Ad Exposure Lift Estimation for Frequency and Timing Decisions
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Solution Overview
Problem
Current methods for measuring the effectiveness of online advertising campaigns are inadequate, as they often result in advertisers either underexposing or overexposing their audiences due to limited understanding of content exposure levels, leading to inefficient resource allocation and poor return on investment.
Innovation Solution
A method and system that estimate the causal effect of different content exposure levels by analyzing impression and conversion data to determine optimal exposure frequencies and times, using a hardware processor to categorize users into test and control groups, generate temporal distributions, and adjust content presentation based on lift analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advertisers increase content exposure frequency and duration, then campaign effectiveness and conversions improve, but advertising cost and resource waste increase
Solution Approach 1:
The patent changes the parameters of content exposure by determining optimal exposure frequency and duration based on causal effect analysis. The system calculates specific exposure parameters (frequency and time) that maximize conversions while minimizing waste, moving from arbitrary exposure decisions to data-driven parameter optimization.
Solution Approach 2:
The patent replaces traditional mechanical attribution methods with a causal inference system using temporal distributions and propensity score matching. This substitution enables more accurate determination of causal effects between content exposure and conversions, allowing for optimized exposure decisions that balance effectiveness and cost.
2Measurement precision
If advertisers use traditional conversion rate measurement, then campaign performance can be tracked, but causal effect of different exposure levels cannot be determined leading to suboptimal exposure decisions
Solution Approach 1:
The patent introduces temporal distributions as an intermediary between content exposure and conversion measurements. By analyzing the time distribution between exposure and conversion events, the system can distinguish causal relationships from mere correlations, recovering the lost causal effect information that traditional attribution methods cannot capture.
Solution Approach 2:
The patent segments users into different propensity groups using propensity score matching based on their characteristics and behavior patterns. This segmentation allows for more precise measurement of causal effects by comparing converted and non-converted users within similar propensity groups, eliminating confounding variables that traditional aggregate measurements cannot address.
3Productivity
If advertisers serve more advertisement impressions to users, then potential conversions increase, but most users receive only one or two impressions limiting campaign effectiveness
Solution Approach 1:
The patent makes the content exposure strategy dynamic by determining optimal frequency and duration based on real-time causal effect analysis. Instead of static exposure decisions, the system continuously adjusts exposure parameters based on measured causal relationships, enabling adaptive optimization that responds to actual user behavior and conversion patterns.
Data Source
AI summary
Methods, systems, and media for estimating the causal effect of different content exposure levels are provided.