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Security / Software Development

How to Choose the Right Identity Resolution System

As the customer journey has become more complex, these systems have become an increasingly popular way to automate the merging of different identifiers.
Jun 21st, 2023 6:43am by
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In today’s business landscape, it’s essential for companies to offer unique and personalized customer experiences. However, as a business expands, the challenge of recognizing customers as unique individuals becomes increasingly complex.

Like any modern business, you’re probably using forms, mobile apps and logged-in user actions to capture the complete customer story. Each of these mechanisms for capturing customer data has its own set of objects and data structures and possibly its own unique identifiers. Records scattered across disparate websites can be difficult to reconcile on their own, but they become even more difficult when developers throw in code requests and change the logic of how information is captured and categorized. It takes hundreds, if not thousands, of lines of code to reconcile every change your business makes to create a clean identity graph.

One way to cut down the amount of time it takes to build this complete view of the customer is to adopt an identity resolution system. As the customer journey has become more complex, spanning dozens of different touchpoints, identity resolution systems have become an increasingly popular way to automate the merging of different identifiers to avoid writing SQL-heavy queries all day long.

Not all identity resolution systems are created equal. Here are the key requirements to look for when selecting an identity resolution system to meet the specific needs of your team.

Matchmaker, Matchmaker, Make Me a Match

A best-in-class approach to identity resolution enables you to match many identifiers to the same person (such as <user_id>, <email>, <phone>, <device_id>, <anonymous_id>) and then set the priority of matching to control how profiles are stitched together.

It’s a powerful model, pioneered by Segment, that helps customers stitch billions of events together into unified profiles.

However, this model requires alignment and consistency in the way you stamp values for each identifier onto your events. Failing to do this will see any identity resolution system break down.

Probabilistic vs. Deterministic: Which Method Is Best for Your Team?

Let’s use a real-world example. Say that two of your identifiers are phone number and email address.

First, a customer shows up on your website and registers with their email and phone number. The email is formatted in all lowercase, like <>, and the phone number is formatted as <+1-123-456-7890>. So far, so normal.

Then, the customer calls your call center, but their phone number is formatted as <1 (123) 456-7890>. The events from the call center will not be stamped onto the same customer profile as was generated from the earlier website events because the phone number formatting doesn’t precisely match.

The same problem will occur if the call center agent types in the customer’s email as <>. The mixed-case email isn’t an exact match to the lowercase email provided on the website, so the events from the call center won’t appear on the same profile as the events captured on the website. Even the smallest differences in formatting can create discrepancies when building a customer profile.

This approach to identity resolution is called “deterministic,” requiring exact matches (instead of “fuzzy” or “probabilistic” matches) on identifier values to unify events into a single profile.

Deterministic is typically the best approach for identity resolution because it’s based on first-party data your customers actually produce rather than probabilistic algorithms that resolve identities based on who your customers likely are.

To illustrate this in practice, imagine there are two John Smiths from Los Angeles with the same ZIP code. A fuzzy match system might indicate those people are the same, but what if they’re not? Now you’ve incorrectly merged these individuals and compromised the accuracy of your data and profiles.

Like a surgeon or an aerospace engineer, when it comes to identity resolution it’s essential to be exact.

Confidence and Credibility in a Data-Driven World

While deterministic identity resolution might seem overly rigorous, it’s actually highly beneficial for personalization. Personalization use cases (sending an email, delivering a recommendation, and so on) require 100% confidence that a user is who you think they are. The only way to guarantee that confidence is through a deterministic identity algorithm.

The alternative is simply guesswork and increases the likelihood that your personalization (or lack thereof) will have a detrimental impact on your customer relationships.

A deterministic identity resolution solution enables 100% reliable profile unification, honoring the exact first-party data a customer provides to a brand. More importantly, embracing a deterministic approach as the core of your identity strategy will allow you to build high-quality customer profiles that power the personalized experiences customers have come to expect.

Getting Started with Deterministic Identity Resolution

Here are some recommendations for implementing a deterministic identity resolution strategy in your organization:

  1. Define Unique Identifiers: Establish a set of unique identifiers that will be used to recognize and match customers across different touchpoints and systems. These identifiers can include user IDs, email addresses, phone numbers, device IDs or any other consistent identifier that uniquely identifies a customer.
  2. Standardize Data Format: Decide on a consistent format for the unique identifiers. This ensures that the data across different sources and channels can be easily compared for exact matches.
  3. Establish Matching Rules: Define matching rules that determine how the unique identifiers are compared to identify matches. For deterministic matching, the rules will focus on exact matches of the identifiers. Establish rules to handle variations in case, formatting or special characters to ensure accurate matching.
  4. Create a Unified Customer Profile: Once matches are identified, merge the data from different sources into a unified customer profile. This profile should consolidate all relevant information and activities associated with an individual customer.
  5. Regularly Update and Maintain Profiles: Continuously update and maintain customer profiles as new data is collected or changes occur. A Customer Data Platform allows you to build a customer profile that you can reference across the customer’s whole lifetime. You can then enrich those profiles with additional data, connect those profiles to data residing in a data warehouse and resolve conflicts or inconsistencies.

Finally, remember that data is a team sport. Deterministic identity resolution might require collaboration between different teams, such as product, customer support and marketing, to ensure a comprehensive and accurate implementation.

This will ensure that no data point gets left behind and that the deterministic identity resolution system will be accurately leveraged across the organization.


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TNS owner Insight Partners is an investor in: Pragma.
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