Dynamic Offer Value Adjustment Based on User Context
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Solution Overview
Problem
Current systems lack the ability to effectively deliver dynamic, context-sensitive offers to incentivize user behavior in real-time, failing to adapt offers based on user location, behavior, and environmental conditions.
Innovation Solution
A network-based system that utilizes mobile devices to deliver dynamic offers with values that incrementally decrease over time, with rules that can slow, halt, or reverse the decrease based on user actions, such as moving towards a retail location or sharing offers, leveraging location data and user profile information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static offers are delivered to users, then system complexity is low, but user engagement and redemption rates are insufficient
Solution Approach 1:
The patent implements dynamic offers that automatically adjust their value and attributes in real-time based on user context, location, behavior patterns, and environmental conditions. The system transitions from static to dynamic offer delivery, where offer parameters are continuously modified to optimize user engagement while managing complexity through automated rule-based adjustments
Solution Approach 2:
The system incorporates feedback loops that monitor user interactions, location data, and redemption patterns to continuously refine and adjust offer parameters. User responses to offers feed back into the system, enabling automated adaptation of future offers based on observed behavior patterns and contextual information
2Measurement precision
If real-time context-sensitive offers are delivered, then user targeting precision is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-segmenting users into groups based on historical behavior patterns and pre-positioning contextual data. This allows real-time offer delivery to leverage pre-processed information, reducing the computational burden during actual offer generation while maintaining high targeting precision
Solution Approach 2:
The patent applies local quality by processing and analyzing data at the user level rather than globally. Each user receives customized offers based on their specific context, location, and behavior patterns, allowing the system to focus computational resources on individual user segments rather than processing all user data uniformly
3Productivity
If offer values remain constant, then system operation is simple, but user incentive effectiveness decreases
Solution Approach 1:
The system implements dynamic offer values that automatically adjust based on user proximity to retail locations, time sensitivity, and engagement patterns. Offer values increase or decrease in real-time to optimize redemption rates, with the complexity of value management handled by automated systems rather than manual intervention
Solution Approach 2:
The patent enables offers to self-adjust their values and attributes based on pre-defined rules and real-time contextual data. The system autonomously manages offer parameters without requiring constant human intervention, allowing complex value adjustments to occur automatically based on user behavior and environmental conditions
Data Source
AI summary
Systems and methods to deliver dynamic context sensitive offers to intent user behavior are discussed. For example, a method to deliver a dynamic context sensitive oar can include operations for generating a dynamic offer, delivering the dynamic offer, receiving data associated with a user, updating a context input with data associated with the user, and adjusting the value of the dynamic offer. Generating the dynamic offer includes associating a rule for manipulating the value of the dynamic offer based on a context input. Updating the context input of the rule includes extracting context information from the data associated with the user. Adjusting the value of the dynamic offer can be based on reevaluation of the rule with the updated context input.