Rule Engine for Customer Trait Identification
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Solution Overview
Problem
Current systems require software developers to write code for identifying customer traits, leading to slow marketing campaign execution and inadequate responses to changing market conditions, as they lack the ability to process customer data efficiently and update customer trait reports in real-time.
Innovation Solution
A computing system that generates and applies rules to customer data to identify traits, allowing marketing teams to configure business logic without coding, enabling parallel processing and regular updates to customer trait reports, thus accelerating marketing campaign execution and response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software developers write code to identify customer traits, then the system can process customer data, but the marketing campaign execution becomes slow and cannot respond quickly to changing market conditions
Solution Approach 1:
The patent extracts the business logic for customer trait identification from traditional software code and separates it into configurable rules that can be modified without requiring software development. This allows marketing teams to independently configure and update customer identification criteria, eliminating the need to wait for software developers to implement changes.
Solution Approach 2:
The system creates a universal platform that handles both the technical processing of customer data and the business logic of identifying target customers within a single integrated architecture. This multi-functional system eliminates the need for separate software development cycles by allowing business users to directly configure the identification logic.
2Productivity
If traditional systems process customer data, then customer traits can be identified, but the processing is not efficient and cannot be updated in real-time
Solution Approach 1:
The system implements dynamic configurability where customer identification rules can be modified in real-time without system reconfiguration or software updates. The architecture allows rules to be changed, activated, or deactivated immediately, enabling the system to adapt to changing market conditions and customer criteria on the fly.
Solution Approach 2:
The patent introduces an intermediary rule engine that sits between the customer data and the identification logic. This intermediary layer processes customer data against configurable rules, allowing business logic to be updated independently from the underlying data processing infrastructure, thus enabling real-time adaptability without affecting processing efficiency.
3Measurement precision
If marketing teams need to configure customer identification requirements, then accurate customer lists can be generated, but currently they must rely on software developers to implement the logic
Solution Approach 1:
The system enables marketing teams to self-configure customer identification requirements directly through the platform without needing to engage software developers. The interface allows business users to define, modify, and activate customer criteria using their domain knowledge, ensuring accurate identification logic while eliminating dependency on technical teams for routine configuration changes.
4Productivity
If code is written to implement customer identification logic, then the system can function, but the conception-to-deployment timeline is extended
Solution Approach 1:
The system performs preliminary configuration of customer identification logic through a standardized rule framework that is pre-built and ready for use. Marketing teams can immediately configure specific criteria within this pre-established framework, eliminating the need for preliminary software development and testing cycles, thus dramatically reducing the conception-to-deployment timeline.
Data Source
AI summary
Techniques are described for creating and maintaining rules to identify customers based on customer traits from customer data and applying the rules to current customer data at a point in time to identify the customer traits in the customer data. A computing system is configured to maintaining one or more rules configured to identify one or more customer traits from customer data, receive customer data of a plurality of customers at a first point of time, apply the one or more rules to the customer data to identify the one or more customer traits in the customer data for one or more customers of the plurality of customers, and outputting a report comprising a plurality of customer identifiers for the plurality of customers and, for a particular customer identifier for a particular customer, one or more indicators of which customer traits are associated with the particular customer.