Predictive Targeting Model for Ad Conversion
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Solution Overview
Problem
Current advertising technologies lack the ability to effectively target users based on their behavioral patterns and geographic affinities beyond proximity to specific locations, leading to inefficient ad delivery.
Innovation Solution
A predictive targeting model that utilizes behavioral data, user profile data, and geographic features to identify locations with high conversion rates and user affinities, allowing for targeted advertising regardless of the user's proximity to specific businesses or demographics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advertisers use current location information and nearby business information for targeting, then advertising delivery is simple and direct, but the targeting capability is limited and cannot effectively reach users with high affinity for brands
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user behavioral data, device information, and location history in advance to build predictive models. These models pre-calculate user affinities and conversion probabilities before advertising campaigns launch, enabling sophisticated targeting without real-time computational complexity.
Solution Approach 2:
The patent introduces intermediary components including predictive modeling systems, data processing layers, and affinity calculation engines that mediate between raw data and advertising delivery. These intermediaries transform complex multi-source data into simplified targeting signals that can be efficiently used for ad selection and delivery.
2Measurement precision
If advertisers target users based on proximity to specific locations, then the targeting method is easy to implement, but it cannot effectively target users based on behavioral patterns and brand affinity
Solution Approach 1:
The patent replaces mechanical/geographic proximity-based targeting with data-driven predictive modeling. Instead of measuring physical distance to locations, the system uses statistical models and machine learning algorithms to calculate abstract concepts like user affinity, conversion probability, and behavioral patterns, achieving higher precision through computational methods.
Solution Approach 2:
The system changes the fundamental parameters used for targeting from simple geographic coordinates to complex behavioral metrics including device information, location history, app usage patterns, and inferred user preferences. This parameter transformation enables precise measurement of user affinity through multiple data dimensions.
3Productivity
If comprehensive behavioral data and multiple data sources are collected for predictive modeling, then targeting precision is improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the comprehensive data processing system into distinct modular components: data collection modules for different data sources, preprocessing modules for cleaning and standardizing data, modeling modules for affinity calculation, and delivery modules for ad selection. This segmentation reduces overall system complexity by making each component independent and manageable.
Solution Approach 2:
The predictive modeling system is designed as a universal platform that can process multiple types of data (location, device, behavioral) and serve multiple advertising objectives simultaneously. The same core infrastructure handles various data sources and model types, reducing redundancy and simplifying the overall system architecture despite handling comprehensive data.
Data Source
AI summary
A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.