The Evergage customer data platform (CDP) helps businesses aggregate all of their customer data in one place for analysis and activation purposes. One result of this aggregation is that it is now possible to understand every one of your customers on an individual level so you can recognize and communicate with each one across channels. For this reason, the concept of individual identity plays an essential role in the customer experience and in any customer data platform.
This article explains the concept of identity resolution as it relates to the Evergage CDP.
Central to the Evergage CDP is a unified customer profile which defines the customer's identity using data gathered from a variety of sources across all channels, regardless of whether that person is known or anonymous.
This single, comprehensive profile is the foundation of any data analysis effort or activation (personalization) campaign. You can use it to determine things like whether each individual should qualify for certain promotions, which product or content recommendations each individual should see, which channel to use to communicate, or when to send an email and which one to send, all based on information gathered across all channels, whether they are on or offline.
The profile includes data that helps you understand the prospect or customer (e.g. purchases, browsing history, email interactions, attributes, subscriptions, loyalty membership and status, interests and preferences, browser type, location, demographics, predictive scores) as well as identifying data elements that provide a connection between different records (e.g. cookies, email address, full name, physical address, phone number, system ID).
Your goal is to have a single profile for each individual, but it’s still possible to end up with multiple profiles for the same person. When data comes from different sources, it needs to be stitched together into one single profile. This is simple when there is clear identifying information (i.e. the same email address), but not every person will clearly identify themselves in every channel for every interaction. The separate profiles created in the following situations all contain important customer data, but each one doesn’t fully account for the person’s true and complete identity:
Evergage will stitch profiles together when the data becomes available to do so. For example, consider a visitor named Mary Smith who has one “Mary Smith” profile and one “anonymous” profile in the Evergage:
Evergage will merge these profiles together once she identifies herself in some way, such as clicking through from an email to the company's site while on her laptop. Once it becomes clear that the anonymous person is actually Mary Smith, both profiles need to be stitched together to create a full picture of Mary.
To do this, Evergage constantly monitors identifying information, then uses deterministic matching to determine if two or more profiles represent the same identity.
Then, Evergage stitches these different profiles together based on clear, unique identifiers such as system ID or email address. For example, if a person visits your site multiple times but never identifies himself by doing something like making a purchase, registering for a webinar, or signing up for a newsletter, his profile remains anonymous. The first time he does provide his email address, Evergage will recognize all of his past sessions that took place with the same cookie, and stitches his anonymous profile together with his new identified profile.
While it’s certainly not ideal to have several profiles that reflect one individual, it’s also a problem to have data from more than one person reflected in a single profile. Since each business has a different set of identity sources and a different tolerance for error and false positives, Evergage gives you the power to add your own rules about your view of the trustworthiness of different data types and decide what action to take when there is a conflict. There are different scenarios that each business should explore to determine what logic makes sense to apply.
For example, you may want to:
Also, you may want to value some sources more highly than others if there is a conflict. For example: