Hyper-Local On-Demand Targeted Advertising System
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Solution Overview
Problem
Current advertising techniques are inadequate for hyper-local domains, such as shopping malls and airports, as they lack the ability to conduct on-demand targeted marketing, track non-standard statistics, and efficiently connect advertisers with consumers in geographic proximity, leading to inefficient revenue distribution and ineffective ad campaigns.
Innovation Solution
A method and system that utilize multi-function devices (MFDs) and a broker to provide on-demand targeted communications within hyper-local domains by receiving and comparing inputs from consumers and advertisers, generating relevant keywords, and transmitting targeted messages and advertisements based on consumer trajectories, enabling semi-structured messaging and tracking of click-through statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If online advertising campaigns are used in hyper-local domains, then the reach to potential customers is expanded, but the cost per effective viewer increases significantly because most viewers are not in geographic proximity to the hyper-local domain
Solution Approach 1:
The system implements location-based targeting by determining the geographic location of the computing device and comparing it with the hyper-local domain boundaries. Advertisements are selectively transmitted only when the device is within the designated hyper-local domain, ensuring that advertising resources are not wasted on users who cannot physically visit the location.
Solution Approach 2:
The broker acts as an intermediary between advertisers and consumers by receiving advertisement data from advertisers, processing location information from consumers via computing devices, and selectively transmitting relevant advertisements. This intermediary layer enables efficient matching of advertisements with geographically appropriate consumers without requiring direct connections between all parties.
2Loss of energy
If pre-printed static advertising material is distributed in hyper-local domains, then the cost of postal distribution is reduced, but the ability to provide personalized and on-demand marketing messages is lost
Solution Approach 1:
The system transitions from static pre-printed advertisements to dynamic on-demand advertisement generation. Advertisements are created and transmitted in real-time based on consumer preferences, search queries, and location data. The broker can generate personalized advertisement content dynamically when a consumer expresses interest in a product category or searches for specific items within the hyper-local domain.
Solution Approach 2:
The system performs preliminary actions by collecting consumer preference data, search history, and location information before generating advertisements. The broker analyzes this pre-collected data to pre-filter and prepare relevant advertisements, so that when a consumer queries the system, personalized advertisements can be immediately transmitted without requiring real-time processing of raw data.
3Ease of operation
If broad online advertising campaigns are transmitted to all internet users, then the simplicity of distribution is maintained, but the precision of targeting specific hyper-local consumers is lost
Solution Approach 1:
The system segments the broad internet user base into specific hyper-local consumer groups based on geographic location. By determining the location of each computing device and comparing it with defined hyper-local domain boundaries, the broker divides the universal audience into location-specific segments, allowing advertisements to be transmitted only to relevant local consumers while maintaining automated distribution processes.
4Ease of operation
If traditional advertising revenue models based on view counts are used, then the simplicity of revenue calculation is maintained, but the ability to accurately measure effectiveness in hyper-local domains is lost
Solution Approach 1:
The system implements feedback mechanisms by tracking consumer interactions with advertisements, including whether consumers visit the advertised hyper-local domain, make purchases, or engage with promotional offers. The broker collects this outcome data and uses it to calculate actual advertising effectiveness and return on investment, providing advertisers with precise measurement of campaign performance rather than just view counts.
Data Source
AI summary
A method of providing targeted communications within a hyper-local domain from a first user to a second user, the method including receiving a communication from a first device, the first communication related to the hyper-local domain, receiving an input from a second device, comparing the input to the communication, and transmitting the communication to the second device, a third device, or combinations thereof, based on the comparing of the input to the communication.