Is your skip-tracing solution using an identity graph? (sponsored)

Published on February 27, 2025 at 9:00 am

Experian

Contacts are useless if they’re not matched to the right consumer
BillionPhotos.com / Adobestock

Many skip-tracing solutions claim to help you find the best and most up-to-date phone numbers, email addresses and physical addresses. But you should ask vendors several important questions before including them on your short list. One is what data can you provide? Equally important, and sometimes overlooked, is what type of database they use and how they link information to consumers.

From big data to best data

Having access to a variety of data sources with the latest customer data is undoubtedly crucial to maintaining your contact list and improving right-party contact (RPC) rates. However, access to more data also introduces new challenges, such as effectively and efficiently organizing all the data.

Database structure and matching rules can affect the performance of skip-tracing tools. For example, even if a tool has access to recent contact information, poor matching rules could lead to:

  • False negatives: When contact information doesn’t get linked to the correct person.
  • False positives: When contact information is linked to the incorrect person.

Variations in technology and approaches explain why skip-tracing tools with access to identical data sources could have different hit rates and suggest different “best” contact methods.

Read: AI in Debt Collection: Benefits and Uses

Why identity graph databases make sense for skip tracing

You can build different types of databases for storing information, such as:

  • Relational databases: A traditional approach that organizes data in rows and columns, similar to how you might enter information in a spreadsheet.
  • Graph databases: A more modern option that organizes information into nodes (also called points) and edges (also called links).

Relational databases are a good option in certain situations, such as when you have a limited amount of highly structured data with consistent relationships. But graph databases can be more efficient when there’s a lot of data, or when the data frequently changes and you’re trying to find connections between data sets.

Many organizations use identity graphs — a type of graph database — to create user profiles. For example, marketers can build customer profiles with contact, payment, behavioral and device information. They could then track the person across devices and deliver highly relevant and personalized ads or offers.

Identity graphs can also be an optimal option for skip tracing because they:

  • Excel at finding connections: Identity graphs are designed to find and connect data points from various sources, such as lists of people, phone numbers, email addresses and physical addresses.
  • Create a single view of each consumer: As new information enters the identity graph, it can be linked to an existing profile or lead to the creation of a new profile. However, each person should only have one profile, and the profiles are sometimes called a consistent, unified, 360-degree or persistent view of a consumer.
  • Can scale efficiently: Graph databases can improve as you add information to the database because there are new potential links to discover. They’re also more efficient than relational databases at generating reports, particularly when you have very large databases.

Additionally, identity graphs are a good fit for skip tracing because they can use probabilistic matching algorithms to connect information to a person based on the likelihood of a connection. Probabilities can be quickly calculated based on data from multiple data sources, and you can fine-tune the algorithms to improve hit rates while minimizing false positives. 

Experian’s TrueTrace™ now uses an identity graph

Experian recently upgraded its popular TrueTrace™ contact management solution to run on an identity graph.

Access to a variety of data is still important for keeping your contact list up to date, and TrueTrace™ is regularly updated with consumer data from:

And now, with an identity graph and probabilistic matching, TrueTrace™ can quickly and efficiently link data from these sources to a single user profile — what we call a Persistent ID. We also use similar techniques for skip-tracing queries to help customers find accurate and recent consumer contact information, even if they initiate the query with incomplete personally identifiable information.

Based on testing, organizations can expect to see a substantial lift in both hit rate and RPC rates using the new TrueTrace™ built on an identity graph, compared to competing tools.

Read: Skip-Tracing Best Practices for Debt Collectors

TrueTrace™ is a consolidated solution for collectors

Along with the upgrade to an identity graph, TrueTrace™ now offers built-in email appending to give collectors an all-in-one experience. You can use a single configurable input to perform specific searches, such as best address or best email, or you can search for any combination of different contact methods.

Learn more online or download the recent white paper, for a deeper dive into database structure, email append and TrueTrace™.