Offer Distribution System Using User Clustering and Conversion Thresholds
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Solution Overview
Problem
Traditional coupon and offer distribution methods, including paper coupons and electronic media, fail to adapt and become uninteresting to users over time, leading to low engagement and inefficient campaign metrics.
Innovation Solution
An offer system that analyzes user information to learn features and patterns, clusters users, and transmits offers to a limited number of registered users based on predicted interest, adjusting the offer distribution based on conversion rates and user interactions to optimize campaign efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If offers are distributed through electronic media to all users, then the quantity of offer distribution is maximized, but user engagement and conversion rates deteriorate due to irrelevant offers
Solution Approach 1:
The patent segments the user base into distinct clusters based on analyzed features and patterns. The offer system divides users into groups with similar characteristics and behaviors, then distributes offers selectively to specific clusters rather than universally. This segmentation allows the system to maintain high distribution quantities while targeting only relevant user groups, thereby preserving conversion rates.
Solution Approach 2:
The patent applies local quality by tailoring offer distribution to specific user clusters with similar features. Each cluster receives offers customized to its characteristics, rather than a uniform distribution approach. This localized targeting ensures that users receive relevant offers based on their profile, maintaining engagement and conversion rates while distributing to multiple segmented groups.
2Productivity
If the offer system transmits offers to a limited number of users in each cluster, then conversion rates are improved through targeted distribution, but the quantity of offer distribution decreases
Solution Approach 1:
The system resolves this contradiction by segmenting the total user population into multiple distinct clusters. Instead of choosing between sending to one large group or many small groups, the system creates several intermediate clusters and distributes offers to limited users within each segment. This allows the system to maintain controlled distribution quantities per cluster while collectively reaching a significant portion of the total user base through multiple targeted segments.
Solution Approach 2:
The offer system dynamically adjusts distribution strategies by analyzing user interactions and conversion rates. Based on performance metrics, the system adapts which clusters receive offers and how many users per cluster are targeted. This dynamic approach allows the system to optimize the balance between distribution quantity and conversion rate, scaling distribution to multiple clusters while maintaining effectiveness.
3Ease of operation
If traditional coupon distribution methods are used, then ease of operation is maintained, but adaptability to user preferences and campaign optimization deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-analyzing user information, features, and patterns before offer distribution. The system performs user profiling and cluster identification in advance, creating a ready-to-use segmentation framework. This preliminary analysis enables the system to quickly and easily distribute offers to appropriate clusters without complex real-time decision-making, maintaining operational simplicity while achieving high adaptability to user preferences.
Solution Approach 2:
The offer system performs self-service by automatically analyzing user data, identifying patterns, and creating user clusters without manual intervention. The system autonomously adapts to user preferences through continuous analysis of interaction data and conversion rates. This self-service capability maintains ease of operation for the operator while achieving high adaptability, as the system automatically adjusts its distribution strategy based on learned user characteristics.
Data Source
AI summary
A method for determining quality signals for offer campaigns comprises an offer system that receives information submitted by users, and analyzes it to learn features of each user and detect patterns. The offer system clusters the users, and transmits an offer to a limited number of users in each user cluster. It receives notification that a user interacted with the offer and determines a conversion rate for each cluster. If the conversion rate exceeds a pre-defined threshold, the offer system transmits the offer to the remaining users in the cluster. Alternatively, the features of the users are rendered into a multi-dimensional graph that plots the distribution of the users. The offer system marks a representation of each user that interacts with the offer on the graph to determine groupings of users. The offer system transmits the offer to the remaining users in each group.