Social Graph Discount Aggregation for Targeted Sales
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Solution Overview
Problem
Consumers often receive volume discounts on products or services that are not relevant to their interests, leading to frustration and reduced engagement with deal websites, as existing systems aggregate groups for bulk purchasing without considering individual preferences.
Innovation Solution
A computer system that aggregates lists of desired items and prices from multiple consumers, assigns them to price ranges based on interest, and provides this information to entities, allowing for targeted sales to subsets of consumers interested in specific price ranges, thereby facilitating relevant volume discounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If consumers are aggregated into groups to receive volume discounts, then the manufacturer can move large volumes of inventory quickly and leverage economy of scale, but the discounts are often for products and services that may not be relevant or of interest to a given consumer in the group
Solution Approach 1:
The patent segments consumers into subgroups based on their individual interest profiles and purchasing histories. Instead of treating all consumers uniformly in a single aggregate group, the system divides the consumer base into multiple segments with similar preferences, allowing targeted volume discounts for each segment's preferred products while maintaining overall inventory movement efficiency
Solution Approach 2:
The patent applies local quality by customizing discount offers to match the specific interests and preferences of individual consumers or small consumer groups. Each consumer receives discounts tailored to their local preferences rather than a generic group offering, improving relevance while the aggregated data from multiple local groups maintains the overall volume discount effectiveness
2Ease of operation
If volume discounts are offered to individual consumers through aggregated groups, then consumers can receive the benefits of bulk purchasing, but the discounts often are not useful for this consumer leading to frustration and reduced engagement
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor consumer responses, purchasing behavior, and engagement levels. This feedback is used to refine and update consumer interest profiles, ensuring that future volume discount offers are increasingly aligned with individual consumer preferences, thereby improving reliability while maintaining ease of access
Solution Approach 2:
The patent performs preliminary analysis of consumer data to identify interest profiles and predict preferred products before offering volume discounts. By pre-segmenting consumers and pre-personalizing discount offers based on their historical behavior and stated preferences, the system ensures that when consumers receive volume discounts, they are highly likely to find them useful and relevant
Data Source
AI summary
During a marketing technique, lists of desired items and associated prices are received from multiple consumers. For example, consumers may provide lists of desired items and the (discounted) prices at which these items are of interest. By assigning the consumers to different price ranges based on the associated prices and an occurrence of at least one of the items in the lists, an aggregated interest in the item, including numbers of consumers that desire the item in the different price ranges, is determined. Information associated with the aggregated interest in the item is then provided to at least one entity that provides the item, thereby facilitating sales of the item to a subset of the consumers associated with one or more of the price ranges.