Mobile Device Travel Pattern Analysis for Targeted Advertising
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Solution Overview
Problem
Current advertising methods are ineffective in targeting mobile device users based on their predicted future destinations, as they rely on static locations and lack personalized, location-dependent strategies.
Innovation Solution
A system that analyzes mobile device users' current and past traffic patterns to predict their next destinations, allowing for the delivery of targeted advertising content, such as coupons, by tracking and recording their movements and providing location-dependent ads based on predicted paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional static location advertising is used, then advertising placement is simple, but advertising effectiveness is low and cannot target users based on predicted destinations
Solution Approach 1:
The system performs preliminary actions by analyzing mobile device traffic patterns and predicting future destinations before the user actually arrives at the location. Advertising content is prepared and delivered in advance based on predicted destinations, allowing the advertising system to anticipate user behavior rather than reacting to current location only
Solution Approach 2:
The advertising system transitions from static location-based advertising to dynamic advertising that adapts to predicted user destinations. The system continuously updates advertising content based on real-time traffic pattern analysis and predicted user movements, making the advertising approach flexible and responsive to changing user behavior patterns
2Measurement precision
If mobile device user traffic patterns are analyzed to predict destinations, then advertising targeting accuracy improves, but data processing complexity and system requirements increase
Solution Approach 1:
The system introduces an intermediary data processing layer that analyzes traffic patterns and predicts destinations before delivering advertising content. This intermediary layer processes raw location data into predicted destination information, separating the complexity of data analysis from the advertising delivery function and allowing for more accurate targeting without overwhelming system requirements
3Productivity
If location-dependent advertising content is delivered based on predicted destinations, then user engagement increases, but requirements for real-time tracking and analysis capabilities increase
Solution Approach 1:
The system enables self-service by automatically analyzing traffic patterns and delivering targeted advertising content without requiring manual intervention. The automated process continuously monitors mobile device movements, predicts destinations, and serves relevant advertising content, increasing user engagement while managing automation requirements through efficient algorithmic processing
Data Source
AI summary
Mobile device users may be tracked either via mobile-signal triangulation or via Global Positioning Satellite information. A mobile device user's recent movements may be analyzed to determine trails or traffic patterns for device user among various locations. Mobile device trail information, either for an individual user or aggregated for multiple users, may be analyzed to determine a next destination for the user. Electronic advertising content, such as advertisements, coupons and/or other communications, associated with the next destination may be sent to the mobile device. Additionally, the identity of the mobile device use may be known and the advertisements or coupons may be tailored according to demographic information regarding the mobile device user. In addition, destinations may be recommended to mobile device users based on the recent locations the users have visited.