Mailbox Coupon Prioritization via Predictive Scoring
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Solution Overview
Problem
Existing systems fail to efficiently display and prioritize relevant coupons in a user's mailbox, leading to missed opportunities for interaction due to the overwhelming number of coupons and lack of context-based relevance, resulting in excessive resource usage and user effort.
Innovation Solution
A predictive model is generated based on user features and coupon features, which assigns scores to coupons, allowing the graphical user interface to prioritize and display the most relevant coupons to the user, enhancing interaction speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all coupons in the mailbox are displayed to the user, then the user can access all available coupons, but the user interface becomes overwhelming and the user cannot efficiently identify relevant coupons
Solution Approach 1:
The system extracts and displays only the most relevant coupons from the user's mailbox based on predictive modeling, rather than showing all coupons. This selective extraction resolves the contradiction by presenting a manageable subset of coupons that are most likely to interest the user, making the interface usable while still providing access to relevant offers.
Solution Approach 2:
The system applies different quality levels to different coupons based on their relevance scores. High-relevance coupons are prominently displayed with higher visibility, while lower-relevance coupons are either downgraded or excluded. This differential treatment resolves the contradiction by ensuring that the most important coupons receive appropriate attention without overwhelming the user with equal treatment of all coupons.
2Productivity
If a predictive model is used to score and prioritize coupons, then the most relevant coupons are displayed first improving user efficiency, but the system requires significant computational resources and complexity
Solution Approach 1:
The system performs preliminary scoring and prioritization of coupons using predictive models before the user views them. By pre-computing relevance scores and arranging coupons in advance, the system enables fast user interaction without requiring complex real-time computations when the user accesses their mailbox. This resolves the contradiction by shifting computational work to a preliminary stage.
Solution Approach 2:
The system uses the user's own historical data and behavior patterns to automatically generate predictive models tailored to each user's preferences. This self-service approach allows the system to adapt to individual users without requiring manual configuration or excessive external computational resources, resolving the complexity issue while maintaining high productivity.
3Loss of energy
If coupons are displayed without prioritization, then the system uses minimal computational resources, but users miss relevant coupons due to the overwhelming number and lack of context-based relevance
Solution Approach 1:
The system changes the parameter of coupon presentation from unsorted to score-based prioritization. By introducing a relevance score parameter derived from predictive modeling, the system efficiently identifies and surfaces the most interaction-worthy coupons without requiring excessive computational resources. This resolves the contradiction by using a simple yet effective scoring mechanism that prevents information loss while maintaining reasonable resource usage.
Data Source
AI summary
One or more computing devices, systems, and/or methods for mailbox-based coupon display are provided. For example, a plurality of email messages stored in a mailbox of a first email account of a first user may be identified, and may be evaluated to identify a plurality of coupons. The plurality of coupons may be analyzed to determine one or more coupon features. User data and/or activity data associated with the first user may be analyzed to determine one or more user features. A predictive model may be tuned based upon the one or more coupon features and/or the one or more user features. A set of scores may be generated for the plurality of coupons based upon the predictive model. A graphical user interface may be controlled based upon the set of scores to display one or more coupons.