person in real-time. This concept is identity resolution. Identity resolution can be achieved in two ways: deterministic matching and probabilistic matching. Deterministic matching is based on what is actually true — data you have collected about the user. This method is highly accurate but can be difficult to scale. Probabilistic matching, on the other hand, is predictive, based on assumptions made from some data you have collected. While less accurate, this method tends to be easier to scale.
When combined, an identity graph and identity resolution enable a variety of use cases, from improved customer journeys and experiences to identity verification and fraud prevention. These are achieved in three steps:
- Resolving data: Unifying customer and prospect data from the identity graph and linking common identifiers
- Enrich: Utilize captured data to understand the customer’s profile
- Activate: Personalize the customer journey and identification process based on the data collected
Mapping the Customer Journey with FullContact Marketplace Integrations
FullContact provides a real-time identity resolution SaaS platform powered by an internal identity graph. FullContact’s identity graph consists of 50+ billion identity fragments to match and provide insights to build a whole-person picture of your users. The graph platform evolves with your users, learning over time to recognize who someone is, regardless of their identifiers change.
FullContact’s identity resolution consists of four integrations, including the Identity.Resolve identity graph, all of which are newly launched on the Marketplace:
Identity.Resolve
The Identity.Resolve identity graph that ingests inputs, such as names, email/mobile ad IDs, addresses, phone numbers, and more — linking them to an individual person, regardless of their source. Using the connected identity data, a unique PersonID is generated — which can then be used as a deduplication method, a portable attribution ID for ad campaigns, or a tool for omnichannel recognition.
- De-duplicate customers: Eliminate duplicate customer records, consolidating in a single profile
- Stitch together prospective customers: Combine customer and prospect data across your ecosystem
- Recognize customers in real-time: Identify your customers and prospects in real-time across multiple channels
Identity.Resolve was built with data privacy and security as top priorities to help you meet your compliance requirements and help foster trust with your users.
Using the identity graph, the FullContact Verify suite of solutions provides real-time signals, matches, and activity scores to determine user risk.
Verify.Signals
Verify.Signals provides signal data and insights into how confident FullContact is about a given person’s identity, activity, and risk level. Verify.Signals is highly specific and provides high-value identity information.
Verify.Match
Verify.Match helps you confirm the data you have collected on a specific person and matches the data FullContact has in the Identity.Resolve identity graph. After validating with the identity graph, Verify.Match offers a risk score based on the data match (or lack of).
Verify.Activity
Verify.Activity assigns a numerical score from 0.0 to 1.0, indicating how much activity has come from a specific identifier. Using this score, you can determine whether you’re dealing with an active or inactive identity.
These integrations provide the insights and data needed for complete identity resolution, allowing you to better understand your end users and their preferences.
Getting Started
Ready to build complete customer profiles and better understand your users? Check out FullContact’s integrations live on the Marketplace. We also encourage you to dive deeper into identity resolution on FullContact’s website, and while you’re there, be sure to check out our partner page to see how we’re working together to bring identity into the MarTech space.
