Targeted Mobile Advertising via Usage Statistics Analysis
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Solution Overview
Problem
Current mobile advertising methods lack precision in tracking user content consumption and often rely on inaccurate assumptions, leading to ineffective advertising strategies, with SMS spamming being a common but inefficient practice.
Innovation Solution
A system that includes mobile telecommunications devices with processors to collect and analyze usage statistics, allowing for targeted advertisements to be presented based on user behavior and preferences, using a server system to infer user relatedness and recommend relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If SMS spamming is used for mobile advertising, then advertisers can reach users broadly, but the advertising effectiveness is low because users delete messages without viewing them
Solution Approach 1:
The system changes the delivery mechanism parameter from bulk SMS to targeted SMS based on usage statistics. By analyzing content consumption patterns (music, videos, news categories) and user demographics, the system transforms generic advertising into personalized advertisements that match user preferences, thereby improving effectiveness while maintaining reach
Solution Approach 2:
The system implements feedback by tracking and analyzing user content consumption behavior. Usage statistics are collected and processed to generate targeted advertisements that reflect actual user interests. This feedback loop ensures advertisements are relevant to recipients, reducing the likelihood of deletion and improving overall advertising effectiveness
2Productivity
If bulk SMS sending capacity is purchased for advertising, then advertisers can send messages to many users, but message delivery accuracy is poor due to inaccurate assumptions about user behavior
Solution Approach 1:
The system replaces the mechanical bulk SMS sending approach with an intelligent system that uses usage statistics and behavioral analysis. Instead of randomly sending messages to large groups, the system substitutes a data-driven targeting mechanism that identifies specific users likely to be interested in each advertisement based on their content consumption patterns
Solution Approach 2:
The system introduces usage statistics analysis as an intermediary between advertisers and users. This intermediary processes information about user content consumption habits and translates it into targeted advertising lists, thereby improving the precision of message delivery without sacrificing productivity
3Reliability
If targeted advertisements are presented based on usage statistics, then advertising relevance to users is improved, but system complexity increases due to data collection and analysis requirements
Solution Approach 1:
The system employs self-service mechanisms where mobile devices automatically report their own usage statistics to the server. The server then automatically processes this data and generates targeted advertisement lists without requiring manual intervention. This automation reduces operational complexity while maintaining high advertising relevance
Data Source
AI summary
Embodiments of a mobile device and server system are described. The mobile devices communicate with the server system and present targeted content, such as advertisements to the mobile device users. The content is targeted based on usage statistics stored on the server system which were previously collected from the mobile device. The server receives the usage statistics collected from the mobile device, makes inferences about preferences of users by tracking application and/or content usage behaviors of the users, generates recommendations for advertisements targeted toward the users of the mobile devices based on usage statistics; and transmits the recommendations to one or more of the mobile devices for presentation to the user(s).