Personalized Incentive Generation via ML for Habit-Based Rewards
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Solution Overview
Problem
Rewards programs for transaction cards often fail to consider individual users' habits, interests, or goals, as the reward categories are pre-established and do not adapt to specific user behaviors.
Innovation Solution
A method and system that utilizes a machine learning model to determine incentives based on user-selected objectives, analyzing past purchases and demographic information to offer personalized rewards, encouraging spending in categories that support the user's objectives while discouraging spending in categories that undermine them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-established reward categories are used, then the rewards program is simple to implement, but it fails to consider individual user habits and goals
Solution Approach 1:
The system dynamically generates and updates reward categories based on user purchase behavior and stated goals. Instead of static pre-established categories, the system adapts categories in real-time to reflect individual user habits, making the rewards program both simple to implement and highly personalized.
Solution Approach 2:
The system automatically analyzes user purchase data and goal information to generate personalized reward categories without requiring manual configuration. The system serves itself by using its own data to create customized reward structures for each user, eliminating the need for complex manual setup while achieving personalization.
2Adaptability or versatility
If personalized incentives based on machine learning are implemented, then user engagement is enhanced, but system complexity increases
Solution Approach 1:
The system uses a universal machine learning model that serves multiple functions: analyzing purchase behavior, identifying user goals, generating personalized categories, and determining optimal incentives. This single multi-functional approach reduces overall system complexity compared to having separate systems for each function.
Solution Approach 2:
The system implements continuous feedback loops where user responses to incentives and subsequent purchase behavior are fed back into the machine learning model. This automated feedback mechanism allows the system to learn and adapt without manual intervention, managing complexity through self-optimization rather than complex control structures.
3Reliability
If incentives are allocated based on user performance toward objectives, then rewards become more effective, but tracking and evaluation complexity increases
Solution Approach 1:
The system continuously tracks user progress toward objectives by monitoring purchase behavior in real-time. Rather than periodic batch processing, the system maintains continuous evaluation of user performance, automatically updating incentive allocation based on current progress. This continuous action simplifies tracking by making it an ongoing natural process rather than a separate complex evaluation step.
Data Source
AI summary
The present disclosure is directed to systems and methods for determining one or more incentives for a user. For example, a method that may include: receiving a selection of an objective from among a plurality of objectives from a user via a client device; presenting, based on the selected objective, one or more incentives from among a plurality of incentives to the user, the plurality of incentives being based on a machine learning model trained to determine the plurality of incentives based on past purchases; receiving a selection of at least one of the one or more incentives from the user via the client device; and determining whether to allocate the selected one or more incentives based on a performance of the user.