Customer Identity™ Engine

Probably the most important task in synchronizing and integrating customer data and communicating with customer-facing applications is establishing Customer Identity for every individual customer.

Problem: Ambiguous Customer Data

The Customer Context™ Server is typically deployed in an environment containing hundreds of millions of customer data fragments stored across dozens of systems.

Most importantly, a single reliable identifier or key for each customer among legacy systems almost never exists. Even recently entered or captured customer data often suffers from low quality or inconsistent data entry.

Solution: Rule-based Customer Identity™ Engine

The only means for determining if two customer data pieces belong to the same person is to perform rule-based matching on person attributes such as name, address, birth date, account number.

In principal, every field of every customer data fragment has to be compared with the corresponding field in all other records. This Herculean task is only feasible with a sophisticated and scalable rule-evaluation architecture.

To attain reliable results, a Customer Identity rule set usually encompasses two dozens top-level rules and more than one hundred sub-rules based on more than thirty different customer attributes or fields.

To achieve the best view of a customer, the attributes from all matched data fragments have to be merged using a second set of rules that pick the best values for each attribute based on source reliability, time stamp, frequency and other criteria.

It is mandatory that only one set of rules determine Customer Identity to accomplish consistent customer consolidation. This makes the Customer Context Server the preferred application for executing Customer Identity rules.

Important: Continuous Maintenance of Customer Identity

Every time any customer attribute is updated by a CRM, e-commerce or legacy system, the updated attribute or field must be compared with similar customer information across the entire Customer Context Cache potentially causing the Customer Context to be corrected, which could require the Customer Context to be split into two or more contexts or to be merged from two or more contexts.

Immediately, the Customer Context Server makes the newly consolidated Customer Context available to all customer touchpoint applications, providing the best view of a customer at all times.