Cross-Channel Offer Targeting with Centralized Hashing
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Solution Overview
Problem
Existing systems for providing credit services and offers often result in asynchronous and conflicting offers across channels, requiring customers to re-input information and leading to off-target or unappealing offers due to lack of synchronization and optimization.
Innovation Solution
A system for instant qualification cross-channel offer targeting that receives personally identifiable information, determines credit authorization states, and generates optimized offers using an offer optimization model, hashing data for synchronization and fraud reduction, allowing for multiple offers per request and reducing processing time and fraud risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional offer systems are used where offers expire upon termination of customer interaction, then each channel operates independently, but this results in asynchronous and conflicting offers across channels, requiring customers to re-input information
Solution Approach 1:
The patent merges offer data across multiple channels by implementing a centralized offer repository that stores offers with extended expiration times. When a customer interacts through any channel, the system checks the centralized repository to retrieve existing offers rather than generating new ones, ensuring synchronization and eliminating conflicts across channels.
Solution Approach 2:
The system implements a universal offer management framework that handles multiple channels (mobile app, web, call center, etc.) through a single centralized system. The offer repository and validation logic serve all channels universally, allowing the same offer to be accessed and managed across different customer interaction points without channel-specific complexity.
2Ease of operation
If offers are generated independently for each channel request, then offer generation is simple per channel, but this leads to customers receiving conflicting and unappealing offers
Solution Approach 1:
The system performs preliminary actions by creating offers in a centralized repository with extended expiration times before they are needed across different channels. Offers are pre-validates and stored with their terms and conditions, so when a customer requests an offer through any channel, the system simply retrieves the pre-prepared offer rather than generating it anew, ensuring consistency while maintaining operational simplicity.
3Adaptability or versatility
If customer information is stored and processed for each offer request, then offer personalization is achieved, but this increases processing time and fraud risk
Solution Approach 1:
The system uses hashing to create a compact copy of customer information (a hash value) that can be quickly compared against stored offer criteria. Instead of processing and comparing entire customer profiles each time an offer is requested, the system generates a hash of the customer's key information and uses this compact representation for rapid matching, significantly reducing processing time while maintaining personalization accuracy.
4Reliability
If traditional offer systems are used without hashing, then data storage is straightforward, but this increases fraud risk and reduces synchronization capability
Solution Approach 1:
The system replaces direct comparison of customer information with cryptographic hashing. Instead of mechanically comparing entire customer profiles and offer criteria (which is time-consuming and vulnerable to fraud), the system transforms customer information into hash values and compares hashes, providing fraud prevention through cryptographic verification while maintaining synchronization efficiency across channels.
Data Source
AI summary
A system for instant qualification cross channel offer targeting is disclosed. The system may receive a plurality of personally identifiable information (PII) elements from at least one of a customer device or an affiliate system. The system may determine a credit authorization state in response to the PII element. The system may receive a first request to generate an offer from at least one of the customer device or the affiliate system. The system may generate the offer in response to an offer optimization model, the credit authorization state, and the first request to generate an offer. The system may hash the plurality of PII elements and determine a unique hash based on the plurality of PII elements. The system may associate the unique hash to the offer and store the offer as offer data.