Context-Aware Digital Offer Channel Selection System
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Solution Overview
Problem
Existing digital promotion systems often provide offers in formats that are not convenient for consumers, leading to inefficient redemption, as they fail to consider the consumer's location and preferences, resulting in missed opportunities for advertisers and retailers.
Innovation Solution
A system utilizing machine learning and artificial intelligence to select the most convenient redemption channel for digital payloads, such as coupons or discounts, based on consumer purchasing behavior, location, and environmental factors, using a creative media engine that integrates targeting, preference, and channel selection tools to provide personalized offers through either online or in-store channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If offers are provided in a fixed format through traditional digital promotion systems, then the system structure is simple, but the consumer convenience and redemption rate decrease
Solution Approach 1:
The patent implements dynamic offer formatting that adapts to consumer context. The system automatically adjusts offer presentation format (e.g., in-store pickup vs. online delivery) based on real-time factors like consumer location, device type, and purchase history, transforming static offers into dynamic, context-aware promotions that enhance convenience without manual intervention
Solution Approach 2:
The system employs machine learning models that automatically analyze consumer data and select optimal offer formats without human intervention. The automated channel selection and personalization engine handles the complexity internally, allowing consumers to receive conveniently formatted offers without needing to understand or configure system parameters
2Productivity
If the system personalizes offers based on consumer behavior and environmental factors, then consumer engagement increases, but data processing requirements and computational resources increase
Solution Approach 1:
The system performs preliminary data processing by pre-segmenting consumers into behavior-based groups and pre-determining offer formats for different consumer profiles. Machine learning models are trained in advance on historical data to establish patterns, allowing the system to quickly match current consumer context with pre-analyzed segments, reducing real-time computational burden while maintaining high personalization quality
Solution Approach 2:
The patent replaces traditional rule-based offer selection with machine learning algorithms that automatically process consumer behavior data. The AI-driven personalization engine substitutes manual or simple algorithmic approaches with sophisticated models that can handle complex data patterns more efficiently, improving advertisement efficiency while optimizing resource utilization through intelligent data processing
3Productivity
If the system provides offers in inconvenient formats, then implementation is straightforward, but redemption rates and consumer satisfaction decrease
Solution Approach 1:
The patent introduces an automated intermediary system that acts as a mediator between the offer database and the consumer. This intelligent intermediary automatically selects and presents the most appropriate redemption channel based on consumer context, eliminating the need for consumers to navigate complex channel options manually while ensuring they receive offers in formats they are most likely to redeem
Data Source
AI summary
A method to select in-store or e-commerce channels for optimized delivery of creative media to consumers includes selecting, based on a prior purchasing behavior for a consumer, a targeted offer, wherein the targeted offer includes at least one of a coupon, a discount, or an advertisement for a retail product. The method also includes selecting a channel for the consumer to redeem the targeted offer, wherein the channel for the consumer to redeem the targeted offer includes one of an in-store redemption of the targeted offer or an online redemption in a shopping basket, providing the targeted offer for display in a client device used by the consumer, and indicating, in the display, the channel for the consumer to redeem the targeted offer. A non-transitory, computer-readable medium storing instructions and a system to perform the above method are also provided.