Dynamic Marketing Incentive Generation via Machine Learning
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Solution Overview
Problem
In the context of marketing incentives, it is challenging for vendors to differentiate their offers from competitors and for customers to determine the best value among numerous competing offers, especially in an oversaturated market environment where mobile devices provide limited options and information, leading to a barrier in making purchasing decisions.
Innovation Solution
The use of machine learning models to generate and optimize marketing offers on mobile devices by incrementing incentives based on user redemption rates, selecting offers that maximize value or engagement likelihood, and leveraging social engagement to create a micro-community effect, which boosts incentives and reduces the barrier to entry for purchasing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple vendors provide marketing incentives for competing offers, then the quantity of offers increases, but it becomes difficult for any one vendor to differentiate their offer from others
Solution Approach 1:
The patent applies local quality by providing customized incentives tailored to specific user profiles, behaviors, and preferences rather than uniform offers. Each user receives personalized incentive magnitudes and types based on their individual characteristics, making differentiation possible even in a saturated market.
Solution Approach 2:
The patent implements dynamics by making incentives adaptive and changeable over time. The system dynamically adjusts incentive magnitudes based on user engagement, redemption rates, and competitive conditions. Offers evolve from static to dynamic, allowing vendors to respond to market changes and user feedback in real-time.
2Device complexity
If vendors provide static incentives, then the simplicity of offer structure is maintained, but the ability to optimize net present value and engagement is limited
Solution Approach 1:
The patent implements feedback mechanisms where user engagement data, redemption rates, and behavioral patterns are continuously collected and fed back into the system. This feedback loop enables the machine learning models to learn from actual user responses and refine incentive strategies, optimizing net present value through data-driven adjustments.
Solution Approach 2:
The system performs self-optimization through automated machine learning models that continuously adjust incentive parameters without manual intervention. The algorithms independently analyze performance data and modify offer structures to maximize value, reducing the need for complex manual management while improving productivity.
3Ease of manufacture
If traditional marketing methods are used, then the simplicity of implementation is maintained, but the effectiveness in oversaturated markets is reduced
Solution Approach 1:
The patent introduces a digital platform as an intermediary between vendors and customers. This platform mediates the interaction by processing offers, managing user profiles, analyzing engagement data, and distributing personalized incentives. The intermediary handles the complexity of personalization and optimization, making advanced marketing effective while maintaining ease of implementation for vendors.
Solution Approach 2:
The patent replaces traditional mechanical marketing methods with automated digital systems. Machine learning algorithms substitute for manual marketing analysis and decision-making processes. The system automatically processes large volumes of user data, generates personalized offers, and optimizes incentives, replacing complex manual operations with efficient automated digital mechanisms.
4Device complexity
If uniform incentives are provided to all users, then the ease of management is maintained, but the likelihood of user engagement and redemption is reduced
Solution Approach 1:
The patent applies local quality by customizing incentives to match individual user characteristics, preferences, and behaviors. Each user receives tailored offers with appropriate incentive magnitudes and types, increasing relevance and engagement likelihood. This personalization transforms uniform management into targeted delivery without significantly increasing operational complexity.
Solution Approach 2:
The system performs preliminary actions by pre-segmenting users into profiles and pre-configuring incentive strategies based on historical data and predicted preferences. This advance preparation enables personalized engagement without requiring complex real-time decision-making during user interactions, maintaining ease of management while improving engagement likelihood.
Data Source
AI summary
A method of generating a marketing offer includes: receiving first data with offers for sale of a product; adding a respective incentive to a corresponding offer, wherein: a magnitude of the respective incentive is incremented toward a corresponding maximum based on a quantity of users that have redeemed the corresponding offer; and the maximum for the respective incentive is determined based on historical data of engagement rates of users for offers with a variety of incentives.