Statistical Household Association via Location Clustering
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Solution Overview
Problem
Advertisers face challenges in delivering targeted mobile advertisements due to the lack of access to demographic and purchase history data, as mobile devices cannot be effectively associated with specific households, leading to inefficient ad targeting and reduced revenue in the mobile advertising space.
Innovation Solution
A method and system using machine learning technologies and data analytics to statistically associate a mobile device with a household by modeling location signals as random time-series of latitude-longitude pairs, calculating scores for geographic clusters, and assigning household IDs, allowing for targeted ad delivery to high-lift segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile devices are used for advertising, then advertising reach and mobility are improved, but the ability to target specific demographic segments deteriorates due to lack of household association
Solution Approach 1:
The patent introduces location data and statistical algorithms as intermediaries to bridge mobile devices and household demographic information. Instead of directly accessing household data, the system uses geographic location patterns as a mediator to infer household associations, enabling demographic targeting without direct access to sensitive personal information.
Solution Approach 2:
The system changes the parameter of device identification from direct household association to probabilistic location-based association. By transforming the identification approach from deterministic (direct household link) to probabilistic (location cluster scoring), the system enables demographic targeting while preserving user privacy and working within mobile advertising constraints.
2Measurement precision
If location data is collected to associate devices with households, then targeting precision is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the problem into manageable components: collecting location data, clustering locations into geographic regions, scoring clusters for household likelihood, and associating devices with households. This segmentation allows the complex task of device-household association to be broken down into discrete, processable steps that can be implemented systematically.
Solution Approach 2:
The system collects more location data than strictly necessary (excessive action) to improve association accuracy. By gathering extensive location samples over time and scoring multiple clusters, the system achieves higher targeting precision through data redundancy, accepting the trade-off of increased data processing requirements.
3Productivity
If statistical methods are used to associate devices with households, then ad campaign effectiveness is improved, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing location data into clusters and pre-calculating cluster scores before actual ad delivery. This preliminary organization of data into scored clusters enables faster real-time device association during ad campaigns, reducing processing time when ads need to be delivered while maintaining statistical rigor.
Data Source
AI summary
Embodiments of the invention relate to methods and systems for associating a mobile device to a household. In various embodiments, a plurality of latitude-longitude pairs is received for a mobile device during a time period. The latitude-longitude pairs are organized into a plurality of clusters corresponding to geographic regions visited by the mobile device during the time period. For each cluster, a score is calculated that represents a likelihood that a user of the mobile device resides in a household within the cluster. The cluster with the highest score is identified as being the location of the user's household. The computation is preferably conducted recursively over time periods. The mobile device is then associated with the user's household.