Ad-Hoc Group Promotion System Using Contextual Data Segmentation
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Solution Overview
Problem
Existing promotion and marketing services face challenges in efficiently leveraging contextual data to identify and target ad-hoc groups of consumers, leading to suboptimal promotion effectiveness and increased costs for merchants.
Innovation Solution
A system and method that utilize consumer devices to collect contextual data, identify ad-hoc groups, determine group intentions, and broadcast targeted group promotion requests, allowing merchants to evaluate yield management information and design responsive promotions, thereby enhancing promotion relevance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional marketing services use broad promotion campaigns, then merchants can reach more consumers, but promotion effectiveness decreases and costs increase
Solution Approach 1:
The system segments consumers into ad-hoc groups based on real-time contextual data such as location, movement patterns, and device proximity. This segmentation allows merchants to target specific groups with relevant promotions rather than using broad campaigns, thereby maintaining reach while improving effectiveness through precise group identification and intention determination.
2Reliability
If merchants target specific consumer groups with customized promotions, then promotion effectiveness increases, but system complexity increases
Solution Approach 1:
Consumer devices automatically perform context collection, group identification, and promotion request broadcasting without requiring complex merchant-side systems. The devices self-organize into ad-hoc groups based on contextual data and autonomously initiate promotion requests, significantly reducing system complexity while maintaining high promotion effectiveness through targeted delivery.
3Measurement precision
If real-time contextual data is collected from multiple sources, then promotion targeting accuracy improves, but data processing complexity increases
Solution Approach 1:
Multiple consumer devices merge their contextual data (location, velocity, acceleration, direction-of-travel, gyroscopic data, temperature, humidity, brightness, gravitational data, orientation, proximity, and audio data) to collectively identify ad-hoc groups. This combining approach improves targeting accuracy through comprehensive data aggregation while distributing processing complexity across multiple devices rather than centralizing it.
4Measurement precision
If ad-hoc groups are identified using multiple contextual parameters, then group identification accuracy improves, but computational requirements increase
Solution Approach 1:
The system uses multiple contextual parameters (location, velocity, acceleration, direction-of-travel, gyroscopic data, temperature, humidity, brightness, gravitational data, orientation, proximity, and audio data) to identify ad-hoc groups, applying partial action by selectively using parameters most relevant to the current context. This approach improves group identification accuracy while managing computational energy by not always requiring all parameters to be processed simultaneously.
Data Source
AI summary
A method, apparatus, and computer program product are disclosed for automatically generating group promotions. An example apparatus includes context collection circuitry configured to collect contextual data, and group identification circuitry configured to identify an ad-hoc group of consumer devices based on the collected contextual data. The example apparatus further includes a processor configured to determine a group intention associated with the ad-hoc group of consumer devices, and communications circuitry configured to broadcast a group promotion request based on the ad-hoc group of consumer devices and the group intention.