Wireless Commerce Transaction Analysis System for Personalized Advertising
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Solution Overview
Problem
Current advertising methods are ineffective in targeting specific consumers with personalized advertisements, as they rely on static locations and broad audiences, failing to leverage the vast amount of data from mobile commerce transactions to predict user behavior and preferences.
Innovation Solution
A wireless commerce transaction analysis system (WCTAS) that collects and analyzes transaction data from mobile devices to generate predictions of future user actions, enabling targeted advertisements to be delivered based on predicted interests and behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional advertising methods using static locations and broad audiences are used, then the advertising coverage area is large, but the advertising relevance to individual consumers is low
Solution Approach 1:
The patent segments the advertising audience into individual consumers based on their transaction histories and behavior patterns. By analyzing each consumer's unique purchase sequence and preferences, the system creates personalized advertisement segments rather than using broad static audiences, thereby improving advertising relevance without requiring overly complex system architecture
Solution Approach 2:
The patent implements dynamic advertising by continuously updating consumer profiles based on real-time transaction data. The system adapts advertisements to changing consumer preferences and behaviors, transforming static advertising into dynamic, personalized content that evolves with consumer needs, improving relevance while maintaining manageable system complexity through incremental updates
2Adaptability or versatility
If transaction data from mobile devices is collected and analyzed to generate predictions, then advertising personalization is improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary data processing by pre-processing transaction data to extract meaningful patterns and consumer profiles before generating advertisements. This preliminary action includes cleaning, normalizing, and structuring data in advance, which reduces the complexity of real-time processing requirements and enables personalized advertising through systematic preparation rather than complex real-time computation
Solution Approach 2:
The patent introduces an intermediary data processing layer that acts as a mediator between raw transaction data and final advertisements. This intermediary layer includes predictive models and profile generation components that simplify the relationship between complex data and advertising output, enabling personalization by translating raw data into actionable consumer insights through structured intermediate representations
3Productivity
If predictions of future user actions are generated based on transaction history, then consumer engagement is improved, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary analysis of transaction histories to build consumer profiles and predict patterns in advance. By pre-computing consumer behavior models and storing them for quick retrieval, the system reduces real-time analysis requirements, enabling rapid generation of personalized advertisements that maintain high consumer engagement without requiring extensive computational time during ad delivery
Solution Approach 2:
The patent implements continuous learning and updating of consumer profiles based on ongoing transaction data. This continuous action allows the system to maintain accurate predictions without re-analyzing entire histories from scratch, instead updating only relevant portions of consumer profiles incrementally, thereby sustaining high engagement while minimizing time loss through efficient continuous processing rather than periodic batch analysis
Data Source
AI summary
Various embodiments of a system and method for generating predictions based on wireless commerce transactions are described. Various embodiments may include a wireless commerce transaction analysis system configured to generate one or more models based on aggregated wireless commerce transaction information indicating one or more wireless commerce transactions completed with respective mobile devices of multiple users. For each of at least some of the wireless commerce transactions, the aggregated wireless commerce transaction information may indicate one or more characteristics of the wireless commerce transaction. The wireless commerce transaction analysis system may also be configured to generate a prediction of a future action to be performed by a user of a particular mobile device. The generation of the prediction may be dependent upon an evaluation of the one or more models and one or more wireless commerce transactions completed with the particular mobile device at one or more locations.